Towards a Reduced <i>In Silico</i> Model Predicting Biochemical Recurrence After Radiotherapy in Prostate Cancer

Objective: Purposes of this work were i) to develop an in silico model tumor response radiotherapy, ii) perform exhaustive sensitivity analysis order iii) propose a simplified version and iv) predict biochemical recurrence with both the comprehensive reduced model. Methods: A multiscale computational radiotherapy was developed. It integrated following radiobiological mechanisms: oxygenation, including hypoxic death; division cells; VEGF diffusion driving angiogenesis; healthy cells oxygen-dependent irradiation, considering, cycle arrest mitotic catastrophe. thorough using Morris screening method performed on 21 prostate tissues. Tumor control probability (TCP) curves 15 versions compared. Logistic regression after 76 localized cancer patients output models. Results: No significant difference found between TCP which only considered their irradiation. Biochemical predictions models improved those made from pre-treatment imaging parameters (AUC = 0.81 ± 0.02 0.82 vs. 0.75 0.03, respectively). Conclusion: able obtained. Significance: This may be used future optimize personalized fractionation schedules.