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Computerized histomorphometric features of glandular architecture predict risk of biochemical recurrence following radical prostatectomy: A multisite study.

5060 Background: Following a radical prostatectomy (RP) for prostate cancer, a patient may experience biochemical recurrence (BCR), defined as two consecutive prostate specific antigen (PSA) readings > 0.2 ng/mL. BCR is correlated with metastasis and disease specific survival. Extant molecular based companion diagnostic tests for predicting risk of BCR and disease progression tend to be expensive and tissue destructive. We sought to evaluate whether computer extracted features of glandular architecture from routine digitized H&E slides could predict post-RP BCR risk. Methods: RP specimens from 683 patients (184 with BCR, 499 without) with post-surgical PSA follow-up information were gathered from six sites. Median non-BCR follow-up was 3.2 years. A representative tumor area was annotated on the diagnostic H&E slide of each patient. 324 (131 BCR) patients from two sites formed the training set. The other 359 (53 BCR) patients formed the validation set. Glands were segmented by a deep learning model. 216 features describing gland arrangement, shape, and disorder were then extracted. An elastic net Cox proportional hazards model was constructed from the training set using the top 10 stable features identified via feature selection. Risk score thresholds were chosen on the training set to stratify patients into low-, medium-, or high-risk. Validation set results were evaluated by the log-rank test and hazard ratio. For the 172 (37 BCR) patients for whom Gleason grade and preoperative PSA values were available, risk classifications were compared using Cox proportional hazards regression. Results: Nine of the top features were gland shape features and one was a gland arrangement feature. The hazard ratio between the low- and high-risk groups on the validation set was 3.04 (p < 0.05). The histomorphometric classifier was predictive of BCR (p < 0.05, hazard ratio = 1.63) independent of Gleason grade group and preoperative PSA in multivariate testing. Conclusions: Computer extracted features of gland morphology can stratify post-RP patients by BCR risk. Our computerized histomorphometric model could serve as a prognostic tool in the post-RP setting.[Table: see text]