Grant Number:	1R15CA208881-01A1 Interpret this number
Primary Investigator:	Palmer, James
Organization:	Louisiana Tech University
Project Title:	Quantification of Colonic Neoplasia Growth and Impact on Colonoscopy Follow-Up
Fiscal Year:	2017

PROJECT SUMMARY / ABSTRACT The ability to detect and remove adenomas along the length of the colon during colonoscopy essentially short- circuits the development of CRC and screening is associated with lower colorectal cancer (CRC) mortality. But follow-up care is needed to provide further benefit because the CRC preventive effects of polypectomy fade with time as new or over-looked neoplasia develop and grow. Understanding the effects of follow-up care has become important because, with the recent recommendation of an average risk screening colonoscopy, there is a large cohort of patients who are eligible for colonoscopy follow-up care. The effects and risks for screening colonoscopy have been studied, but the risk dynamics of CRC and the appropriate care following an initial colonoscopy are not well understood. Therefore, the long-term objective of this research is to quantify the risk dynamics for colorectal cancer following a colonoscopy and to identify patients who are candidates for either more or less intensive monitoring. To accomplish this objective, colonic neoplasia growth rates will be quantified by fitting the rates in a predictive model of colonic neoplasia development to serial colonoscopy results. In particular, we are interested in quantifying how the colonic neoplasia growth rates vary by sex, colon location, patient age, and patient-specific colonoscopy results. Quantifying these differences gives the patient-specific dynamics of colorectal cancer risk. The colonic growth rates will be used in stochastic simulations of colorectal cancer monitoring protocols to identify the optimal (greatest increase in quality- adjusted life years), cost-effective follow-up care based on a patient’s risk factors.




Evaluating the Cost-Effective Use of Follow-Up Colonoscopy Based on Screening Findings and Age.
Authors: Joseph G.N. , Heidarnejad F. , Sherer E.A. .
Source: Computational and mathematical methods in medicine, 2019; 2019, p. 2476565.
EPub date: 2019-02-19.
PMID: 30915155
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