Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence.

ABSTRACT:

Background

To investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy.

Methods

Clinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction.

Results

RadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models

SUBMITTER: Hou Y 

PROVIDER: S-EPMC10646026 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature