Document Detail


Combined Application of Information Theory on Laboratory Results with Classification and Regression Tree Analysis: Analysis of Unnecessary Biopsy for Prostate Cancer.
MedLine Citation:
PMID:  23078854     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
BACKGROUND: The probability of a prostate cancer-positive biopsy result varies with PSA concentration. Thus, we applied information theory on classification and regression tree analysis (CART) for decision making predicting the probability of a biopsy result at various PSA concentrations. METHODS: From 2007 to 2009, prostate biopsies were performed in 664 referred patients in a tertiary hospital. We created 2 CART models based on the information theory: one for moderate uncertainty (PSA concentration: 2.5 - 10ng/ml) and the other for high uncertainty (PSA concentration: 10-25ng/ml). RESULTS: The CART model for moderate uncertainty (n=321) had 3 splits based on PSA density (PSAD), hypoechoic nodules, and age and the other CART for high uncertainty (n=160) had 2 splits based on prostate volume and free PSA. In this validation set, the patients (14.3 % and 14.0 % for moderate and high uncertainty groups, respectively) could avoid unnecessary biopsies without false-negative results. CONCLUSIONS: Using these CART models based on uncertainty information of PSA, the overall reduction in unnecessary prostate biopsies was 14.0 - 14.3 % and CART models were simplified. Using uncertainty of laboratory results from information theoretic approach can provide additional information for decision analysis such as CART.
Authors:
Sang-Hyun Hwang; Tina Pyo; Heung-Bum Oh; Hyun Jun Park; Kwan-Jeh Lee
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-10-15
Journal Detail:
Title:  Clinica chimica acta; international journal of clinical chemistry     Volume:  -     ISSN:  1873-3492     ISO Abbreviation:  Clin. Chim. Acta     Publication Date:  2012 Oct 
Date Detail:
Created Date:  2012-10-19     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  1302422     Medline TA:  Clin Chim Acta     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012. Published by Elsevier B.V.
Affiliation:
Department of Laboratory Medicine, Center for Diagnostic Oncology, Research Institute and Hospital, National Cancer Center, Goyang-si, South Korea.
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