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.
This is a preview of subscription content
Subscribe to Journal
Get full journal access for 1 year
only $24.75 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017;67:7–30.
Center MM, Jemal A, Lortet-Tieulent J, Ward E, Ferlay J, Brawley O, et al. International variation in prostate cancer incidence and mortality rates. Eur Urol. 2012;61:1079–92.
Wilt TJ, Brawer MK, Jones KM, Barry MJ, Aronson WJ, Fox S, et al. Radical prostatectomy versus observation for localized prostate cancer. N Engl J Med. 2012;367:203–13.
Bill-Axelson A, Holmberg L, Garmo H, Rider JR, Taari K, Busch C, et al. Radical prostatectomy or watchful waiting in early prostate cancer. N Engl J Med. 2014;370:932–42.
Cooperberg MR, Davicioni E, Crisan A, Jenkins RB, Ghadessi M, Karnes RJ. Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. Eur Urol. 2015;67:326–33.
Boorjian SA, Thompson RH, Tollefson MK, Rangel LJ, Bergstralh EJ, Blute ML, et al. Long-term risk of clinical progression after biochemical recurrence following radical prostatectomy: the impact of time from surgery to recurrence. Eur Urol. 2011;59:893.
Hoffman KE, Nguyen PL, Chen MH, Chen RC, Choueiri TK, Hu JC, et al. Recommendations for post-prostatectomy radiation therapy in the United States before and after the presentation of randomized trials. J Urol. 2011;185:116.
Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA, et al. The 2014 International Society of Urological Pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma: definition of grading patterns and proposal for a new grading system. Am J Surg Pathol. 2016;40:244–52.
Cooperberg MR, Hilton JF, Carroll PR. The Capra-S Score. A straightforward tool for improved prediction of outcomes after radical prostatectomy. Cancer. 2011;117:5039–46.
Donovan MJ, Cordon-Cardo C. Implementation of a precision pathology program focused on oncology-based prognostic and predictive outcomes. Mol Diagn Ther. 2017;21:115–23.
Cordon-Cardo C, Kotsianti A, Verbel DA, Teverovskiy M, Capodieci P, Hamann S, et al. Improved prediction of prostate cancer recurrence through systems pathology. J Clin Invest. 2007;117:1876–83.
Donovan MJ, Hamann S, Clayton M. A systems pathology approach for the prediction of prostate cancer progression after radical prostatectomy. J Clin Oncol. 2008;26:3923–9.
Donovan MJ, Khan FM, Fernandez G, Mesa-tejada R, Sapir M, Zubek VB, et al. Personalized prediction of tumor response and cancer progression on prostate needle biopsy. J Urol. 2009;182:123–30.
Scott R, Khan FM, Zeineh J, Donovan M, Fernandez G. Gland ring morphometry for prostate cancer prognosis in multispectral immunofluorescence images. Medical Image Computing and Computer Assisted Intervention, MICCAI 2014. Lect Notes Comput Sci. 2014;8673:585–92.
Khan FM, Scott R, Donovan MJ, Fernandez G. Predicting and replacing the pathological Gleason grade with automated gland ring morphometric features from immunofluorescent prostate cancer images. J Med Imag. 2017;4:021103.
Donovan MJ, Khan FM, Fernandez G, Mesa-Tejada R, Sapir M, Zubek VB, et al. Personalized prediction of tumor response and cancer progression on prostate needle biopsy. J Urol. 2009;182:125–32.
Cuzick J, Stone S, Fisher G, Yang ZH, North BV, Berney DM, et al. Validation of an RNA cell cycle progression score for predicting death from prostate cancer in a conservatively managed needle biopsy cohort. Br J Cancer. 2015;11:382–9.
Ross AE, Johnson MH, Yousefi K, Davicioni E, Netto GJ, Marchionni L, et al. Tissue-based genomics augments post-prostatectomy risk stratification in a natural history cohort of intermediate and high risk men. Eur Urol. 2016;69:157–65.
Spratt DE, Yousefi K, Deheshi S, Ross AE, Den RB, Schaeffer EM, et al. Individual patient-level met-analysis of the performance of the Decipher Genomic Classifier in high-risk men after prostatectomy to predict development of metastatic disease. J Clin Oncol. 2017;35:1991–8.
Donovan MJ, Khan FM, Powell D, Bayer-Zubek V, Cordon-Cardo C, Costa J, et al. Postoperative systems models more accurately predict risk of significant disease progression than standard risk groups and a 10-year postoperative nomogram: potential impact on the receipt of adjuvant therapy after surgery. BJUI. 2012;109:40–5.
Punnen S, Freedland SJ, Presti JC Jr, Aronson WJ, Terris MK, Kane CJ, et al. Multi-institutional validation of the CAPRA-S score to predict disease recurrence and mortality after radical prostatectomy. Eur Urol. 2015;65:1171–7.
Donovan MJ, Cordon-Cardo C. Genomic analysis in active surveillance: predicting high-risk disease using tissue biomarkers. Curr Opin Urol. 2014;24:303–10.
Pascale M, Aversa C, Barbazza R, Maronqiu B, Siracusano S, Stoffel F, et al. The proliferation marker Ki67, but not neuroendocrine expression, is an independent factor in the prediction of prognosis of primary prostate cancer patients. Radiol Oncol. 2016;50:313–20.
Green WJ, Ball G, Hulman G, Johnson C, Van Schalwyk G, Ratan HL, et al. Ki67 and DLX2 predict increased risk of metastasis formation in prostate cancer-a targeted molecular approach. Br J Cancer. 2016;115:236–42.
Tretiakova MS, Wei W, Boyer HD, Newcomb LF, Hawley S, Auman H, et al. Prognostic value of Ki67 in localized prostate carcinoma: a multi-institutional study of > 1000 prostatectomies. Prostate Cancer Prostatic Dis. 2016;19:264–70.
Leach D, Need E, Toivanen R, Trotta AP, Palethorpe HM, Tamblyn DJ, et al. Stromal androgen receptor regulates the composition of the microenvironment to influence prostate cancer outcome. Oncotarget. 2015;6:16135–50.
Urbanucci A, Barfield S, Kytola V, Itkonen HM, Coleman IM, Vodak D, et al. Androgen receptor deregulation drives bromodomain-mediated chromatin alterations in prostate cancer. Cell Rep. 2017;19:2045–59.
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.
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.
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.
About this article
Cite this article
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). https://doi.org/10.1038/s41391-018-0067-4
BMC Cancer (2021)
Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer
World Journal of Urology (2020)
Laboratory Investigation (2019)