Validation of AI-based postoperative nomograms for biochemical recurrence in prostate adenocarcinoma.

e17104 Background: After radical prostatectomy (RP), a steep increase in PSA level is an early sign of the disease progression in prostate cancer, which is known as biochemical recurrence (BCR). The risk of BCR can be evaluated based on a combination of clinicopathological factors, and the patient’s Gleason score plays a significant role. However, this scoring system may have lower consistency due to interobserver reproducibility in classifying Gleason Patterns (GP) as well as in quantifying the amount of each GP. We developed AI-based nomograms, with the aim of investigating their prognostic efficacy. Methods: In this study, digitized whole-slide images (WSIs) of H&E-stained prostatectomy specimens and clinical follow-up information were obtained from two sources: Pusan National University Hospital (PNUH, n = 967, event = 342) from 2010 to 2021 with the median follow-up being 3.7 years, and The Cancer Genome Atlas (TCGA, n = 352, event = 79) from 2000 to 2013 with the median follow-up 2.6 years. We used the DeepDx Prostate - RP, an AI-based prostate cancer Gleason grading model, to compute a pixel-wise probability map of each GP in WSIs. Then, a weighted sum of the probabilities and GPs was calculated for each pixel. The proposed slide-level score (AI score) was then determined by averaging them across all pixels in WSI, resulting in a value ranging from 3 to 5. Also, patients were divided into five groups using AI score thresholds (3.1, 3.5, 3.9, and 4.1), and five binary variables (AI score group 1-5) were generated, where a value of 1 indicates a patient’s categorization into a group. To evaluate predictability we created new nomograms incorporating AI scores based on existing nomograms: MSKCC and CAPRA-S. Unlike the original nomograms, the proposed nomograms do not include the Grade Group (GG) made by pathologists. Then, we fitted a Cox regression model on one of two datasets using the original and newly formed nomograms, and validated on the other dataset reporting the concordance index (c-index). Confidence intervals for the c-index were generated via the non-parametric bootstrap resampling with 9,999 samples. Results: The table shows the prognostic performance of sets of clinicopathological factors and demonstrates that the proposed AI score improved the predictive power. The nomograms with AI score group 1-5 outperformed the original nomograms, as shown in (d). Conclusions: In conclusion, we developed the AI-based nomograms which can improve the accuracy of predicting the biochemical recurrence in prostate cancer compared to the existing nomograms. [Table: see text]