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

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

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

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

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

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

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

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

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

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

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