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ROE: Radiotherapy Outcomes Estimator - An Open-Source Tool for Modeling Radiotherapy Outcomes

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A Jackson

A Iyer , A Jackson*, A Apte , M Thor , J Deasy , Memorial Sloan-Kettering Cancer Center, New York, NY


SU-I-GPD-T-251 (Sunday, July 30, 2017) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: To introduce a tool for exploring the impact of dose scaling on Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) in radiotherapy.

Methods: We developed a MATLAB-based tool that can be used with open-source software CERR (Computational Environment for Radiotherapy Research) to predict and analyze dose responses of patient outcomes with varying prescription dose (Rx). The Users define outcomes models and clinical constraints, following a simple pre-defined syntax. Inputs are specified in easily edited JavaScript Object Notation (JSON) treatment protocol files. Each file specifies: tumor and critical structures; associated outcome models; model parameters including clinical risk factors (age, gender, stage etc.); and clinical constraints on outcomes. Currently available predictive models include Lyman-Kutcher-Burman, linear, biexponential, logistic, and the TCP model of Walsh et al. The tool simultaneously displays the TCP/NTCP dose responses as functions on Rx, and indicates the Rx where the clinical limits are violated. Users can modify the clinical and model variables to visualize the simultaneous impact on TCP/NTCP.

Results: We used ROE to generate dose-response plots for currently available models, simultaneously displaying the clinical outcome goals.

Conclusion: We developed an open-source, GPL copyrighted tool for interactive exploration of the dose-response relationship to aid in radiotherapy planning. It provides a suite of curated models and allows for the definition of new models and clinical guidelines.

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