Using nuclear morphometry to predict the need for treatment among men with low grade, low stage prostate cancer enrolled in a program of expectant management with curative intent

PURPOSE We assessed the use of quantitative clinical and pathologic information to predict which patients would eventually require treatment for prostate cancer (CaP) in an expectant management (EM) cohort. EXPERIMENTAL DESIGN We identified 75 men having prostate cancer with favorable initial biopsy...

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Bibliographic Details
Published in:The Prostate 2008-02, Vol.68 (2), p.183-189
Main Authors: Makarov, Danil V., Marlow, Cameron, Epstein, Jonathan I., Miller, M. Craig, Landis, Patricia, Partin, Alan W., Carter, H. Ballentine, Veltri, Robert W.
Format: Article
Language:English
Subjects:
Aged
Biological and medical sciences
Cell Nucleus - pathology
Cohort Studies
Disease Progression
expectant management
Gynecology. Andrology. Obstetrics
Humans
Image Processing, Computer-Assisted - methods
Linear Models
low grade and low stage cancer
Male
Male genital diseases
Medical sciences
Middle Aged
Neoplasm Staging
Nephrology. Urinary tract diseases
Predictive Value of Tests
Prognosis
prostate biopsy
prostate cancer
Prostatic Neoplasms - diagnosis
Prostatic Neoplasms - pathology
Prostatic Neoplasms - therapy
quantitative nuclear grade
ROC Curve
Sensitivity and Specificity
Tumors
Tumors of the urinary system
Urinary tract. Prostate gland
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Summary:PURPOSE We assessed the use of quantitative clinical and pathologic information to predict which patients would eventually require treatment for prostate cancer (CaP) in an expectant management (EM) cohort. EXPERIMENTAL DESIGN We identified 75 men having prostate cancer with favorable initial biopsy characteristics; 30 developed an unfavorable biopsy (Gleason grade > 6, >2 cores with cancer, >50% of a core with cancer, or a palpable nodule) requiring treatment and 45 maintained favorable biopsies throughout a median follow‐up of 2.7years. Demographic, clinical data and quantitative tissue histomorphometry determined by digital image analysis were analyzed. RESULTS Logistic regression (LR) modeling generated a quantitative nuclear grade (QNG) signature based on the enrollment biopsy for differentiation of Favorable and Unfavorable groups using a variable LR selection criteria of Pz 
ISSN:0270-4137
1097-0045
DOI:10.1002/pros.20679