| Sumario: | Simple Summary: Advanced prostate cancer (PCa) is prone to recurrence and metastasis after treatment. Previous studies have demonstrated the potential of deep learning radiomics to predict 24-month progression in advanced PCa using pretreatment MRI ADC map-derived features, outperforming both traditional radiomics and clinical models. In our study, we first conducted a comparison of manual and AI-generated segmentation impacts, demonstrating equivalent prognostic value and resolving prior concerns about annotation dependency. Then, we established a radiomics model as a time-to-event predictor, with stable discrimination (48-month AUC > 0.75) and calibration (Brier score < 0.15), enabling risk-adapted surveillance intervals—a capability absent in earlier models. These innovations collectively advance radiomics toward clinically actionable, observer-agnostic tools for precision oncology. The use of deep learning radiomics to predict whether advanced prostate cancer (PCa) will progress within two years after treatment has been validated, yet there remains a lack of research on estimating time to progression. Patients were enrolled from October 2017 to March 2024. One hundred and eighty-two patients with advanced PCa diagnosed through ultrasound-guided systematic prostate biopsy were enrolled. A deep learning-based radiomics model for predicting progression was firstly developed using pretreatment MR apparent diffusion coefficient (ADC) maps, and the performance of manual (ROIref) versus AI-derived (ROIai) tumor segmentations was compared. Then, survival analysis was performed to compare ROIref-based and ROIai-based radiomics-predicted probabilities in the risk stratification. The area under the receiver operating characteristics curve (AUC) was used to estimate the model efficacy. The model achieved high AUC values for progression prediction in test sets (ROIref: 0.840, ROIai: 0.852). No significant difference was observed between ROIai-based and ROIref-based approaches (ΔAUC = 0.012, p = 0.870) in the test set. Both ROIref-predicted and ROIai-predicted probabilities independently predicted progression in multivariate Cox proportional hazard regression models (p < 0.001) and stratified patients into distinct survival groups (log-rank p < 0.001). Decision curve analysis confirmed equivalent clinical utility across thresholds (0.1–0.6), with net benefit exceeding the "treat all" and "treat none" strategies. In conclusion, deep learning-based radiomics models could effectively predict advanced PCa progression, with AI-derived tumor annotations performing equally to manual expert ones.
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