Machine Learning in Prediction of Second Primary Cancer and Recurrence in Colorectal Cancer

doi:10.7150/ijms.37134

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 期刊論文 > Item 310902500/24466

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/24466

Title:	Machine Learning in Prediction of Second Primary Cancer and Recurrence in Colorectal Cancer
Authors:	Ting, WC;Lu, YCAG;Ho, WC;Cheewakriangkrai, CL;Chang, ORR;Lin, CL
Keywords:	colorectal cancer;second primary malignancy;machine learning
Date:	2020
Issue Date:	2022-08-09T08:02:48Z (UTC)
Publisher:	IVYSPRING INT PUBL
ISSN:	1449-1907
Abstract:	Background: Colorectal cancer (CRC) is the third commonly diagnosed cancer worldwide. Recurrence of CRC (Re) and onset of a second primary malignancy (SPM) are important indicators in treating CRC, but it is often difficult to predict the onset of a SPM. Therefore, we used mechanical learning to identify risk factors that affect Re and SPM. Patient and Methods: CRC patients with cancer registry database at three medical centers were identified. All patients were classified based on Re or no recurrence (NRe) as well as SPM or no SPM (NSPM). Two classifiers, namely A Library for Support Vector Machines (LIBSVM) and Reduced Error Pruning Tree (REPTree), were applied to analyze the relationship between clinical features and Re and/or SPM category by constructing optimized models. Results: When Re and SPM were evaluated separately, the accuracy of LIBSVM was 0.878 and that of REPTree was 0.622. When Re and SPM were evaluated in combination, the precision of models for SPM+Re, NSPM+Re, SPM+NRe, and NSPM+NRe was 0.878, 0.662, 0.774, and 0.778, respectively. Conclusions: Machine learning can be used to rank factors affecting tumor Re and SPM. In clinical practice, routine checkups are necessary to ensure early detection of new tumors. The success of prediction and early detection may be enhanced in the future by applying big data analysis methods such as machine learning.
URI:	http://dx.doi.org/10.7150/ijms.37134 https://www.webofscience.com/wos/woscc/full-record/WOS:000514851300001 https://ir.csmu.edu.tw:8080/handle/310902500/24466
Relation:	INTERNATIONAL JOURNAL OF MEDICAL SCIENCES ,2020 ,v17 ,issue 3 ,p280-291
Appears in Collections:	[中山醫學大學研究成果] 期刊論文

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