PSA BASED EARLY DETECTION OF PROSTATE CANCER RECURRENCE

基于 PSA 的前列腺癌复发早期检测

• 批准号: 7337637
• 负责人: Jeremy M.G. Taylor
• 金额: $ 31.95万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-13 至 2009-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7337637
• 关键词: Adverse effects; Biological Assay; Cancer Center; Cancer Patient; Characteristics; Clinic; Clinical; Clinical Markers; Data; Data Set; Date Available; Disease; Early Diagnosis; Elevation; Exploratory/Developmental Grant; External Beam Radiation Therapy; General Hospitals; Guidelines; Heterogeneity; Hormonal; Individual; Internet; Intervention; Joints; Lead; Malignant Neoplasms; Malignant neoplasm of prostate; Massachusetts; Measurement; Measures; Methods; Michigan; Modeling; Monitor; Patients; Performance; Phase; Predictive Value; Probability; Prostate-Specific Antigen; Radiation Therapy Oncology Group; Radiation therapy; Rate; Recording of previous events; Recurrence; Reproducibility; Risk; Sampling; Scheme; Serum; Staging; Standards of Weights and Measures; Statistical Models; Test Result; Time; Training; Uncertainty; Universities; University Hospitals; Validation; Work; base; cancer recurrence; comparative; follow-up; hormone therapy; programs; time use; tool; tumor; web based interface; web interface

The aim of this application is to develop a tool for feasible evidence-based clinical monitoring of prostate cancer patients treated by external beam radiation. Methods require the monitoring of serum prostate- specific antigen (PSA). Joint statistical models are fit to retrospective PSA and clinical recurrence data from 1945 patients treated at Massachusetts General Hospital and the University of Michigan Cancer Center (UMCC). Predictions of time to recurrence for new patients based on their current serial PSA history are obtained by integrating over fits from the joint model. The methods are validated using additional follow-up data from UMCC patients and from additional RTOG patients. Alternative prediction models are developed for patients treated with concurrent hormonal therapy using data from 541 UMCC patients and validated on data from RTOG. A web-based interface is constructed for access by clinicians and patients. The user enters characteristics of the patient along with all available dates and PSA values from start of treatment. The program returns an estimated probability of recurrence along with a measure of uncertainty. In addition to the early detection of prostate cancer recurrence, the long term objective of this work is to facilitate evidence-based monitoring of cancer patients through training of clinicians to interact with optimized programs on the world wide web.

本应用程序的目的是开发一种可行的基于证据的前列腺临床监测工具