Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture and Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Water
Abstract
Purpose: To combine clinical, serum, pathologic and computer derived information into an artificial neural network to develop/validate a model to predict prostate cancer tumor aggressiveness in both a retrospective and prospective cohort of men with clinically localized prostate cancer both prior to and after radical prostatectomy. Scope: Prospective enrollment of 500 men who are scheduled to undergo radical retropubic prostatectomy (year 01). Development of an artificial neural network (year 02). Prospective validation of this model (projected year 03). All models will be tested and developed for biopsy and prostatectomy material. Major Findings: To date, we have completed prospective enrollment of 557 men, collected tissue, serum and clinical/pathological information for 493 and completed computer image data analysis of 402 samples. We currently have begun construction of a model to predict prostate cancer aggressiveness and anticipate completion of this task by December 2000 At this time, prospective enrollment of a validation subset will begin.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2000
- Accession Number
- ADA387999
Entities
People
- Alan W. Partin
Organizations
- Johns Hopkins University