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