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 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 2.7 years. 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 P(z)<0.05. The QNG signature utilized 12 nuclear morphometric descriptors (NMDs) and had an area under the receiver operator characteristic curve (ROC-AUC) of 87% with a sensitivity of 82%, specificity of 70% and accuracy of 75%. A multivariable LR model combining QNG signature with clinical and pathological variables yielded an AUC-ROC of 88% and a sensitivity of 81%, specificity of 78% and accuracy of 79%. A LR model using prostate volume, PSA density, and number of pre-diagnosis biopsies resulted in an AUC-ROC of 68% and a sensitivity of 85%, specificity of 37% and accuracy of 56%.
CONCLUSIONS: QNG using EM prostate biopsies improves the predictive accuracy of LR models based on traditional clinicopathologic variables in determining which patients will ultimately develop an unfavorable biopsy. Our QNG-based model must be rigorously, prospectively validated prior to use in the clinical arena.