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A Journal on Nephrology and Urology

Indexed/Abstracted in: EMBASE, PubMed/MEDLINE, Science Citation Index Expanded (SciSearch), Scopus
Impact Factor 0,536

Frequency: Bi-Monthly

ISSN 0393-2249

Online ISSN 1827-1758


Minerva Urologica e Nefrologica 2010 September;62(3):231-9



Personalized approach to prostate cancer prognosis

Donovan M. J. 1, Costa J. 1,2, Cordon-Cardo C. 1,3

1 Aureon Biosciences, Inc, Yonkers, NY, USA;
2 Department of Pathology and Comprehensive Cancer Center, Yale School of Medicine, New Haven, CT, USA;
3 Departments of Pathology and Urology and the Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA

Personalized medicine in the management of patients with prostate cancer is limited to the integration of patient attributes such as age, genetic risk and comorbidities with specific clinical-pathologic variables including serum prostate specific antigen (PSA), imaging and features from the diagnostic prostate needle biopsy or prostatectomy specimen including tumor differentiation (i.e. Gleason), volume and extent of disease (i.e. tumor length and / or percentage, number of positive cores at diagnosis or pathologic stage post surgery including margin status). Although the development of various clinical statistical instruments such as nomograms have provided a mechanism to interrogate such variables, most urologists rely on basic prognostic features of stage, grade and PSA along with clinical judgment to define and understand individual risk and predict health outcomes. Furthermore, unlike other tumor types such as breast cancer, there are no routine ancillary diagnostic studies performed on the prostate needle biopsy or prostatectomy specimen to support and refine the treatment decision process for the individual patient. In this review we will provide a summary of the current practice of predictive statistical modeling in prostate cancer and explore how technical advances in functional histology have played a role in the development and incorporation of a systems based platform for providing a patient-specific risk profile useful for clinical decision making.

language: English


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