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

Prostate. 2008 Feb 1;68(2):183-9. doi: 10.1002/pros.20679.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Cell Nucleus / pathology*
  • Cohort Studies
  • Disease Progression
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Linear Models
  • Male
  • Middle Aged
  • Neoplasm Staging
  • Predictive Value of Tests
  • Prognosis
  • Prostatic Neoplasms / diagnosis*
  • Prostatic Neoplasms / pathology*
  • Prostatic Neoplasms / therapy
  • ROC Curve
  • Sensitivity and Specificity