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Classifying the precancers: A metadata approach

DOI: 10.1186/1472-6947-3-8

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Terms in the UMLS (Unified Medical Language System) related to precancers were extracted. Extracted terms were reviewed and additional terms added. Each precancer was assigned one of six general classes. The entire classification was assembled as an XML (eXtensible Mark-up Language) file. A Perl script converted the XML file into a browser-viewable HTML (HyperText Mark-up Language) file.The classification contained 4700 precancer terms, 568 distinct precancer concepts and six precancer classes: 1) Acquired microscopic precancers; 2) acquired large lesions with microscopic atypia; 3) Precursor lesions occurring with inherited hyperplastic syndromes that progress to cancer; 4) Acquired diffuse hyperplasias and diffuse metaplasias; 5) Currently unclassified entities; and 6) Superclass and modifiers.This work represents the first attempt to create a comprehensive listing of the precancers, the first attempt to classify precancers by their biological properties and the first attempt to create a pathologic classification of precancers using standard metadata (XML). The classification is placed in the public domain, and comment is invited by the authors, who are prepared to curate and modify the classification.Premalignant lesions are arguably the most important disease entities of modern man. In theory, the identification and elimination of cancer precursors would lead to the near-eradication of cancer [1]. The importance of the precancers was recently emphasized by the American Association for Cancer Research Task Force on the Treatment and Prevention of Intraepithelial Neoplasia [2]. In this report, the Task Force recognized IEN [intraepithelial neoplasia] as a near-obligate precursor to invasive cancer and identified IEN as a treatable disease. "Reducing IEN burden, therefore, is an important and suitable goal for medical (noninvasive) intervention to reduce invasive cancer risk and to reduce surgical morbidity. Achieving the prevention and regression of IEN confers an


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