Keywords: Diagnosis/Prediction, Radiomics
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.
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