Weakly supervised deep learning for diagnosis of prostate cancer after radical prostatectomy: a population- based, multicenter study

Abstract Introduction/Objective The pathological diagnosis of prostate cancer after radical prostatectomy (RP) is a challenging and time-consuming process. Artificial intelligence has the potential to enhance this situation significantly. Our study aimed to assess the efficacy of weakly supervised deep learning, specifically focusing on practical diagnostic tasks for RP specimens. Methods/Case Report We obtained slices from clinical centers across four regions in China. The model was developed based on a multiple instance learning framework. We evaluated the model’s predictive accuracy, covering both slice-level and lesion-level diagnoses. Additionally, we investigated lesion localization using attention visualization. Slice-level diagnosis included prostate cancer detection and Gleason grading, while lesion-level diagnosis involved recognizing index lesions and non-index lesions. Results (if a Case Study enter NA) The study involved 13,245 slides from 304 patients. The area under the curve values in four validation sets ranged from 0.921 (95% CI 0.913–0.930) to 0.964 (95% CI 0.959–0.968). Regarding Gleason grading, the model demonstrated quadratic Weighted Kappa values of 0.748 and 0.720 for internal and external validation, respectively. The model predicted 1,683 (35.3%) positive and 3,081 (64.7%) negative slices in all validation sets. The median area of index lesions and non-index lesions was 92.6mm2 (IQR: 54.2–130.0mm2) and 3.5mm2 (IQR: 1.4–10.9mm2) (p<0.001), respectively. The model accurately predicted index lesions in all cases for three validation sets. Furthermore, the model achieved 100% accuracy in predicting slides with the maximum tumor area for index lesions in two validation sets. In visual assessment, 200 randomly selected slices predicted as true positive demonstrated that the model could identify 97.5% (195/200) of the index lesions. Conclusion The weakly supervised deep learning model demonstrated remarkably high diagnostic and localization capabilities in identifying index lesions within RP specimens. This targeted approach would effectively alleviate the diagnostic workload associated with pathology assessments for prostate cancer.