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: 6983640
• 负责人: Jeremy M.G. Taylor
• 金额: $ 19.84万
• 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 2006-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6983640
• 关键词: Internet; biomarker; blood proteins; cancer risk; clinical research; computer human interaction; computer program /software; computer system design /evaluation; early diagnosis; human data; human mortality; human subject; longitudinal human study; male; mathematical model; model design /development; neoplasm /cancer radiation therapy; neoplasm /cancer relapse /recurrence; prostate neoplasms; prostate specific antigen; statistics /biometry

DESCRIPTION (provided by applicant): 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.

描述(申请人提供)：本申请的目的是开发一种对接受外照射治疗的前列腺癌患者进行可行的循证临床监测的工具。方法要求监测血清前列腺特异性抗原(PSA)。联合统计模型适用于回顾在马萨诸塞州总医院和密歇根大学癌症中心(UMCC)治疗的1945名患者的PSA和临床复发数据。根据新患者当前的连续PSA病史，通过从关节模型中积分Over Fits来预测复发时间。使用UMCC患者和其他RTOG患者的其他随访数据验证了这些方法。使用来自541名UMCC患者的数据，为同时接受激素治疗的患者开发了替代预测模型，并在RTOG的数据上进行了验证。构建了一个基于Web的界面，以供临床医生和患者访问。用户输入患者的特征以及从治疗开始的所有可用日期和PSA值。该程序返回估计的复发概率以及不确定性的度量。除了前列腺癌复发的早期检测外，这项工作的长期目标是通过培训临床医生与万维网上的优化程序进行互动，促进对癌症患者的循证监测。

项目成果

Joint latent class models for longitudinal and time-to-event data: a review.

• DOI: 10.1177/0962280212445839
• 发表时间: 2014-03
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Proust-Lima C;Séne M;Taylor JM;Jacqmin-Gadda H
• 通讯作者: Jacqmin-Gadda H

Predicting biochemical failure and overall survival through intratherapy PSA changes during definitive external beam radiotherapy.

通过确定性外照射放疗期间治疗中 PSA 的变化来预测生化失败和总体生存率。

• DOI: 10.1016/j.ijrobp.2008.03.013
• 发表时间: 2008
• 期刊: International journal of radiation oncology, biology, physics
• 作者: Soto,DanielE;Andridge,RebeccaR;Taylor,JeremyMG;McLaughlin,PatrickW;Sandler,HowardM;Pan,CharlieC
• 通讯作者: Pan,CharlieC