From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

136
From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy Eric Williams Memorial Sloan-Kettering Cancer Center New York, NY January 17, 2014

Transcript of From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Page 1: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

From Higgs to the Hospital: Normal TissueComplication Probability Modeling in Radiation

Therapy

Eric Williams

Memorial Sloan-Kettering Cancer CenterNew York, NY

January 17, 2014

Page 2: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication Probability ModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 1 / 29

Page 3: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication Probability ModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 1 / 29

Page 4: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication Probability ModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 1 / 29

Page 5: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication ProbabilityModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 1 / 29

Page 6: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication Probability ModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 1 / 29

Page 7: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication Probability ModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 1 / 29

Page 8: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication Probability ModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 1 / 29

Page 9: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication Probability ModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 1 / 29

Page 10: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Introduction

From Higgs:

To Health:

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 2 / 29

Page 11: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication Probability ModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 2 / 29

Page 12: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation in Medicine – Discovery

• Radiation (x-rays) discovered by Wilhelm Roentgen(1895) while Henri Becquerel concurrently discoveredradioactivity (uranium)

• Following, Marie Curie pioneered research inradioactivity with radium and polonium

• Potential to medicine quickly realized (Figure 1)

• Within a month, radiographs were underproduction

• Within 6 months, they were used in battle tolocate bullets in soldiers

Figure 1: The firstx-ray of BerthaRoentgen’s hand.• Dangers of radiation also quick to surface:

“If one leaves a small glass ampulla with several centigramsof radium salt in ones pocket for a few hours, one will feelabsolutely nothing. But in 15 days afterwards redness willappear on the epidermis, and then a sore, which will be verydifficult to heal. A more prolonged action could lead toparalysis and death.”– Pierre Curie, Nobel lecture 1903

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 3 / 29

Page 13: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation in Medicine – Discovery

• Radiation (x-rays) discovered by Wilhelm Roentgen(1895) while Henri Becquerel concurrently discoveredradioactivity (uranium)

• Following, Marie Curie pioneered research inradioactivity with radium and polonium

• Potential to medicine quickly realized (Figure 1)

• Within a month, radiographs were underproduction

• Within 6 months, they were used in battle tolocate bullets in soldiers

Figure 1: The firstx-ray of BerthaRoentgen’s hand.• Dangers of radiation also quick to surface:

“If one leaves a small glass ampulla with several centigramsof radium salt in ones pocket for a few hours, one will feelabsolutely nothing. But in 15 days afterwards redness willappear on the epidermis, and then a sore, which will be verydifficult to heal. A more prolonged action could lead toparalysis and death.”– Pierre Curie, Nobel lecture 1903

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 3 / 29

Page 14: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation in Medicine – Discovery

• Radiation (x-rays) discovered by Wilhelm Roentgen(1895) while Henri Becquerel concurrently discoveredradioactivity (uranium)

• Following, Marie Curie pioneered research inradioactivity with radium and polonium

• Potential to medicine quickly realized (Figure 1)

• Within a month, radiographs were underproduction

• Within 6 months, they were used in battle tolocate bullets in soldiers

Figure 1: The firstx-ray of BerthaRoentgen’s hand.• Dangers of radiation also quick to surface:

“If one leaves a small glass ampulla with several centigramsof radium salt in ones pocket for a few hours, one will feelabsolutely nothing. But in 15 days afterwards redness willappear on the epidermis, and then a sore, which will be verydifficult to heal. A more prolonged action could lead toparalysis and death.”– Pierre Curie, Nobel lecture 1903

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 3 / 29

Page 15: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation in Medicine – Modern Use

• Diagnostic tools:• X-ray images → computed tomography (CT )• Positron Emission Tomography (PET )• Magnetic Resonance Imaging (MRI )

Eleckta Linear Accelerator• Therapeutic tools:• Brachytherapy : radioactive sources place near disease• Nuclear medicine: Radioactive material injected or injested by patient• External beam radiotherapy: intense radiation from external source

is focused on the cancerous tissue

→ Nearly 2/3 of all cancer patients will receive radiation therapy ←during the course of their treatment.1

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 4 / 29

Page 16: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation in Medicine – Modern Use

• Diagnostic tools:• X-ray images → computed tomography (CT )• Positron Emission Tomography (PET )• Magnetic Resonance Imaging (MRI )

Eleckta Linear Accelerator• Therapeutic tools:• Brachytherapy : radioactive sources place near disease• Nuclear medicine: Radioactive material injected or injested by patient• External beam radiotherapy: intense radiation from external source

is focused on the cancerous tissue

→ Nearly 2/3 of all cancer patients will receive radiation therapy ←during the course of their treatment.1

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 4 / 29

Page 17: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation in Medicine – Modern Use

• Diagnostic tools:• X-ray images → computed tomography (CT )• Positron Emission Tomography (PET )• Magnetic Resonance Imaging (MRI )

Eleckta Linear Accelerator• Therapeutic tools:• Brachytherapy : radioactive sources place near disease• Nuclear medicine: Radioactive material injected or injested by patient• External beam radiotherapy: intense radiation from external source

is focused on the cancerous tissue

→ Nearly 2/3 of all cancer patients will receive radiation therapy ←during the course of their treatment.1

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 4 / 29

Page 18: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication ProbabilityModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 4 / 29

Page 19: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Purpose

A key challenge in radiotherapy is maximizing radiation doses to cancercells while minimizing damage to surrounding healthy (normal) tissue

Successful tumor control depends principally on the total dosedelivered, but tolerances of surrounding normal tissues limit this dose.3

Goal: To model Normal Tissue Complication Probability (NTCP), basedon clinical and dosimetric predictors, to reduce future toxicities and allowhigher doses to the target for greater tumor control.

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 5 / 29

Page 20: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Dose-Volume Histograms

To obtain useful predictors, need to simplify complicated 3Ddosimetric and anatomic information from treatment plans:

Lung Treatment Plan

→Dose-Volume Histogram

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 6 / 29

Page 21: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Dose-Volume Histograms

Dose-volume histograms (DVH)

• DVHs summarize dose-volume information for a particularstructure (e.g. tumor, or organ)

• A point on the DVH represents: The volume (V) of the givenstructure that received at least dose (D)

VD : Vol. (V ) receiving ≥ dose (D)

V20Gy= 40%

V50Gy= 15%

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 7 / 29

Page 22: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Dose-Volume Histograms

Dose-volume histograms (DVH)

• DVHs summarize dose-volume information for a particularstructure (e.g. tumor, or organ)

• A point on the DVH represents: The volume (V) of the givenstructure that received at least dose (D)

VD : Vol. (V ) receiving ≥ dose (D)

V20Gy= 40%

V50Gy= 15%

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 7 / 29

Page 23: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Dose-Volume Histograms

Dose-volume histograms (DVH)

• DVHs summarize dose-volume information for a particularstructure (e.g. tumor, or organ)

• A point on the DVH represents: The volume (V) of the givenstructure that received at least dose (D)

VD : Vol. (V ) receiving ≥ dose (D)

V20Gy= 40%

V50Gy= 15%

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 7 / 29

Page 24: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Model Building

• NTCP models use these DVH reduction values (e.g. VD) aspredictive parameters to produce a single measure: probability ofcomplication

→ ??? →

???

Common NTCPindependent variables

• Dose/Volume parameters: e.g. VD or DV

• Min/Max/Mean dose to organ

• Generalized Equivalent Uniform Dose

• Clinical inputs (e.g. age, KPS, smoke)

Common NTCPcomplication probability models

• Logistic Regression

• ROC Analysis

• Cox Proportional Hazards

• Logrank + Kaplan-Meier

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 8 / 29

Page 25: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Model Building

• NTCP models use these DVH reduction values (e.g. VD) aspredictive parameters to produce a single measure: probability ofcomplication

→ VD,i →

VD

Common NTCPindependent variables

• Dose/Volume parameters: e.g. VD or DV

• Min/Max/Mean dose to organ

• Generalized Equivalent Uniform Dose

• Clinical inputs (e.g. age, KPS, smoke)

Common NTCPcomplication probability models

• Logistic Regression

• ROC Analysis

• Cox Proportional Hazards

• Logrank + Kaplan-Meier

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 8 / 29

Page 26: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Model Building

• NTCP models use these DVH reduction values (e.g. VD) aspredictive parameters to produce a single measure: probability ofcomplication

→ MeanDosei →

Dmean

Common NTCPindependent variables

• Dose/Volume parameters: e.g. VD or DV

• Min/Max/Mean dose to organ

• Generalized Equivalent Uniform Dose

• Clinical inputs (e.g. age, KPS, smoke)

Common NTCPcomplication probability models

• Logistic Regression

• ROC Analysis

• Cox Proportional Hazards

• Logrank + Kaplan-Meier

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 8 / 29

Page 27: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Model Building

• NTCP models use these DVH reduction values (e.g. VD) aspredictive parameters to produce a single measure: probability ofcomplication

→ F(Di, Vi, ...)→

F(D,V, ...)

Common NTCPindependent variables

• Dose/Volume parameters: e.g. VD or DV

• Min/Max/Mean dose to organ

• Generalized Equivalent Uniform Dose

• Clinical inputs (e.g. age, KPS, smoke)

Common NTCPcomplication probability models

• Logistic Regression

• ROC Analysis

• Cox Proportional Hazards

• Logrank + Kaplan-Meier

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 8 / 29

Page 28: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Model Building

• NTCP models use these DVH reduction values (e.g. VD) aspredictive parameters to produce a single measure: probability ofcomplication

→ ??? →

???

Common NTCPindependent variables

• Dose/Volume parameters: e.g. VD or DV

• Min/Max/Mean dose to organ

• Generalized Equivalent Uniform Dose

• Clinical inputs (e.g. age, KPS, smoke)

Common NTCPcomplication probability models

• Logistic Regression

• ROC Analysis

• Cox Proportional Hazards

• Logrank + Kaplan-Meier

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 8 / 29

Page 29: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Model Building

• NTCP models use these DVH reduction values (e.g. VD) aspredictive parameters to produce a single measure: probability ofcomplication

→ ??? →

0 10 20 30 40 50 600

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

gEUD [Gy]

Com

plic

ation p

robabili

ty

log10

(a) = 0.6

p−val: 1.33e−04

Common NTCPindependent variables

• Dose/Volume parameters: e.g. VD or DV

• Min/Max/Mean dose to organ

• Generalized Equivalent Uniform Dose

• Clinical inputs (e.g. age, KPS, smoke)

Common NTCPcomplication probability models

• Logistic Regression

• ROC Analysis

• Cox Proportional Hazards

• Logrank + Kaplan-Meier

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 8 / 29

Page 30: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Model Building

• NTCP models use these DVH reduction values (e.g. VD) aspredictive parameters to produce a single measure: probability ofcomplication

→ ??? →

Common NTCPindependent variables

• Dose/Volume parameters: e.g. VD or DV

• Min/Max/Mean dose to organ

• Generalized Equivalent Uniform Dose

• Clinical inputs (e.g. age, KPS, smoke)

Common NTCPcomplication probability models

• Logistic Regression

• ROC Analysis

• Cox Proportional Hazards

• Logrank + Kaplan-Meier

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 8 / 29

Page 31: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Model Building

• NTCP models use these DVH reduction values (e.g. VD) aspredictive parameters to produce a single measure: probability ofcomplication

→ ??? →

Common NTCPindependent variables

• Dose/Volume parameters: e.g. VD or DV

• Min/Max/Mean dose to organ

• Generalized Equivalent Uniform Dose

• Clinical inputs (e.g. age, KPS, smoke)

Common NTCPcomplication probability models

• Logistic Regression

• ROC Analysis

• Cox Proportional Hazards

• Logrank + Kaplan-Meier

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 8 / 29

Page 32: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

NTCP Modeling: Model Building

• NTCP models use these DVH reduction values (e.g. VD) aspredictive parameters to produce a single measure: probability ofcomplication

→ ??? →

Common NTCPindependent variables

• Dose/Volume parameters: e.g. VD or DV

• Min/Max/Mean dose to organ

• Generalized Equivalent Uniform Dose

• Clinical inputs (e.g. age, KPS, smoke)

Common NTCPcomplication probability models

• Logistic Regression

• ROC Analysis

• Cox Proportional Hazards

• Logrank + Kaplan-Meier

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 8 / 29

Page 33: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication Probability ModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 8 / 29

Page 34: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Purpose

Chest-wall pain (CWP) is among the most common adverse effectsof stereotactic body radiation therapy (SBRT) for thoracic tumors.

The purpose of this (and similar) normal tissue toxicity study is both:

Predictive→ Build predictive models of the incidenceof CWP using dose/volume and clinical parameters.

Prescriptive→ Derive clinically implementabledose/volume guidelines (thresholds) to be imposed infuture treatments.

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 9 / 29

Page 35: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Purpose

Chest-wall pain (CWP) is among the most common adverse effectsof stereotactic body radiation therapy (SBRT) for thoracic tumors.

The purpose of this (and similar) normal tissue toxicity study is both:

Predictive→ Build predictive models of the incidenceof CWP using dose/volume and clinical parameters.

Prescriptive→ Derive clinically implementabledose/volume guidelines (thresholds) to be imposed infuture treatments.

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 9 / 29

Page 36: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Purpose

Chest-wall pain (CWP) is among the most common adverse effectsof stereotactic body radiation therapy (SBRT) for thoracic tumors.

The purpose of this (and similar) normal tissue toxicity study is both:

Predictive→ Build predictive models of the incidenceof CWP using dose/volume and clinical parameters.

Prescriptive→ Derive clinically implementabledose/volume guidelines (thresholds) to be imposed infuture treatments.

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 9 / 29

Page 37: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Patient Cohort

Patient and Tumor Characteristics• 316 lung tumors in 295 patients treated between 2006-2012 wereretrospectively analyzed

N Percent (%)

Median age 77 (49− 95)yMedian KPS 70 (50− 100)Tumor

Primary NSCLC 285 90.2Oligometastatic 13 4.1Recurrent 18 5.7

Doses x Num Fx.18− 20 Gy × 3 113 35.812 Gy × 4 114 36.19− 10 Gy × 5 62 19.6Other 27 8.5

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 10 / 29

Page 38: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Chest-wall definition

Definition of chest wall (CW)

Chest wall contoured for each patient:

• 2cm expansion of the lung inrind around ipsilateral lung

• 4 CT slices (0.8 cm) aboveand below the tumor

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 11 / 29

Page 39: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Outcome Definition

Definition of Chest-Wall Piain (CWP)

CWP Grade Description

Grade 1 Mild pain, not interfering with functionGrade 2 Moderate pain interfering with function but not ADLs,

requiring NSAIDs/TylenolGrade 3 Severe pain interfering with ADLs, requiring narcotics,

or needing intervention

CTCAE v4.0 with specifications

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 12 / 29

Page 40: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Outcome Definition

Definition of Chest-Wall Piain (CWP)

CWP Grade Description

Grade 1 Mild pain, not interfering with functionGrade 2 Moderate pain interfering with function but not

ADLs, requiring NSAIDs/TylenolGrade 3 Severe pain interfering with ADLs, requiring

narcotics, or needing intervention

CTCAE v4.0 with specifications

CWP outcome studied ≥ 2 Grade.

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 12 / 29

Page 41: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Modeling

Actuarial analysis necessary due to inherent latency of chest-wall pain

0 1 2 3 4 5 60

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Years

Inic

idence o

f gra

de >

= 2

Chestw

all

Pain

Median onset time: 0.61 yr

• Univariate and multivariate Cox Proportional Hazards (CPH)model used to identify predictive factors of CWP

• Regression analysis for survival data

• ROC analysis and Logrank test with Kaplan-Meier methodused to assess correlation of risk factors to CWP

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 13 / 29

Page 42: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Univariate Results

Variable Coef. Std. Err lnL CPH p-value

V39Gy 0.0207 0.0032 −320.30 1.1× 10−10

V30Gy 0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dose/Fx 0.001 0.0003 −331.90 7.5× 10−4

Num. of Fx −0.47 0.18 −333.85 7.5× 10−3

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

Predictors not significant in univariate CPH: KPS, Sex, Age

Variable beta se lnL p-value

KPS -0.02 0.01 -337.06 0.25Sex -0.18 0.26 -337.45 0.48Age -0.01 0.01 -337.68 0.83

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 14 / 29

Page 43: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Univariate Results

Variable Coef. Std. Err lnL CPH p-value

→ V39Gy 0.0207 0.0032 −320.30 1.1× 10−10

→ V30Gy 0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dose/Fx 0.001 0.0003 −331.90 7.5× 10−4

Num. of Fx −0.47 0.18 −333.85 7.5× 10−3

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

• VD is a common dose-volume metric utilized by planners in theclinic, from literature,5 to limit:

• Radiation pneumonitis in NSCLC treatments, V20Gy < 30%

• Late rectal toxicity in prostate cancer treatments, V50Gy < 50%

• Acute esophagitis in thoracic treatments, V35Gy < 40%

Note: V30Gy < 70cc already implemented as CW constraint

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 14 / 29

Page 44: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Univariate Results

Variable Coef. Std. Err lnL CPH p-value

→ V39Gy 0.0207 0.0032 −320.30 1.1× 10−10

→ V30Gy 0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dose/Fx 0.001 0.0003 −331.90 7.5× 10−4

Num. of Fx −0.47 0.18 −333.85 7.5× 10−3

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

• Since goal of study is produce clinically implementable prescriptivemodels, we must take many practicalities into consideration, e.g.

• Complexity added to treatment planning systems• Ease of implementation (many constraints already in place)• Oncologists understanding/comfort• Study findings in relation to current constraints

→ For these reasons V30 Gy is chosen as variable of interest over V39 Gy

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 14 / 29

Page 45: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Univariate Results

Variable Coef. Std. Err lnL CPH p-value

V39Gy 0.0207 0.0032 −320.30 1.1× 10−10

→ V30Gy 0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dose/Fx 0.001 0.0003 −331.90 7.5× 10−4

Num. of Fx −0.47 0.18 −333.85 7.5× 10−3

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

0 10 20 30 40 50 60−340

−335

−330

−325

−320

(VD

) Dose [Gy]

CP

H log−

likelih

ood

Low 68% CI

Low 95% CI

Max LogL = −320.3 at D

39 Gy

0 10 20 30 40 50 6010

−10

10−8

10−6

10−4

10−2

100

(VD

) Dose [Gy]

CP

H p

−valu

e

Min p−val = 1.1e−10 at V39 Gy

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 14 / 29

Page 46: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Univariate Results

Variable Coef. Std. Err lnL CPH p-value

V39Gy 0.0207 0.0032 −320.30 1.1× 10−10

→ V30Gy 0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dose/Fx 0.001 0.0003 −331.90 7.5× 10−4

Num. of Fx −0.47 0.18 −333.85 7.5× 10−3

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

V30threshold

SensitivityTP

TP+FN

SpecificityTN

TN+FP

30cc 0.891 0.29450cc 0.828 0.52470cc 0.656 0.0726

AUC = 0.73 [0.66− 0.81 (95%CI)]

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 14 / 29

Page 47: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Univariate Results

Variable Coef. Std. Err lnL CPH p-value

V39Gy 0.0207 0.0032 −320.30 1.1× 10−10

→ V30Gy 0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dose/Fx 0.001 0.0003 −331.90 7.5× 10−4

Num. of Fx −0.47 0.18 −333.85 7.5× 10−3

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

• V30Gy splits at 30cc, 50cc,70cc all significant

• Recommend: V30Gy ≤ 50cc

• Greater protection than70cc

• More achievable than30cc

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 14 / 29

Page 48: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Univariate Results

Variable Coef. Std. Err lnL CPH p-value

V39Gy 0.0207 0.0032 −320.30 1.1× 10−10

→ V30Gy 0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dose/Fx 0.001 0.0003 −331.90 7.5× 10−4

Num. of Fx −0.47 0.18 −333.85 7.5× 10−3

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

• V30Gy splits at 30cc, 50cc,70cc all significant

• Recommend: V30Gy ≤ 50cc

• Greater protection than70cc

• More achievable than30cc

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 14 / 29

Page 49: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Univariate Results

Variable Coef. Std. Err lnL CPH p-value

V39Gy 0.0207 0.0032 −320.30 1.1× 10−10

V30Gy 0.0129 0.0022 −322.65 7.8× 10−10

→ Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

→ Dose/Fx 0.001 0.0003 −331.90 7.5× 10−4

→ Num. of Fx −0.47 0.18 −333.85 7.5× 10−3

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

But we’ve forgotten something!

Number Dose per Prescriptionof Fractions Fraction (Gy) Dose (Gy)

3 18− 20 54− 604 12 605 9− 10 45− 50

What is a ‘fraction’ and how does it effect treatment?E. Williams (MSKCC) Higgs → Hospital January 17, 2014 14 / 29

Page 50: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Fractionation Effects

Radiation therapy is a (3 + 1)−D problem!‘Fractionation’ refers to how the radiation is delivered over TIME

(one fraction = one serving of radiation)

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 15 / 29

Page 51: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Fractionation Effects

Radiation therapy is a (3 + 1)−D problem!‘Fractionation’ refers to how the radiation is delivered over TIME

(one fraction = one serving of radiation)

Conventional fractionation (old school):

2− 3 Gy/fraction → overall treatment times of months!

SBRT/Hypo-fractionation (new school):

8− 20 Gy/fraction (!)→ overall treatment times of week(s)High risk of severe toxicities without sophisticated beam delivery

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 15 / 29

Page 52: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Fractionation Effects

Radiation therapy is a (3 + 1)−D problem!‘Fractionation’ refers to how the radiation is delivered over TIME

(one fraction = one serving of radiation)

Conventional fractionation (old school):

2− 3 Gy/fraction → overall treatment times of months!

SBRT/Hypo-fractionation (new school):

8− 20 Gy/fraction (!)→ overall treatment times of week(s)High risk of severe toxicities without sophisticated beam delivery

Why does this matter??→ The biological response of tissues (normal and tumor) depends onthe fractionation regime (how much dose per fraction)!

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 15 / 29

Page 53: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Fractionation Effects

How does this effect this study?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 16 / 29

Page 54: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Fractionation Effects

How does this effect this study?

Cohort has various SBRTfractionation schemes! →

Number Dose per Prescriptionof Fractions Fraction (Gy) Dose (Gy)

3 18− 20 54− 604 12 605 9− 10 45− 50

Problem: If tissues respond differently to different fractionationschemes (see above), how can we infer dose-responses relationship ina mixed cohort?Solution: Linear-Quadratic Model!2 Proposed as solution to thisproblem for conventional radiotherapy in the 80s

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 16 / 29

Page 55: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Fractionation Effects

How does this effect this study?

Cohort has various SBRTfractionation schemes! →

Number Dose per Prescriptionof Fractions Fraction (Gy) Dose (Gy)

3 18− 20 54− 604 12 605 9− 10 45− 50

Problem: If tissues respond differently to different fractionationschemes (see above), how can we infer dose-responses relationship ina mixed cohort?Solution: Linear-Quadratic Model!2 Proposed as solution to thisproblem for conventional radiotherapy in the 80s

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 16 / 29

Page 56: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Fractionation Effects

How does this effect this study?

Cohort has various SBRTfractionation schemes! →

Number Dose per Prescriptionof Fractions Fraction (Gy) Dose (Gy)

3 18− 20 54− 604 12 605 9− 10 45− 50

Problem: If tissues respond differently to different fractionationschemes (see above), how can we infer dose-responses relationship ina mixed cohort?Solution: Linear-Quadratic Model!2 Proposed as solution to thisproblem for conventional radiotherapy in the 80s

Currently unclear whether LQ model extends to SBRT→ a goal of this study!

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 16 / 29

Page 57: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: LQ Model

The LQ Model accounts for the effect of fractionation on cell-killingthrough a single, tissue dependent, parameter α/β (for moredetailed explanation see [Hall 2012])

→ Normalized Total Dose (NTD), replaces ‘physical’ dose, andallows for comparison between different fractionation schemes:

NTDα/β = (nd)×

(αβ + dαβ + 2

)n− number of fractions

d− dose per fraction

Using NTD results in models that are easily implementable in theclinic (important). Therefore it would be of much interest if LQformalism can be applied to predictive models in SBRT cohorts...

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 17 / 29

Page 58: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest Wall Pain: LQ ModelQuestion: Does using LQ model NTD instead of ‘physical’ dose improve ourNTCP models?

Method: Compare VD CPH models (previous results) to models usingVNTDα/β for a range of α/β

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 18 / 29

Page 59: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest Wall Pain: LQ ModelQuestion: Does using LQ model NTD instead of ‘physical’ dose improve ourNTCP models?

Method: Compare VD CPH models (previous results) to models usingVNTDα/β for a range of α/β

0 50 100 150 200

−334

−332

−330

−328

−326

−324

−322

−320

−318

VD

[Gy]

Lo

g−

like

liho

od

, C

ox m

od

el

Max ln(L) at V39

0 2 4 6 8 10 12 14 16 18 20 22 24−321.5

−321

−320.5

−320

−319.5

−319

Log−

likelih

ood for

best V

NT

D C

ox M

odel

α/β [Gy]

Physical DoseBest fit ln(L) = −320.3

Answer:

Yes, using NTD with any α/β value < 17.7 Gy results in a better SBRTCWP VNTD model than physical dose!

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 18 / 29

Page 60: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest Wall Pain: LQ ModelQuestion: Does using LQ model NTD instead of ‘physical’ dose improve ourNTCP models?

Method: Compare VD CPH models (previous results) to models usingVNTDα/β for a range of α/β

0 50 100 150 200

−334

−332

−330

−328

−326

−324

−322

−320

−318

VD

[Gy]

Lo

g−

like

liho

od

, C

ox m

od

el

0 2 4 6 8 10 12 14 16 18 20 22 24−318.8

−318.7

−318.6

−318.5

−318.4

−318.3

−318.2

−318.1

−318

−317.9

−317.8

Physical DoseBest fit ln(L) = −320.3

NTD DoseBest fit ln(L) = −317.87

at α/β = 2.1

α/β [Gy]

Log−

likelih

ood for

best V

NT

D C

PH

M

Low 68% CI

Answer:

Yes, using NTD with any α/β value < 17.7 Gy results in a better SBRTCWP VNTD model than physical dose!

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 18 / 29

Page 61: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest Wall Pain: LQ ModelQuestion: Does using LQ model NTD instead of ‘physical’ dose improve ourNTCP models?

Method: Compare VD CPH models (previous results) to models usingVNTDα/β for a range of α/β

0 50 100 150 200

−334

−332

−330

−328

−326

−324

−322

−320

−318

VD

[Gy]

Lo

g−

like

liho

od

, C

ox m

od

el

0 2 4 6 8 10 12 14 16 18 20 22 24−318.8

−318.7

−318.6

−318.5

−318.4

−318.3

−318.2

−318.1

−318

−317.9

−317.8

Physical DoseBest fit ln(L) = −320.3

NTD DoseBest fit ln(L) = −317.87

at α/β = 2.1

α/β [Gy]

Log−

likelih

ood for

best V

NT

D C

PH

M

Low 68% CI

Answer: Yes, using NTD with any α/β value < 17.7 Gy results in a better SBRTCWP VNTD model than physical dose!

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 18 / 29

Page 62: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest Wall Pain: LQ ModelQuestion: Does using LQ model NTD instead of ‘physical’ dose improve ourNTCP models?

Method: Compare VD CPH models (previous results) to models usingVNTDα/β for a range of α/β

0 50 100 150 200

−334

−332

−330

−328

−326

−324

−322

−320

−318

VD

[Gy]

Lo

g−

like

liho

od

, C

ox m

od

el

0 2 4 6 8 10 12 14 16 18 20 22 24−318.8

−318.7

−318.6

−318.5

−318.4

−318.3

−318.2

−318.1

−318

−317.9

−317.8

Physical DoseBest fit ln(L) = −320.3

NTD DoseBest fit ln(L) = −317.87

at α/β = 2.1

α/β [Gy]

Log−

likelih

ood for

best V

NT

D C

PH

M

Low 68% CI

Answer: Yes, using NTD with any α/β value < 17.7 Gy results in a better SBRTCWP VNTD model than physical dose!

Best fit VNTD model at α/β = 2.1 Gy → V99Gy2.1

(Gyα/β normalized dose units)

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 18 / 29

Page 63: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: CPH Model Results

Variable Coef. Std. Err lnL CPH p-value

→ V99Gy2.1 0.0175 0.0035 −317.87 4.3× 10−12

V30Gyphys0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 19 / 29

Page 64: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: CPH Model Results

Variable Coef. Std. Err lnL CPH p-value

→ V99Gy2.1 0.0175 0.0035 −317.87 4.3× 10−12

V30Gyphys0.0129 0.0022 −322.65 7.8× 10−10

→ Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

Model: V99Gy2.1 + Tx

CPH p-value lnL AIC

V99Gy2.1 1.1× 10−7-317.7 639.4

Tx 0.58

No surprise: LQ-model NTD accounts for different fractionations,prescription dose is correlated with # of fractions, should drop out

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 19 / 29

Page 65: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: CPH Model Results

Variable Coef. Std. Err lnL CPH p-value

→ V99Gy2.1 0.0175 0.0035 −317.87 4.3× 10−12

V30Gyphys0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

→ Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

Model: V99Gy2.1 + cm2cw

CPH p-value lnL AIC

V99Gy2.1 4.3× 10−7-317.4 638.8

cm2cw 0.33

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 19 / 29

Page 66: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: CPH Model Results

Variable Coef. Std. Err lnL CPH p-value

→ V99Gy2.1 0.0175 0.0035 −317.87 4.3× 10−12

V30Gyphys0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

→ BMI 0.04 0.02 −335.32 0.031

Model: V99Gy2.1+BMI

CPH p-value lnL AIC

V99Gy2.1 3.6× 10−10-315.7 635.3

BMI 0.035

Valid bi-variate CPH model!

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 19 / 29

Page 67: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: CPH Model Results

Variable Coef. Std. Err lnL CPH p-value

→ V99Gy2.1 0.0175 0.0035 −317.87 4.3× 10−12

V30Gyphys0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

→ BMI 0.04 0.02 −335.32 0.031

Two significant CPH NTCP models:

V99Gy2.1

CPH p-value lnL AIC

V99Gy2.14.3× 10−12 −317.87 637.7

V99Gy2.1+BMI

CPH p-value lnL AIC

V99Gy2.13.6× 10−10

−315.7 635.3BMI 0.035

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 19 / 29

Page 68: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: CPH Model Results

Variable Coef. Std. Err lnL CPH p-value

→ V99Gy2.1 0.0175 0.0035 −317.87 4.3× 10−12

V30Gyphys0.0129 0.0022 −322.65 7.8× 10−10

Presc. Dose (Tx) 0.0008 0.0002 −329.76 6.8× 10−5

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

→ BMI 0.04 0.02 −335.32 0.031

Two significant CPH NTCP models:

V99Gy2.1

CPH p-value lnL AIC

V99Gy2.14.3× 10−12 −317.87 637.7

V99Gy2.1+BMI

CPH p-value lnL AIC

V99Gy2.13.6× 10−10

−315.7 635.3BMI 0.035

Bivariate model preferred by AICE. Williams (MSKCC) Higgs → Hospital January 17, 2014 19 / 29

Page 69: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: KM + Logrank results

V99Gy2.1

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 p = 2.1e − 06

HR = 4.06

Years

Pro

babili

ty o

f C

W P

ain

V99 < 31.6ccV99 ≥ 31.6cc

V99Gy2.1+BMI

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 p = 3.2e − 06

HR = 3.84

Years

Pro

babili

ty o

f C

W P

ain

βV99Gy2.1× V99Gy2.1 + βBMI × BMI < 1.64

βV99Gy2.1× V99Gy2.1 + βBMI × BMI ≥ 1.64

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 20 / 29

Page 70: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: KM + Logrank results

V99Gy2.1

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 p = 2.1e − 06

HR = 4.06

Years

Pro

babili

ty o

f C

W P

ain

V99 < 31.6ccV99 ≥ 31.6cc

V99Gy2.1+BMI

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 p = 3.2e − 06

HR = 3.84

Years

Pro

babili

ty o

f C

W P

ain

βV99Gy2.1× V99Gy2.1 + βBMI × BMI < 1.64

βV99Gy2.1× V99Gy2.1 + βBMI × BMI ≥ 1.64

How do oncologists/medical physcists/planners implementthese results?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 20 / 29

Page 71: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Clinic Implementation

LQ Model results lends to convenient clinicalinterpretation and implementation:

NTDα/β = Dphys ×(

αβ+

DphysNfx

αβ+2

)Dphys - physical dose used and understood by

physicians/planners

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 21 / 29

Page 72: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Clinic Implementation

LQ Model results lends to convenient clinicalinterpretation and implementation:

NTDα/β = Dphys ×(

αβ+

DphysNfx

αβ+2

)Dphys - physical dose used and understood by

physicians/planners

∴ D2phys +(αβ ·Nfx)×Dphys +(−Nfx ·NTDα/β ·(αβ +2)) = 0

→ can solve for Dphys in terms of fraction number (Nfx)←

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 21 / 29

Page 73: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Clinic Implementation

LQ Model results lends to convenient clinicalinterpretation and implementation:

NTDα/β = Dphys ×(

αβ+

DphysNfx

αβ+2

)Dphys - physical dose used and understood by

physicians/planners

∴ D2phys +(αβ ·Nfx)×Dphys +(−Nfx ·NTDα/β ·(αβ +2)) = 0

→ can solve for Dphys in terms of fraction number (Nfx)←

Why is this helpful in communicating results?CWP V99Gy2.1 as an example →

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 21 / 29

Page 74: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Clinic Implementation

Example: Presenting CPH V99Gy2.1 results to the MD - 2 scenarios:

“To reduce the risk of post-SBRT chest-wall pain...”

‘LQ-model’ speak:

Try to keep CW volume receiving atleast 99 Gy of normalized total dose with

α/β = 2.1 Gy to less than 31.6cc→ V99Gy2.1

< 31.6cc ←

‘Physical’ dose model speak:

Try to keep CW dose-volume limitsgiven in table:

Number of VDFractions Threshold

3 V32Gy < 31.6cc4 V36Gy < 31.6cc5 V40Gy < 31.6cc

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 22 / 29

Page 75: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Clinic Implementation

Example: Presenting CPH V99Gy2.1 results to the MD - 2 scenarios:

“To reduce the risk of post-SBRT chest-wall pain...”

‘LQ-model’ speak:

Try to keep CW volume receiving atleast 99 Gy of normalized total dose with

α/β = 2.1 Gy to less than 31.6cc→ V99Gy2.1

< 31.6cc ←

‘Physical’ dose model speak:

Try to keep CW dose-volume limitsgiven in table:

Number of VDFractions Threshold

3 V32Gy < 31.6cc4 V36Gy < 31.6cc5 V40Gy < 31.6cc

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 22 / 29

Page 76: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Clinic Implementation

Example: Presenting CPH V99Gy2.1 results to the MD - 2 scenarios:

“To reduce the risk of post-SBRT chest-wall pain...”

‘LQ-model’ speak:

Try to keep CW volume receiving atleast 99 Gy of normalized total dose with

α/β = 2.1 Gy to less than 31.6cc→ V99Gy2.1

< 31.6cc ←

‘Physical’ dose model speak:

Try to keep CW dose-volume limitsgiven in table:

Number of VDFractions Threshold

3 V32Gy < 31.6cc4 V36Gy < 31.6cc5 V40Gy < 31.6cc

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 22 / 29

Page 77: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Clinic Implementation

Example: Presenting CPH V99Gy2.1 results to the MD - 2 scenarios:

“To reduce the risk of post-SBRT chest-wall pain...”

‘LQ-model’ speak:

Try to keep CW volume receiving atleast 99 Gy of normalized total dose with

α/β = 2.1 Gy to less than 31.6cc→ V99Gy2.1

< 31.6cc ←

‘Physical’ dose model speak:

Try to keep CW dose-volume limitsgiven in table:

Oncologists, planners and radiationtherapists are more fluent in ‘physical’

dose than ‘LQ-model’ dose!

Number of VDFractions Threshold

3 V32Gy < 31.6cc4 V36Gy < 31.6cc5 V40Gy < 31.6cc

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 22 / 29

Page 78: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

SBRT Induced Chest-Wall Pain: Clincal Results

Model: V99Gy2.1

Nfx = 3

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 p = 7.5e − 03

HR = 2.65

Years

Pro

babili

ty o

f C

W P

ain

V99 < 57.3ccV99 ≥ 57.3cc

Nfx = 4

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 p = 2.8e − 02

HR = 2.91

Years

Pro

babili

ty o

f C

W P

ain

V99 < 28.8ccV99 ≥ 28.8cc

Nfx = 5

0 0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 p = 2.4e − 02

HR = 4.34

Years

Pro

babili

ty o

f C

W P

ain

V99 < 0.716ccV99 ≥ 0.716cc

Model: V99Gy2.1+BMI

Nfx = 3

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 p = 8.4e − 05

HR = 4.36

Years

Pro

ba

bili

ty o

f C

W P

ain

βV99Gy2.1× V99Gy2.1 + βBMI × BMI < 2.1

βV99Gy2.1× V99Gy2.1 + βBMI × BMI ≥ 2.1

Nfx = 4

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 p = 8.0e − 02

HR = 2.27

Years

Pro

ba

bili

ty o

f C

W P

ain

βV99Gy2.1× V99Gy2.1 + βBMI × BMI < 1.91

βV99Gy2.1× V99Gy2.1 + βBMI × BMI ≥ 1.91

Nfx = 5

0 0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 p = 1.3e − 01

HR = 3.22

YearsP

rob

ab

ility

of

CW

Pa

in

βV99Gy2.1× V99Gy2.1 + βBMI × BMI < 0.338

βV99Gy2.1× V99Gy2.1 + βBMI × BMI ≥ 0.338

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 23 / 29

Page 79: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication Probability ModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 23 / 29

Page 80: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation Therapy & Global Health - A DigressionHalf of the 10 million cancer diagnoses/yr (not counting melanomas of the skin)occur in developing countries where the cancer incidence is increasing

dramatically4

Over 25 countries have no radiotherapy services available

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 24 / 29

Page 81: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation Therapy & Global Health - A Digression

Assertion: Normal tissue toxicities should be avoided at all costs,regardless of the technological capabilities of the institute.

Question: How can these results be communicated in a global context?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 25 / 29

Page 82: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation Therapy & Global Health - A Digression

Assertion: Normal tissue toxicities should be avoided at all costs,regardless of the technological capabilities of the institute.

Question: How can these results be communicated in a global context?

‘Solution’: Nomograms

• Graphical calculating devicesince 1884

• No computer/calculatornecessary

• Can be used to display mostmultivariate predictive models

• Hypothetical ‘atlas ofnomogram health outcomes’

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 25 / 29

Page 83: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation Therapy & Global Health - A Digression

Assertion: Normal tissue toxicities should be avoided at all costs,regardless of the technological capabilities of the institute.

Question: How can these results be communicated in a global context?

‘Solution’: Nomograms

• Graphical calculating devicesince 1884

• No computer/calculatornecessary

• Can be used to display mostmultivariate predictive models

• Hypothetical ‘atlas ofnomogram health outcomes’

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 25 / 29

Page 84: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation Therapy & Global Health - A Digression

Assertion: Normal tissue toxicities should be avoided at all costs,regardless of the technological capabilities of the institute.

Question: How can these results be communicated in a global context?

‘Solution’: Nomograms

• Graphical calculating devicesince 1884

• No computer/calculatornecessary

• Can be used to display mostmultivariate predictive models

• Hypothetical ‘atlas ofnomogram health outcomes’

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 25 / 29

Page 85: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation Therapy & Global Health - A Digression

Assertion: Normal tissue toxicities should be avoided at all costs,regardless of the technological capabilities of the institute.

Question: How can these results be communicated in a global context?

‘Solution’: Nomograms

• Graphical calculating devicesince 1884

• No computer/calculatornecessary

• Can be used to display mostmultivariate predictive models

• Hypothetical ‘atlas ofnomogram health outcomes’

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 25 / 29

Page 86: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Radiation Therapy & Global Health - A Digression

Assertion: Normal tissue toxicities should be avoided at all costs,regardless of the technological capabilities of the institute.

Question: How can these results be communicated in a global context?

‘Solution’: Nomograms

• Graphical calculating devicesince 1884

• No computer/calculatornecessary

• Can be used to display mostmultivariate predictive models

• Hypothetical ‘atlas ofnomogram health outcomes’

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 25 / 29

Page 87: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Overview

• IntroductionWhat does the Higgs have to do with it?

• The α, β, γ’s of Radiation in MedicineDiscovery and modern use

• Normal Tissue Complication Probability ModelingNTCP → DVH → NTD: A short history of acronyms

• Radiation induced Chest-Wall PainA retrospective analysis

• Radiation Therapy & Global HealthA digression

• ConclusionsSeriously though, whats the deal with the Higgs?

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 25 / 29

Page 88: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (I/IV): NTCP Modeling - Lessons Learned

Normal Tissue Complication Probability Modeling conclusions:

• Normal tissue toxicities are dose limiting and often lethal

• NTCP models parameterize clinical and dose-volume metrics toreduce toxicity and increase dose to target

• Radiation induced chest-wall pain post-SBRT:

• LQ-model dose superior to physical dose predicting CWP

• Improved VD CW thresholds (implemented at MSKCC),potentially reducing future complications

• VD+BMI model best predicts ≥ 2 Grade CWP

• Nomograms provide quick, practical and intuitivemultivariate probability calculations

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 26 / 29

Page 89: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (I/IV): NTCP Modeling - Lessons Learned

Normal Tissue Complication Probability Modeling conclusions:

• Normal tissue toxicities are dose limiting and often lethal

• NTCP models parameterize clinical and dose-volume metrics toreduce toxicity and increase dose to target

• Radiation induced chest-wall pain post-SBRT:

• LQ-model dose superior to physical dose predicting CWP

• Improved VD CW thresholds (implemented at MSKCC),potentially reducing future complications

• VD+BMI model best predicts ≥ 2 Grade CWP

• Nomograms provide quick, practical and intuitivemultivariate probability calculations

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 26 / 29

Page 90: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (I/IV): NTCP Modeling - Lessons Learned

Normal Tissue Complication Probability Modeling conclusions:

• Normal tissue toxicities are dose limiting and often lethal

• NTCP models parameterize clinical and dose-volume metrics toreduce toxicity and increase dose to target

• Radiation induced chest-wall pain post-SBRT:

• LQ-model dose superior to physical dose predicting CWP

• Improved VD CW thresholds (implemented at MSKCC),potentially reducing future complications

• VD+BMI model best predicts ≥ 2 Grade CWP

• Nomograms provide quick, practical and intuitivemultivariate probability calculations

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 26 / 29

Page 91: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (I/IV): NTCP Modeling - Lessons Learned

Normal Tissue Complication Probability Modeling conclusions:

• Normal tissue toxicities are dose limiting and often lethal

• NTCP models parameterize clinical and dose-volume metrics toreduce toxicity and increase dose to target

• Radiation induced chest-wall pain post-SBRT:

• LQ-model dose superior to physical dose predicting CWP

• Improved VD CW thresholds (implemented at MSKCC),potentially reducing future complications

• VD+BMI model best predicts ≥ 2 Grade CWP

• Nomograms provide quick, practical and intuitivemultivariate probability calculations

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 26 / 29

Page 92: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (I/IV): NTCP Modeling - Lessons Learned

Normal Tissue Complication Probability Modeling conclusions:

• Normal tissue toxicities are dose limiting and often lethal

• NTCP models parameterize clinical and dose-volume metrics toreduce toxicity and increase dose to target

• Radiation induced chest-wall pain post-SBRT:

• LQ-model dose superior to physical dose predicting CWP

• Improved VD CW thresholds (implemented at MSKCC),potentially reducing future complications

• VD+BMI model best predicts ≥ 2 Grade CWP

• Nomograms provide quick, practical and intuitivemultivariate probability calculations

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 26 / 29

Page 93: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (I/IV): NTCP Modeling - Lessons Learned

Normal Tissue Complication Probability Modeling conclusions:

• Normal tissue toxicities are dose limiting and often lethal

• NTCP models parameterize clinical and dose-volume metrics toreduce toxicity and increase dose to target

• Radiation induced chest-wall pain post-SBRT:

• LQ-model dose superior to physical dose predicting CWP

• Improved VD CW thresholds (implemented at MSKCC),potentially reducing future complications

• VD+BMI model best predicts ≥ 2 Grade CWP

• Nomograms provide quick, practical and intuitivemultivariate probability calculations

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 26 / 29

Page 94: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (I/IV): NTCP Modeling - Lessons Learned

Normal Tissue Complication Probability Modeling conclusions:

• Normal tissue toxicities are dose limiting and often lethal

• NTCP models parameterize clinical and dose-volume metrics toreduce toxicity and increase dose to target

• Radiation induced chest-wall pain post-SBRT:

• LQ-model dose superior to physical dose predicting CWP

• Improved VD CW thresholds (implemented at MSKCC),potentially reducing future complications

• VD+BMI model best predicts ≥ 2 Grade CWP

• Nomograms provide quick, practical and intuitivemultivariate probability calculations

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 26 / 29

Page 95: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (I/IV): NTCP Modeling - Lessons Learned

Normal Tissue Complication Probability Modeling conclusions:

• Normal tissue toxicities are dose limiting and often lethal

• NTCP models parameterize clinical and dose-volume metrics toreduce toxicity and increase dose to target

• Radiation induced chest-wall pain post-SBRT:

• LQ-model dose superior to physical dose predicting CWP

• Improved VD CW thresholds (implemented at MSKCC),potentially reducing future complications

• VD+BMI model best predicts ≥ 2 Grade CWP

• Nomograms provide quick, practical and intuitivemultivariate probability calculations

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 26 / 29

Page 96: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (II/IV): Medical Physics - Lessons Learned

Conducted multiple health outcomes studies while at MSKCC:

• Chest-wall pain in thoracic SBRT:• Modeling of predictive parameters• Efficacy of linear-quadratic dose correction in model building

• Modeling radiation pneumonitis:• on generalized equivalent uniform dose in a pooled cohort• due to regional lung sensitivities in NSCLC radiation treatments• after incidental irradiation of the heart

• Incidence of brachial plexopathy after high-dose SBRT

• Dosimetric predictors of esophageal toxicity after SBRT for centrallung tumors

• Modeling pulmonary toxicity in a large cohort of central lung tumorstreated with SBRT

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 27 / 29

Page 97: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (II/IV): Medical Physics - Lessons Learned

Conducted multiple health outcomes studies while at MSKCC:

• Chest-wall pain in thoracic SBRT:• Modeling of predictive parameters• Efficacy of linear-quadratic dose correction in model building

• Modeling radiation pneumonitis:• on generalized equivalent uniform dose in a pooled cohort• due to regional lung sensitivities in NSCLC radiation treatments• after incidental irradiation of the heart

• Incidence of brachial plexopathy after high-dose SBRT

• Dosimetric predictors of esophageal toxicity after SBRT for centrallung tumors

• Modeling pulmonary toxicity in a large cohort of central lung tumorstreated with SBRT

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 27 / 29

Page 98: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (II/IV): Medical Physics - Lessons Learned

Conducted multiple health outcomes studies while at MSKCC:

• Chest-wall pain in thoracic SBRT:• Modeling of predictive parameters• Efficacy of linear-quadratic dose correction in model building

• Modeling radiation pneumonitis:• on generalized equivalent uniform dose in a pooled cohort• due to regional lung sensitivities in NSCLC radiation treatments• after incidental irradiation of the heart

• Incidence of brachial plexopathy after high-dose SBRT

• Dosimetric predictors of esophageal toxicity after SBRT for centrallung tumors

• Modeling pulmonary toxicity in a large cohort of central lung tumorstreated with SBRT

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 27 / 29

Page 99: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (II/IV): Medical Physics - Lessons Learned

Conducted multiple health outcomes studies while at MSKCC:

• Chest-wall pain in thoracic SBRT:• Modeling of predictive parameters• Efficacy of linear-quadratic dose correction in model building

• Modeling radiation pneumonitis:• on generalized equivalent uniform dose in a pooled cohort• due to regional lung sensitivities in NSCLC radiation treatments• after incidental irradiation of the heart

• Incidence of brachial plexopathy after high-dose SBRT

• Dosimetric predictors of esophageal toxicity after SBRT for centrallung tumors

• Modeling pulmonary toxicity in a large cohort of central lung tumorstreated with SBRT

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 27 / 29

Page 100: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (II/IV): Medical Physics - Lessons Learned

Conducted multiple health outcomes studies while at MSKCC:

• Chest-wall pain in thoracic SBRT:• Modeling of predictive parameters• Efficacy of linear-quadratic dose correction in model building

• Modeling radiation pneumonitis:• on generalized equivalent uniform dose in a pooled cohort• due to regional lung sensitivities in NSCLC radiation treatments• after incidental irradiation of the heart

• Incidence of brachial plexopathy after high-dose SBRT

• Dosimetric predictors of esophageal toxicity after SBRT for centrallung tumors

• Modeling pulmonary toxicity in a large cohort of central lung tumorstreated with SBRT

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 27 / 29

Page 101: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (II/IV): Medical Physics - Lessons Learned

Conducted multiple health outcomes studies while at MSKCC:

• Chest-wall pain in thoracic SBRT:• Modeling of predictive parameters• Efficacy of linear-quadratic dose correction in model building

• Modeling radiation pneumonitis:• on generalized equivalent uniform dose in a pooled cohort• due to regional lung sensitivities in NSCLC radiation treatments• after incidental irradiation of the heart

• Incidence of brachial plexopathy after high-dose SBRT

• Dosimetric predictors of esophageal toxicity after SBRT for centrallung tumors

• Modeling pulmonary toxicity in a large cohort of central lung tumorstreated with SBRT

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 27 / 29

Page 102: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (III/IV): Particle Physics - Lessons Learned

‘Big data’ analytics and modeling experience acquired at CERN:

• Conducted a search for exotic theoretical particle - Extra-dimensionalWarped Randall-Sundrum Graviton (spoiler: I didn’t find it)

• Huge data: 3TB of ‘clean’ data• Quantification of discovery (or lack there of)• Tools: Machine learning (e.g. boosted decision trees), Monte-Carlo

simulation, Bayesian/Frequentist models, etc...• Complete parameterization of systematic uncertainties→ crucial for

generating and communicating estimates for the Global Burden ofDisease

• Managing large scale data analysis projects from inception tocompletion

• Extensive experience working in large research organizations withdiverse colleagues

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 28 / 29

Page 103: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (III/IV): Particle Physics - Lessons Learned

‘Big data’ analytics and modeling experience acquired at CERN:

• Conducted a search for exotic theoretical particle - Extra-dimensionalWarped Randall-Sundrum Graviton (spoiler: I didn’t find it)

• Huge data: 3TB of ‘clean’ data• Quantification of discovery (or lack there of)• Tools: Machine learning (e.g. boosted decision trees), Monte-Carlo

simulation, Bayesian/Frequentist models, etc...• Complete parameterization of systematic uncertainties→ crucial for

generating and communicating estimates for the Global Burden ofDisease

• Managing large scale data analysis projects from inception tocompletion

• Extensive experience working in large research organizations withdiverse colleagues

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 28 / 29

Page 104: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (III/IV): Particle Physics - Lessons Learned

‘Big data’ analytics and modeling experience acquired at CERN:

• Conducted a search for exotic theoretical particle - Extra-dimensionalWarped Randall-Sundrum Graviton (spoiler: I didn’t find it)

• Huge data: 3TB of ‘clean’ data• Quantification of discovery (or lack there of)• Tools: Machine learning (e.g. boosted decision trees), Monte-Carlo

simulation, Bayesian/Frequentist models, etc...• Complete parameterization of systematic uncertainties→ crucial for

generating and communicating estimates for the Global Burden ofDisease

• Managing large scale data analysis projects from inception tocompletion

• Extensive experience working in large research organizations withdiverse colleagues

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 28 / 29

Page 105: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (III/IV): Particle Physics - Lessons Learned

‘Big data’ analytics and modeling experience acquired at CERN:

• Conducted a search for exotic theoretical particle - Extra-dimensionalWarped Randall-Sundrum Graviton (spoiler: I didn’t find it)

• Huge data: 3TB of ‘clean’ data• Quantification of discovery (or lack there of)• Tools: Machine learning (e.g. boosted decision trees), Monte-Carlo

simulation, Bayesian/Frequentist models, etc...• Complete parameterization of systematic uncertainties→ crucial for

generating and communicating estimates for the Global Burden ofDisease

• Managing large scale data analysis projects from inception tocompletion

• Extensive experience working in large research organizations withdiverse colleagues

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 28 / 29

Page 106: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (III/IV): Particle Physics - Lessons Learned

‘Big data’ analytics and modeling experience acquired at CERN:

• Conducted a search for exotic theoretical particle - Extra-dimensionalWarped Randall-Sundrum Graviton (spoiler: I didn’t find it)

• Huge data: 3TB of ‘clean’ data• Quantification of discovery (or lack there of)• Tools: Machine learning (e.g. boosted decision trees), Monte-Carlo

simulation, Bayesian/Frequentist models, etc...• Complete parameterization of systematic uncertainties→ crucial for

generating and communicating estimates for the Global Burden ofDisease

• Managing large scale data analysis projects from inception tocompletion

• Extensive experience working in large research organizations withdiverse colleagues

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 28 / 29

Page 107: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (III/IV): Particle Physics - Lessons Learned

‘Big data’ analytics and modeling experience acquired at CERN:

• Conducted a search for exotic theoretical particle - Extra-dimensionalWarped Randall-Sundrum Graviton (spoiler: I didn’t find it)

• Huge data: 3TB of ‘clean’ data• Quantification of discovery (or lack there of)• Tools: Machine learning (e.g. boosted decision trees), Monte-Carlo

simulation, Bayesian/Frequentist models, etc...• Complete parameterization of systematic uncertainties→ crucial for

generating and communicating estimates for the Global Burden ofDisease

• Managing large scale data analysis projects from inception tocompletion

• Extensive experience working in large research organizations withdiverse colleagues

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 28 / 29

Page 108: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (III/IV): Particle Physics - Lessons Learned

‘Big data’ analytics and modeling experience acquired at CERN:

• Conducted a search for exotic theoretical particle - Extra-dimensionalWarped Randall-Sundrum Graviton (spoiler: I didn’t find it)

• Huge data: 3TB of ‘clean’ data• Quantification of discovery (or lack there of)• Tools: Machine learning (e.g. boosted decision trees), Monte-Carlo

simulation, Bayesian/Frequentist models, etc...• Complete parameterization of systematic uncertainties→ crucial for

generating and communicating estimates for the Global Burden ofDisease

• Managing large scale data analysis projects from inception tocompletion

• Extensive experience working in large research organizations withdiverse colleagues

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 28 / 29

Page 109: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (III/IV): Particle Physics - Lessons Learned

‘Big data’ analytics and modeling experience acquired at CERN:

• Conducted a search for exotic theoretical particle - Extra-dimensionalWarped Randall-Sundrum Graviton (spoiler: I didn’t find it)

• Huge data: 3TB of ‘clean’ data• Quantification of discovery (or lack there of)• Tools: Machine learning (e.g. boosted decision trees), Monte-Carlo

simulation, Bayesian/Frequentist models, etc...• Complete parameterization of systematic uncertainties→ crucial for

generating and communicating estimates for the Global Burden ofDisease

• Managing large scale data analysis projects from inception tocompletion

• Extensive experience working in large research organizations withdiverse colleagues

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 28 / 29

Page 110: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (IV/IV)

Finally, I am excited for the opportunity to transfer the skills I’ve acquiredat the CERN and Memorial Sloan-Kettering Cancer Center to address

the greatest challenges in Global Health

Thank you!

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 111: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Conclusions (IV/IV)

Finally, I am excited for the opportunity to transfer the skills I’ve acquiredat the CERN and Memorial Sloan-Kettering Cancer Center to address

the greatest challenges in Global Health

Thank you!

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 112: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Backups

Page 113: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

References I

[1] IMV Medical Information Division. “Physician Characteristics andDistribution in the U.S.” In: 2003 SROA Benchmarking (2010).

[2] Fowler FJ. “The linear-quadratic model and progress inradiotherapy”. In: BR. J. Radiol. 62 (1989), pp. 679–694.

[3] Barnett GC, West CML, Dunning AM, et al. “Normal tissue reactionsto radiotherapy: towards tailoring treatment dose by genotype”. In:Nature Reviews Cancer 9 (2009), pp. 134–142.

[4] IAEA. A Silent Crisis: Cancer Treatment in Developing Countries.2006.

[5] Marks LB, Yorke ED, and Deasy JO. “Use of Normal TissueComplicatoin Probability Models in the Clinic”. In: Int. J. RadiationOncology Biol. Phys. 76 (2010), S10–S19.

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 114: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Karnofsky performance status (KPS)

In medicine (oncology and other fields), performance status is an attemptto quantify cancer patients’ general well-being and activities of daily life.This measure is used to determine whether they can receive chemotherapy,whether dose adjustment is necessary, and as a measure for the requiredintensity of palliative care. It is also used in oncological randomizedcontrolled trials as a measure of quality of life.

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 115: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP: Linear-Quadratic Model

Modeling fractionation: Linear-Quadratic Model

• LQ model describes cell-survival curvesassuming two components of cell killing

1) ∝ dose (single-strand DNA breaks)2) ∝ dose2 (double-strand DNA breaks)

• Cell survival then modeled:

S = e−αD−βD2

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 116: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP: Linear-Quadratic Model

Modeling fractionation: Linear-Quadratic Model

• LQ model describes cell-survival curvesassuming two components of cell killing

1) ∝ dose (single-strand DNA breaks)2) ∝ dose2 (double-strand DNA breaks)

• Cell survival then modeled:

S = e−αD−βD2

From this cell-survival model, we can derive a Normalized Total Dose(NTD) useful to compare two different fractionation schemes:

NTD = (nd)× (1 + dα/β )/(1 +

2α/β )

n - number of fractionsd - dose per fraction

→ α/β is a free, tissue dependent, parameter→ Given α/β, NTD replaces dose and allows comparison betweendifferent fractionation schemes

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 117: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP: Linear-Quadratic Model

Modeling fractionation: Linear-Quadratic Model

• LQ model describes cell-survival curvesassuming two components of cell killing

1) ∝ dose (single-strand DNA breaks)2) ∝ dose2 (double-strand DNA breaks)

• Cell survival then modeled:

S = e−αD−βD2

From this cell-survival model, we can derive a Normalized Total Dose(NTD) useful to compare two different fractionation schemes:

NTD = (nd)× (1 + dα/β )/(1 +

2α/β )

n - number of fractionsd - dose per fraction

→ α/β is a free, tissue dependent, parameter→ Given α/β, NTD replaces dose and allows comparison betweendifferent fractionation schemes

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 118: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP: CPH Univariate Backup

Variable beta se lnL CPH p-value

→ D83cc 0.0766 0.0136 −323.30 1.7× 10−8

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

0 200 400 600 800 1000−338

−336

−334

−332

−330

−328

−326

−324

−322

(DV) Volume [cc]

CP

H log−

likelih

ood

Low 68% CI

Low 95% CIMax LogL = −323.3 at D

83 cc

0 200 400 600 800 100010

−8

10−6

10−4

10−2

100

(DV) Volume [cc]

CP

H p

−valu

e

Min p−val = 1.7e−08 at D83 cc

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 119: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP: CPH Univariate Backup

Variable beta se lnL CPH p-value

→ D83cc 0.0766 0.0136 −323.30 1.7× 10−8

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

DV and VD correlated due to DVH constraints (R(V39Gy, D83cc) = 0.86)

(VD

) Dose [Gy]

(DV)

Volu

me [cc]

R(VD

,DV) Correlations

20 40 60

200

400

600

800

1000

0.2

0.4

0.6

0.8

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 120: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP: CPH Univariate Backup

Variable beta se lnL CPH p-value

D83cc 0.0766 0.0136 −323.30 1.7× 10−8

→ Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

BMI 0.04 0.02 −335.32 0.031

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 121: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP: CPH Univariate Backup

Variable beta se lnL CPH p-value

D83cc 0.0766 0.0136 −323.30 1.7× 10−8

Dist. GTV to CW −0.52 0.18 −330.17 1.4× 10−3

→ BMI 0.04 0.02 −335.32 0.031

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 122: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP: ROC Curves V30Gy

AUC: area under curve =probability that random positiveinstance will be assigned correctly

AUC S.E. 95% CI

0.73 0.038 0.66 - 0.81

Standardized AUC (σAUC): 6.02p-value: 8.7×10−10

TP : True Positive: # complications above cutFP : False Positive: # censor above cutTN : True Negative: # censor below cutFN : False Negative: # complications below cut

V30

Threshold TP/FP FN/TN

30cc 57/178 7/7450cc 53/120 11/13270cc 42/69 22/183

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 123: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP: ROC Curves V30Gy

V30

Threshold

SenstivityTP

TP+FN

SpecificityTN

TN+FP

EfficiencyTP+TN

TP+TN+FP+TN

30cc 0.891 0.294 0.41550cc 0.828 0.524 0.58570cc 0.656 0.726 0.712

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 124: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP: Average DVHs (2cm and 3cm defs)

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 125: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Universal Survival Curve

Page 126: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Survival Curves

• The linear quadratic (LQ) model approximates clonogenic survivaldata with truncated power series expansion of natural log of survivingproportion S, → lnS = −α · d− β · d2

• LQ model overestimates the effect of radiation on clonogenicity in thehigh doses commonly used in SBRT

• The multitarget model (MTM) provides another description ofclonogenic survival, assuming n targets need to be hit to disruptclonogenicity

S = e−d/d1 ·{1− (1− e−d/D0)n

}• d1 and D0 are the parameters that determine the initial (first log kill)

and final “slopes” of survival curve• Fits empirical data well, especially in the high-dose range

→ Universal Survival Curve hybridizes LQ model for low-dose range andthe multitarget model asymptote for high-dose range

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 127: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Universal Survival Curve

• Universal Survival Curve (USC) described by:

lnS =

{−(α · d+ β · d2) if d ≤ DT

− 1D0d+

DqD0

if d ≥ DT

}

• 4 independent params (α, β,D0, and Dq) constrainted to 3 whenasymptotic line of MTM is tangential to LQ model parabola at DT

• β as dependent variable allows params. to be obtained by measured curve

β =(1− α ·D0)

2

4D0 ·Dq

• Transition dose, DT , calculated as a function of three remaining USC params

DT =2 ·Dq

1− α ·D0E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 128: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP: Model Comparisons

LQ and USC models: α/β = 3 Gy

Best Model Logistic RegressionModel Parameters Log-likelihood/df AIC

LQ gEUD log10(a) = 1.1 −0.467 295.30LQ DMAX log10(a) = ∞ −0.469 296.53Phys gEUD log10(a) = 1.3 −0.459 290.25Phys DMAX log10(a) = ∞ −0.461 291.51

log10(a) = 0.6USC gEUD α ·D0 = 0.22 −0.453 290.22

Dq = 6.4 GyDT = 16.4 Gy

log10(a) = ∞α ·D0 = 0.13

USC DMAX Dq = 6.6 Gy −0.461 295.34DT = 15.2 Gy

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 129: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

CWP Logistic Regression Model Responses

LQ gEUD, AIC= 295.3

0 50 100 150 200 250 300 350 400 4500

0.1

0.2

0.3

0.4

0.5

0.6

gEUDLQ

log10

(a) =1

Com

plic

ation r

ate

observ

ed

LQ DMAX, AIC= 296.53

0 100 200 300 400 500 6000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Max BED Dose [Gy]

CW

P p

robabili

ty

Phys gEUD, AIC= 290.25

0 10 20 30 40 50 600

0.1

0.2

0.3

0.4

0.5

0.6

gEUDPHYS

log10

(a) =1.3C

om

plic

atio

n r

ate

ob

se

rve

d

Phys DMAX, AIC= 291.51

0 10 20 30 40 50 60 700

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Max PHYS

CW

P p

rob

ab

ility

USC gEUD, AIC= 290.22

0 20 40 60 80 100 120 140 160 180 2000

0.1

0.2

0.3

0.4

0.5

0.6

gEUDUSC

log10

(a) =0.6

Co

mp

lica

tio

n r

ate

ob

se

rve

d

USC DMAX, AIC= 295.34

0 50 100 150 200 250 300 350 4000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

USC Dmax [Gy]

CW

P p

robabili

ty

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 130: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

LQ Model: Chest-wall pain

• gEUD analysis with −1 < log10(a) < 1

• BEDLQ model with single parameter α/β

→ BEDLQ = D(1 + d

α/β

)

−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1−0.495

−0.49

−0.485

−0.48

−0.475

−0.47

−0.465

−0.46

log10

(a)

Log−

likelih

ood / d

f

Max log-likelihood/df = −0.467

−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1294

296

298

300

302

304

306

308

310

312

314

log10

(a)

AIC

Min AIC(n paramLQ = 1) = 295.33

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 131: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

LQ Model: Chest-wall pain

−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 110

−6

10−5

10−4

10−3

10−2

10−1

log10

(a)

p−

valu

e

Min p-value at log10(a) = 1p = 1.1× 10−6

0 50 100 150 200 250 300 350 400 4500

0.1

0.2

0.3

0.4

0.5

0.6

gEUDLQ

log10

(a) =1

Com

plic

ation r

ate

observ

ed

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 132: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Universal Survival Curve: Chest-wall pain• gEUD analysis with −1 < log10(a) < 1

• BEDUSC model with three parameters: α/β, α ·D0 and Dq

BEDUSC =

{D

(1 + d

α/β

)if d ≤ DT

1α·D0

(D − n ·Dq) if d ≥ DT

}

• αβ= 3 Gy, (α ·D0) = 0.01 : 0.01 : 0.5, Dq = 0.2 : 0.2 : 7.6 Gy

Dq [Gy]

α⋅ D

0

1 2 3 4 5 6 7

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Lo

g−

like

liho

od

/df

−0.5

−0.495

−0.49

−0.485

−0.48

−0.475

−0.47

−0.465

−0.46

−0.455

Max llhd/df = −0.453 at Dq = 6.4 Gy, α ·D0 = 0.22

log10(a) = 0.6

Dq [Gy]

α⋅ D

0

1 2 3 4 5 6 7

0.1

0.2

0.3

0.4

0.5

0.6

0.7

AIC

295

300

305

310

315

320

Min AIC = 290.23 at Dq = 6.4 Gy, α ·D0 = 0.22

log10(a) = 0.6, llhd = −0.453

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 133: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Universal Survival Curve: Chest-wall pain

Dq [Gy]

α⋅ D

0

1 2 3 4 5 6 7

0.1

0.2

0.3

0.4

0.5

0.6

0.7

DT

10

20

30

40

50

60

Dq [Gy]

α⋅ D

0

1 2 3 4 5 6 7

0.1

0.2

0.3

0.4

0.5

0.6

0.7

P−

va

lue

0.01

0.02

0.03

0.04

0.05

0.06

Dq [Gy]

α⋅ D

0

1 2 3 4 5 6 7

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Best lo

g10(a

)

−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 134: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Universal Survival Curve: Chest-wall pain

Dq [Gy]

α⋅ D

0

1 2 3 4 5 6 7

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Lo

g−

like

liho

od

/df

−0.5

−0.495

−0.49

−0.485

−0.48

−0.475

−0.47

−0.465

−0.46

−0.455

0 1 2 3 4 5 6 7 8−0.461

−0.46

−0.459

−0.458

−0.457

−0.456

−0.455

−0.454

−0.453

−0.452

−0.451

Dq [Gy]

Ma

x L

og

−lik

elih

oo

d/d

f

0 0.1 0.2 0.3 0.4 0.5−0.46

−0.459

−0.458

−0.457

−0.456

−0.455

−0.454

−0.453

−0.452

−0.451

α⋅ D0 [Gy]

Max L

og−

likelih

ood/d

f

0 5 10 15 20 25 30−0.468

−0.466

−0.464

−0.462

−0.46

−0.458

−0.456

−0.454

−0.452

−0.45

DT [Gy]

Max L

og−

likelih

ood/d

f

68% CI

95% CI

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 135: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Universal Survival Curve: Chest-wall pain

Dq [Gy]

α⋅ D

0

1 2 3 4 5 6 7

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Fra

ctio

n F

ull

CW

LQ

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Dq [Gy]

α⋅ D

0

1 2 3 4 5 6 7

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Fra

ctio

n L

Q B

ins

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

• “Fraction Full CW LQ” - Fraction ofpatients with all dose bins ≤ DT (calc asBEDLQ)

• “Fraction LQ Bins” - Fraction of all dosebins with ≤ DT (calc as BEDLQ)

0 5 10 15 20 25 30 350

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

DT [Gy]

Fra

ction L

Q

Fraction Full CW LQ

Fraction Bins LQ

• Steps in Fraction Full CW LQ, patientfractionation schemes?

• At best fit:

• Frac LQ bins = 0.947• Frac Full LQ = 0.703

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29

Page 136: From Higgs to the Hospital: Normal Tissue Complication Probability Modeling in Radiation Therapy

Universal Survival Curve: CWP

LQ model

0 50 100 150 200 250 300 350 400 4500

0.1

0.2

0.3

0.4

0.5

0.6

gEUDLQ

log10

(a) =1

Com

plic

ation r

ate

observ

ed

Max llhd/df = −0.467p = 1.1× 10−6, log10(a) = 1

AIC = 295.33

USC model

0 20 40 60 80 100 120 140 160 180 2000

0.1

0.2

0.3

0.4

0.5

0.6

gEUDUSC

log10

(a) =0.6

Com

plic

ation r

ate

observ

ed

Max llhd/df = −0.453p = 1.8× 10−7, log10(a) = 0.6

AIC = 290.2Dq = 6.4 Gy, α ·D0 = 0.22

DT = 16.4 Gy

E. Williams (MSKCC) Higgs → Hospital January 17, 2014 29 / 29