Literature DB >> 15285925

Progression rates of colorectal cancer by Dukes' stage in a high-risk group: analysis of selective colorectal cancer screening.

Jau-Min Wong1, Ming-Fang Yen, Mei-Shu Lai, Stephen W Duffy, Robert A Smith, Tony Hsiu-Hsi Chen.   

Abstract

PURPOSE: The progression rates of colorectal cancer by Dukes' stage in a high-risk group were estimated and applied to evaluate the efficacy of different screening regimens. PATIENTS AND METHODS: Of 6303 high-risk subjects invited to a colorectal cancer screening project with colonoscopy, 39 screen-detected cases and 16 postscreening cases were diagnosed with information available on Dukes' stage. A five-state Markov process was applied to estimate parameters pertaining to the disease natural history of colorectal cancer by Dukes' stage.
RESULTS: The estimates of the mean sojourn time in years were 3.10 for preclinical Dukes' A and B and 1.92 for preclinical Dukes' stages C and D. The predicted reductions of Dukes' stages C and D achieved by annual, biennial, 3-yearly, and 6-yearly screening regimens against the control group were 60%, 49%, 40%, and 25%, respectively. These, in turn, yield the corresponding predicted mortality reductions of 39%, 33%, 28%, and 18%.
CONCLUSIONS: These findings suggest that to achieve a 30% mortality reduction, as observed in annual fecal occult blood testing, a prudent interscreening interval with colonoscopy for this high-risk group should not be longer than 3 years.

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Year:  2004        PMID: 15285925     DOI: 10.1097/00130404-200405000-00005

Source DB:  PubMed          Journal:  Cancer J        ISSN: 1528-9117            Impact factor:   3.360


  7 in total

1.  Probability model for estimating colorectal polyp progression rates.

Authors:  Chaitra Gopalappa; Selen Aydogan-Cremaschi; Tapas K Das; Seza Orcun
Journal:  Health Care Manag Sci       Date:  2010-10-05

Review 2.  Calibration methods used in cancer simulation models and suggested reporting guidelines.

Authors:  Natasha K Stout; Amy B Knudsen; Chung Yin Kong; Pamela M McMahon; G Scott Gazelle
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

3.  Validation of a modelling approach for estimating the likely effectiveness of cancer screening using cancer data on prevalence screening and incidence.

Authors:  Nora Pashayan; Paul Pharoah; László Tabár; David E Neal; Richard M Martin; Jenny Donovan; Freddie Hamdy; Stephen W Duffy
Journal:  Cancer Epidemiol       Date:  2010-08-16       Impact factor: 2.984

4.  Lagtimes in diagnosis and treatment of colorectal cancer: determinants and association with cancer stage and survival.

Authors:  J Wattacheril; J R Kramer; P Richardson; B D Havemann; L K Green; A Le; H B El-Serag
Journal:  Aliment Pharmacol Ther       Date:  2008-08-08       Impact factor: 8.171

5.  Diagnostic value evaluation of trefoil factors family 3 for the early detection of colorectal cancer.

Authors:  Hui Xie; Jian-Hai Guo; Wei-Min An; Sheng-Tao Tian; Hai-Peng Yu; Xue-Ling Yang; Hua-Ming Wang; Zhi Guo
Journal:  World J Gastroenterol       Date:  2017-03-28       Impact factor: 5.742

6.  Predicting the progress of colon cancer by DNA methylation markers of the p16 gene in feces - Evidence from an animal model.

Authors:  Wen-Chih Wu; Chih-Hsiung Hsu; Jen-Chun Kuan; Jih-Fu Hsieh; Chien-An Sun; Tsan Yang; Chang-Chieh Wu; Yu-Ching Chou
Journal:  Genet Mol Biol       Date:  2013-08-30       Impact factor: 1.771

7.  Association of Ambient Fine Particulate Matter (PM2.5) with Elevated Fecal Hemoglobin Concentration and Colorectal Carcinogenesis: A Population-Based Retrospective Cohort Study.

Authors:  Mei-Sheng Ku; Chen-Yu Liu; Chen-Yang Hsu; Han-Mo Chiu; Hsiu-Hsi Chen; Chang-Chuan Chan
Journal:  Cancer Control       Date:  2021 Jan-Dec       Impact factor: 3.302

  7 in total

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