Improved prediction of recurrence after prostate cancer radiotherapy using multimodal data and in silico simulations

Prediction of biochemical recurrence (BCR) after prostate cancer radiotherapy is crucial for devising personalised treatments. BCR has been traditionally predicted using clinical data or in vivo imaging within AI frameworks such as radiomics approaches, but with limited results and reduced interpretability. These analysis are additionally hin-dered by the imbalanced and heterogeneous nature of data. In this paper, we present a novel approach to predict BCR at 5 years, based not only on clinical and image features, but also on a patient specific radiobiolog-ical mechanistic in silico model simulating tumour growth and radiation response. By combining all these data, we aim at i) improving the pre-diction of BCR after prostate cancer radiotherapy (RT), and ii) bringing interpretability to this prediction. A cohort of 254 patients was used. Pre-treatment T2-w MRIs, ADC maps and 7 clinicopathological char-acteristics were available. Patient specific digital twins of tumours were created from MRIs. The prescribed treatment was simulated with the mechanistic model yielding 414 features characterising the response of the tumour to RT. A first univariate feature selection analysis was con-ducted to select the most predictive features. Then, a machine learning algorithm was trained using selected features and compared with a deep learning (DL) approach based on clinicopathological characteristics and MRIs. Our approach achieved an AUC of 0.74 by training a random for-est classifier combining most predictive features. The DL model achieved an AUC of 0.69. This methodology opens the road to interpretability of the response to radiotherapy and tailored treatments for prostate cancer patients.