Generalizability of a biopsy-trained artificial intelligence algorithm to TURP and HoLEP specimens for detection of incidental prostate adenocarcinoma

Abstract

Abstract Background Incidental prostatic adenocarcinoma is frequently identified in transurethral resection of the prostate (TURP) and holmium laser enucleation of the prostate (HoLEP) specimens obtained for the management of benign prostatic hyperplasia. Artificial intelligence (AI)-based algorithms have demonstrated promising performance in prostate biopsy assessment; however, their applicability to TURP and HoLEP materials remains insufficiently studied. We evaluated the standalone performance of an AI algorithm trained on prostate needle biopsies for detecting incidental adenocarcinoma in TURP and HoLEP specimens. Methods A total of 3,241 whole-slide images from 368 consecutive patients (247 HoLEP and 121 TURP cases) received between May 2023 and December 2025 were retrospectively analyzed. All slides were scanned at ×20 magnification using a Hamamatsu NanoZoomer S360 scanner and processed using a commercially available AI algorithm. The algorithm generated heatmap masks highlighting regions suspicious for malignancy. Conventional histopathological diagnosis served as the reference standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic curve (AUC-ROC) were calculated at the case level. The Cochran–Armitage trend test was used to assess the relationship between the number of AI-flagged slides and malignancy rates. Results Thirty-five cases contained adenocarcinoma, of which 33 were correctly identified by the algorithm, yielding an overall sensitivity of 94.3%. Specificity was 76.0%, PPV 29.2%, NPV 99.2%, and accuracy 77.7%. Sensitivity was 92.3% for HoLEP specimens and 100% for TURP specimens. The case-level AUC-ROC was 0.89. Malignancy rates increased significantly with the number of AI-flagged suspicious slides (0.8%, 13.6%, 18.8%, and 44.4% for 0, 1, 2, and ≥ 3 suspicious slides, respectively; p < 0.001). The two false-negative cases were low-volume Grade Group 1 tumors. Conclusions A biopsy-trained AI algorithm demonstrated high sensitivity and excellent negative predictive value for detecting incidental prostatic adenocarcinoma in TURP and HoLEP specimens. These findings support its potential role as a screening and workflow-assistance tool in routine uropathology practice.