Dose-response Explorer: An Open-source-code Matlab-based tool for modeling treatment outcome as a function of predictive factors Gita Suneja Issam El Naqa,

Dose-response Explorer:An Open-source-code Matlab-based tool for modeling treatment outcome as a function of

predictive factors

Dose-response Explorer:An Open-source-code Matlab-based tool for modeling treatment outcome as a function of

predictive factors Gita Suneja

Issam El Naqa, Patricia Lindsay,Andrew Hope, James Alaly, Jeffrey

Bradley,Joseph O. Deasy

Gita Suneja Issam El Naqa, Patricia Lindsay,

Andrew Hope, James Alaly, Jeffrey Bradley,

Joseph O. DeasySupported by NIH grant R01 CA 85181

What is DREX?What is DREX?

An open-source-code Matlab-based tool for:

1) Modeling tumor control probability (TCP) and normal tissue complication probability (NTCP)

2) Evaluating robustness of models

3) Graphing the results for purposes of outcomes analysis for practitioners, training for residents, and hypothesis-testing for further research

An open-source-code Matlab-based tool for:

1) Modeling tumor control probability (TCP) and normal tissue complication probability (NTCP)

2) Evaluating robustness of models

3) Graphing the results for purposes of outcomes analysis for practitioners, training for residents, and hypothesis-testing for further research

• Motivation– Cornerstone of treatment planning is the need to

balance tumor control probability (TCP) with normal tissue complication probability (NTCP)

• Objective– Physicians and scientists need a tool that is

straightforward and flexible in the study of treatment parameters and clinical factors

• Motivation– Cornerstone of treatment planning is the need to

balance tumor control probability (TCP) with normal tissue complication probability (NTCP)

• Objective– Physicians and scientists need a tool that is

straightforward and flexible in the study of treatment parameters and clinical factors

Motivation & ObjectivesMotivation & Objectives

FeaturesFeatures

1. Analytical modeling of normal tissue complication probability (NTCP) and tumor control probability (TCP)

2. Combination of multiple dose-volume variables and clinical variables using multi-term logistic regression modeling

3. Manual selection or automated estimation of model parameters

4. Estimation of uncertainty in model parameters5. Performance assessment of univariate and multivariate

analysis 6. Capacity to graphically visualize NTCP or TCP

prediction vs. selected model variable(s)

1. Analytical modeling of normal tissue complication probability (NTCP) and tumor control probability (TCP)

2. Combination of multiple dose-volume variables and clinical variables using multi-term logistic regression modeling

3. Manual selection or automated estimation of model parameters

4. Estimation of uncertainty in model parameters5. Performance assessment of univariate and multivariate

analysis 6. Capacity to graphically visualize NTCP or TCP

prediction vs. selected model variable(s)

Basic ModulesBasic Modules

Data Input

Radiobiological

model?

TCP NTCP

Model type?

Model type?

Poisson orLinear

quadratic

Analytical Analytical

Lyman-Kutcher-Burman (LKB) or Critical volume

Logistic regression

Univariate/multivariate performance assessment

Graphical representation

Export output

Multi-metric

1

2

4

5

3

Modeling Method I: AnalyticalModeling Method I: Analytical• NTCP

– Lyman-Kutcher-Burman (LKB) Model (Lyman 1985, Kutcher and

Burman 1989)

– Critical Volume Model (Niemierko and Goitein 1993)

• TCP– Poisson Statistics

– Linear-quadratic (LQ) Prediction

• NTCP– Lyman-Kutcher-Burman (LKB) Model (Lyman 1985, Kutcher and

Burman 1989)

– Critical Volume Model (Niemierko and Goitein 1993)

• TCP– Poisson Statistics

– Linear-quadratic (LQ) Prediction

50

50

( )EUD D

NTCPmD

ln( ln ) ln( ln )( )d crNTCP

pot TCP=exp(-Nexp(-(( + *d)*D+ln2*t/T ))

Modeling Method II: MultimetricModeling Method II: Multimetric• Logistic regression – additive sigmoid model

• Two types of data exploration1. Manual2. Automated

- Determining Model Order by Leave-one-out-Cross-Validation (Ref.: “Multi-Variable Modeling of Radiotherapy Outcomes: Determining Optimal Model Size,” Deasy et al., poster SU-FF-T-376 )

- Model parameters estimated by forward selection on multiple bootstrap samples

• Logistic regression – additive sigmoid model

• Two types of data exploration1. Manual2. Automated

- Determining Model Order by Leave-one-out-Cross-Validation (Ref.: “Multi-Variable Modeling of Radiotherapy Outcomes: Determining Optimal Model Size,” Deasy et al., poster SU-FF-T-376 )

- Model parameters estimated by forward selection on multiple bootstrap samples

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( )( ) , 1,...,1

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i

g

i g

eY i n

e

x

xx

Performance AssessmentPerformance Assessment

• Spearman’s Rank Correlation

• Area under the Receiver Operating Characteristic (ROC) curve

• Survival analysis using the Kaplan-Meier estimator

• Spearman’s Rank Correlation

• Area under the Receiver Operating Characteristic (ROC) curve

• Survival analysis using the Kaplan-Meier estimator

Univariate Graphical Representations Univariate Graphical Representations

Graph/Plot Description/Function

Self-correlation

Color-washed Spearman’s cross-correlation image of selected variables and observed outcome

Scatter •User selects abscissa and ordinate variables

•Provides user with visual cues about the discrimination ability of certain factors

Survival curves

Use Kaplan-Meier estimates

Multivariate Graphical RepresentationsMultivariate Graphical Representations

Graph/Plot Description/Function

Histogram Cumulative plot of observed response (bar graph) and model-predicted response (line graph)

Contour Demonstrates the effect of the model variables on shaping the predicted outcome

Octile •Patients are uniformly binned into 8 groups

•Helps visualized goodness of fit of model

ROC Assess prediction power of model

ConclusionsConclusions

• User-friendly software tool to analyze dose response effects of radiation

• Incorporates treatment and clinical factors, as well as biophysical models

• Various graphical representations

• Available in the near future on the web at

radium.wustl.edu/DREX

• User-friendly software tool to analyze dose response effects of radiation

• Incorporates treatment and clinical factors, as well as biophysical models

• Various graphical representations

• Available in the near future on the web at

radium.wustl.edu/DREX