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Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test




Postoperative risk assessment remains an important variable in the effective treatment of prostate cancer. There is an unmet clinical need for a test with the potential to enhance the Gleason grading system with novel features that more accurately reflect a personalized prediction of clinical failure.


A prospectively designed retrospective study utilizing 892 patients, post radical prostatectomy, followed for a median of 8 years. In training, using digital image analysis to combine microscopic pattern analysis/machine learning with biomarkers, we evaluated Precise Post-op model results to predict clinical failure in 446 patients. The derived prognostic score was validated in 446 patients. Eligible subjects required complete clinical-pathologic variables and were excluded if they had received neoadjuvant treatment including androgen deprivation, radiation or chemotherapy prior to surgery. No patients were enrolled with metastatic disease prior to surgery. Evaluate the assay using time to event concordance index (C-index), Kaplan–Meier, and hazards ratio.


In the training cohort (n = 306), the Precise Post-op test predicted significant clinical failure with a C-index of 0.82, [95% CI: 0.76–0.86], HR:6.7, [95% CI: 3.59–12.45], p < 0.00001. Results were confirmed in validation (n = 284) with a C-index 0.77 [95% CI: 0.72–0.81], HR = 5.4, [95% CI: 2.74–10.52], p < 0.00001. By comparison, a clinical feature base model had a C-index of 0.70 with a HR = 3.7. The Post-Op test also re-classified 58% of CAPRA-S intermediate risk patients as low risk for clinical failure.


Precise Post-op tissue-based test discriminates low from intermediate high risk prostate cancer disease progression in the postoperative setting. Guided by machine learning, the test enhances traditional Gleason grading with novel features that accurately reflect the biology of personalized risk assignment.

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We would like to thank members of the Biorepository and Pathology Core and all support personnel in the Department of Pathology at the Icahn School of Medicine at Mt. Sinai. We would also like to acknowledge both Roswell Park Cancer Center and the Henry Ford Hospital for access to their respective prostatectomy tissue cohorts.


The study was funded by the Icahn School of Medicine at Mt. Sinai but Mt. Sinai was not directly involved in the design and conduct of the study, the collection, management, analysis and interpretation of the data; preparation, review, or approval of the manuscript nor the decision to submit the manuscript for publication.

Author contributions

MJD, GF, RS, JZ, FMK, AT, and CCC contributed to study concept and design. MJD, GF, RS, GK, NG, EC, and FMK contributed to study development and methods, and GF, RS, NG, and EC collected the data. All authors analyzed and interpreted the data. FMK provided statistical and model development support. GK, NG, EC, and JZ provided administrative, technical, and material support. GF, JZ, RS, and MJD supervised the study. MJD, GF, and CCC wrote the manuscript. All authors have approved the final version of the manuscript.

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Correspondence to Michael J. Donovan.

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Conflict of interest

MJD, GF, RS, FMK, JZ, and CCC have patents in varying aspects of the methods, technology and modeling platform utilized in the study.

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Donovan, M.J., Fernandez, G., Scott, R. et al. Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test. Prostate Cancer Prostatic Dis 21, 594–603 (2018).

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