Development of Internet-Accessible Prediction Models for Prostate Cancer Diagnosis, Treatment, and Follow-Up

摘要

The objective of the development of Internet-accessible prediction models is to enhance the diagnosis accuracy, treatment efficacy and prognosis for patients with carcinoma of prostate cancer (CaP). On the basis of the established tools (Oracle database, Internet-accessible data collection applications, and program packages for data retrieval and standardization) in the first year of the support, the roles of a set of variables (race/ethnicity, diagnostic age, labs, delayed treatment interval, treatment types, early and late hormonal therapy, etc.) on the outcome of CaP patients were analyzed. The results show that delayed surgery over 3 months post diagnosis would impact the outcome of patients with high risk disease (submitted to J Urol). Early hormonal therapy can delay clinical metastasis only in men with high risk disease (J urol, in press). Race, percentage of cancer positive cores over total biopsy cores, surgical margin status and seminal vesicle invasion are independent prognostic variables for men with intermediate risk disease (submitted to J urol). Race plays an important role in tumor volume and spatial distribution based on 3 dimensional reconstructed models with radical prostatectomy specimens (submitted to J Urol). Post- treatment PSA doubling time < 3 months is a surrogate for prostate cancer specific mortality following surgery or radiation therapy (J Clin Onc 2003). PSA Doubling time calculator and CPDR nomograms were implemented on CPDR website.