A validated tumor control probability model based on a meta‐analysis of low, intermediate, and high‐risk prostate cancer patients treated by photon, proton, or carbon‐ion radiotherapy. Issue 2 (14th January 2016) - Walsh, Seán; Roelofs, Erik; Kuess, Peter; Lambin, Philippe; Jones, Bleddyn; Georg, Dietmar; Verhaegen, Frank
Abstract : Purpose: A fully heterogeneous population averaged mechanistic tumor control probability (TCP) model is appropriate for the analysis of external beam radiotherapy (EBRT). This has been accomplished for EBRT photon treatment of intermediate‐risk prostate cancer. Extending the TCP model for low and high‐risk patients would be beneficial in terms of overall decision making. Furthermore, different radiation treatment modalities such as protons and carbon‐ions are becoming increasingly available. Consequently, there is a need for a complete TCP model. Methods: A TCP model was fitted and validated to a primary endpoint of 5‐year biological no evidence of disease clinical outcome data obtained from a review of the literature for low, intermediate, and high‐risk prostate cancer patients (5218 patients fitted, 1088 patients validated), treated by photons, protons, or carbon‐ions. The review followed the preferred reporting item for systematic reviews and meta‐analyses statement. Treatment regimens include standard fractionation and hypofractionation treatments. Residual analysis and goodness of fit statistics were applied. Results: The TCP model achieves a good level of fit overall, linear regression results in a p ‐value of <0.000 01 with an adjusted‐weighted‐ R 2 value of 0.77 and a weighted root mean squared error (wRMSE) of 1.2%, to the fitted clinical outcome data. Validation of the model utilizing three independent datasets obtained from the literature resulted in an adjusted‐weighted‐ R 2 value of 0.78 and a wRMSE of less than 1.8%, to the validation clinical outcome data. The weighted mean absolute residual across the entire dataset is found to be 5.4%. Conclusions: This TCP model fitted and validated to clinical outcome data, appears to be an appropriate model for the inclusion of all clinical prostate cancer risk categories, and allows evaluation of current EBRT modalities with regard to tumor control prediction.