Predictive Value of Biochemical Recurrence in Advanced Prostate Cancer: Development of Deep Learning-based Radiomics Model

Motivation: Deep learning forpredicting biochemical recurrence (BCR) is feasible but needs further evaluation in advanced prostate cancer (PCa). Goal(s): We aimed to develop radiomics models with automatic segmentation derived from pretreatment ADC maps that may be predictive of BCR in advanced PCa. Approach: In this study, PCa areas were segmented on ADC images by using a pre-trained artificial intelligence (AI) model. Three models were constructed to evaluate BCR prediction level. Results: The deep-radiomics model was superior than the clinical model and the conventional radiomics model in the aspect of prediction accuracy, clinical impact and risk assessment. Impact: With accurate BCR prediction by deep-radiomics model, more appropriate treatment plans may be formulated and intervention treatment can be carried out as soon as possible, resulting in better prognosis for patients with PCa.