Work

Pd11-04 Genomic Expression and Prognostic Significance of Stromal Microenvironment in Primary Prostate Cancer

Year: 2019

Type: article

Abstract: METHODS: We collected diagnostic PNBX from 163 prostate cancer cases (85 M0 and 78 M1) that contained high grade cancer foci.Pathological annotation was performed by two independent pathologists.Following slide digitization at 40X, image tiles (approximately 30 image tiles per case, n [ 6,501) were used to extract 62 handcrafted (HC) and 64 autoencoder (AE) nuclear features, as well as 1,234 tissuebased (TI) features.A Z-score normalization of features was performed.The nuclear and tissue architecture features were combined and used in a Leave One Out cross validation with 3 models: elastic net, gradient boosted decision trees, and a multi-layer perceptron.RoM scores were generated for each tile, and cases were classified into M0, M1, or "uncertain" based on the variance of RoM scores.Clinical progression was predicted using the model.RESULTS: After color normalization, 273,103 total nuclei and 6501 tiles were analyzed and values extracted for HC, AE, and TI features.Of the three models tested, the E-net model performed the best with an AUC of 0.885 from a cross-validation experiment.Of the 163 cases, 80% were assigned to M0 or M1 status, while 20% were placed into the "uncertain" category.Fifteen from 78 M1 cases were misclassified as M0 cases, while the other 63 cases were correctly classified.The classifier predicted progression to metastatic disease with an area under the curve (AUC) of 0.75.A second validation cohort of 59 more cases is underway.CONCLUSIONS: DIA technology and machine learning software enabled extraction of 1,360 features that differentiate high-grade M0 from M1 prostate cancer.Initial results show promise in using this classifier to prognosticate progression based solely on diagnostic PNBX.DIA technology has the potential to serve as a cost-effective clinical tool for confirming M-stage and predicting metastatic progression.When combined with additional clinical variables, the accuracy of the prediction can be further improved.

Source: The Journal of Urology

DOI: 10.1097/01.ju.0000555360.67170.96