Computerized image analysis of nuclear shape as a prognostic factor for prostatic cancer

TLDR

The study introduces a nuclear roundness factor derived from computerized image analysis to predict individual patient prognosis in stage B‑2 prostatic cancer. The authors applied quantitative nuclear image analysis to 17 excised specimens, describing the method’s accuracy and reproducibility. The nuclear shape factor distinguishes high‑metastatic‑potential tumors from less aggressive ones, offering a quantitative tool that can aid pathologists and urologists in prognostication.

Abstract

Abstract Computer assisted image analysis was used to determine quantitative and reproducible nuclear morphology of 17 totally excised pathological specimens from stage B‐2 prostatic cancer in an attempt to correlate histopathological analysis with clinical prognosis for the individual patient. We describe a nuclear roundness factor that correlates with the clinical outcome of the individual patient. This nuclear shape factor appears to identify prostatic tumors with a high metastatic potential from tumors that are less aggressive. Quantitative nuclear image analysis, its accuracy, and reproducibility are described and discussed. This technique may provide the pathologist with a valuable tool for analyzing prostatic cancer cells in a quantitative manner, thereby contributing precise information to the urologist relevant to the prognosis of the individual patient.