An image analysis method for assessment of prognostic risk in prostate cancer: a pilot study

Fully automated computerized image analysis at medium resolution (1 micron per pixel space) was applied in a study of 17 patients with stage D1 prostate cancer. For this pilot study, patients were selected on the basis of very good or very poor outcome. This selection was made in the hope of identifying morphometric features that are useful in prognostic assessment. Nine patients with good outcome were alive after 7 or more years of follow-up and eight patients with poor prognosis were dead of disease in less than 3 years. All patients were treated with 125I seed implantation to the prostate and pelvic lymph node dissection. Hormone therapy was not administered until the time of distant failure. Routine hematoxylin and eosin tissue sections of lymph nodal tissue bearing metastatic neoplasm were used for this analysis. A minimum of eight scenes per case was analysed. Of 50 measured parameters on each cluster, five (gray level distribution, number of cell clusters per scene, bending energy, average cluster area and cluster polarity) were useful to distinguish patients with good outcome from those with a poor outcome. Thirteen of the 17 patients were correctly classified by image analysis (P = 0.044, Fischer's exact test). By comparison, flow cytometry of the identical tissue samples correctly classified 14 of 17 patients (diploid, good outcome; aneuploid, poor outcome; P = 0.009). Only one patient was incorrectly classified by both image analysis and flow cytometry, implying a complementary prognostic role for the two methods. The encouraging result, successful identification of useful morphometric features, justifies a larger study of unselected patients.