Keywords: Urogenital, Prostate, Machine learning
Motivation: There are currently a few studies that utilize artificial intelligence technology to analyze both for precise prediction of prostate cancer prognosis.
Goal(s): To explore the value of a multi-modality fused model integrating clinical feature, radiomics model, MRI DL model and pathomics model in enhancing predictive efficacy of post-RP BCR.
Approach: The CRDH fused model was established by combining clinical model, radiomics model, MRI DL model and pathomics model with COX regression.
Results: In the testing set, the CRDH model achieved a C-index of 0.87, significantly higher than pathological T stage, radiomics model, MRI DL model, pathomics model and clinical score (P<0.05).
Impact: The multi-modality fused model incorporating clinical variable, radiomics model, MRI DL model, pathomics model was better than all single-modality models. Our model could assess the prognosis of patients with PCa after surgery, providing strong support for formulating subsequent treatment plans.
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