Exploring the efficacy of a continuous form of the histologic grade in prostate cancer prognosis prediction.

e17105 Background: Adenocarcinoma of the prostate is the second most common type of cancer among men worldwide. The Gleason grading system remains the gold standard for evaluating the prognosis of prostate cancer by assessing the cancer morphology. However, the current discretized Gleason pattern regime limits the depiction of fine-grained histomorphological changes. We evaluated the efficacy of the algorithm-generated continuous histologic grade value in prostate cancer prognosis prediction. Methods: Whole-slide images of H&E prostatectomy tissue, along with the follow-up data including new tumor event (NTE) and biochemical recurrence (BCR), were obtained for the cases diagnosed as prostate cancer during 2000-2013 from The Cancer Genome Atlas (TCGA) database. Cases with missing data were excluded, resulting in a total of 308 and 397 cases being used for the analysis of NTE and BCR, respectively. Our study utilized a deep learning-based algorithm that performs Gleason scoring as follows. First, it computes per-pixel likelihood values for each of the 4 classes: benign, Gleason 3, 4, and 5. Then, the per-pixel class with the highest likelihood value and the slide-wise ISUP grade group (GG-AI) is successively determined. We tweaked the algorithm to aggregate the likelihood-weighted Gleason grade assigned to each pixel into a continuous form of the histologic grade (c-HG). We compared the prognostic performance of c-HG with that of the original ISUP grade group in TCGA (GG) and that of algorithm-generated GG-AI in predicting the risk of NTE as well as BCR. The Cox regression analysis was conducted, setting the cases from one of three donating institutions (EJ, HC, KK) as the target, fitting the Cox model with the cases from the other institutions, and evaluating the fitted model on the target. Results: The median follow-up duration in months was 32 for NTE and 29 for BCR, respectively. The number of cases with NTE was 51, while the one with BCR was 82. The table presents the c-index values obtained from each institution for GG, GG-AI, and c-HG. Note that the number of cases in each institution is different for NTE and BCR, due to the different number of removed cases with missing data. c-HG showed the best average performance in both NTE and BCR risk predictions. It is also notable that c-HG showed a stable performance for varying institutions. Conclusions: We proposed an algorithm-based method of representing histologic grade as a continuous value, which may give better predictions in disease progression for prostate adenocarcinoma. [Table: see text]