1. 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
Supported by NIH grant R01 CA 85181

2. What is DREX? An open-source-code Matlab-based tool for:
Modeling tumor control probability (TCP) and normal tissue complication probability (NTCP)
Evaluating robustness of models
Graphing the results for purposes of outcomes analysis for practitioners, training for residents, and hypothesis-testing for further research

3. Motivation & Objectives
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
We are concerned with the quality of the dose distribution and the radiobiological effects of treatment of the patient

4. Features
Analytical modeling of normal tissue complication probability (NTCP) and tumor control probability (TCP)
Combination of multiple dose-volume variables and clinical variables using multi-term logistic regression modeling
Manual selection or automated estimation of model parameters
Estimation of uncertainty in model parameters
Performance assessment of univariate and multivariate analysis
Capacity to graphically visualize NTCP or TCP prediction vs. selected model variable(s)
Quantitative analysis of models – rank correlations, area under ROC
Qualitative analysis of models – graphical representations

5. Basic Modules Data Input 1 2 3 Logistic regression 4
Radiobiological
model? 2
TCP
NTCP
Model type? 3
Model type?
Analytical
Multi-metric
Analytical
Poisson or
Linear quadratic
Logistic regression
Lyman-Kutcher-Burman (LKB) or Critical volume 4
Data input (dose-volume and clinical data) – dBase; reserved fields are dBase.dvh, .output, .TimeAxis, .PatientStatus
Analytic models – based on biophysical understanding of what happens to tumor cell undergoing radiation
Multimetric models – phenomenological and depend on clinical data
Logistic regression – with manual or automated parameter estimation
Univariate/multivariate performance assessment – using correlations, survival and ROC curves
Graphical representation – scatter, contour, histogram, and octile plots
Univariate/multivariate performance assessment
Graphical representation 5
Export output

6. Modeling 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

7. Modeling Method II: Multimetric
Logistic regression – additive sigmoid model
Two types of data exploration
Manual
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

8. Performance Assessment
Spearman’s Rank Correlation
Area under the Receiver Operating Characteristic (ROC) curve
Survival analysis using the Kaplan-Meier estimator
Spearmans – measures the association between different model variables and endpoint outcome, and the correlation between the predicted and observed outcomes; important in treatment plan ranking
ROC – graphs of the true positive fraction (sensitivity) vs. the false positive fraction (1- specificity) for a continuum of threshold values: Az=1 is ideal, while Az<0.5 is a random guess
Kaplan-Meyer estimator – measures the different in survival between several cutpoints using log-rank correlations8

9. 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
Scatter plot – provides user with visual cues about the discrimination ability of certain factors

10. Multivariate 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

15. Conclusions
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