PUBLICATION DATE

2013-11

TITLE

Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma.

ISSUE

11

VOLUME

108

ISSN (print)

N/A

ISSN (electronic)

N/A

ABSTRACT

OBJECTIVES: Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine-learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine-learning algorithms. METHODS: We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine-learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared with the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis, and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. RESULTS: After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95% confidence interval (CI) 0.56-0.67), whereas the machine-learning algorithm had a c-statistic of 0.64 (95% CI 0.60-0.69) in the validation cohort. The HALT-C model had a c-statistic of 0.60 (95% CI 0.50-0.70) in the validation cohort and was outperformed by the machine-learning algorithm. The machine-learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (P<0.001) and integrated discrimination improvement (P=0.04). CONCLUSIONS: Machine-learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC.

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1 articles:8ba6c1c2e6d879cef0ab8f9bd973a2fb sg:abstract OBJECTIVES: Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine-learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine-learning algorithms. METHODS: We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine-learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared with the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis, and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. RESULTS: After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95% confidence interval (CI) 0.56-0.67), whereas the machine-learning algorithm had a c-statistic of 0.64 (95% CI 0.60-0.69) in the validation cohort. The HALT-C model had a c-statistic of 0.60 (95% CI 0.50-0.70) in the validation cohort and was outperformed by the machine-learning algorithm. The machine-learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (P<0.001) and integrated discrimination improvement (P=0.04). CONCLUSIONS: Machine-learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC.
2 sg:doi 10.1038/ajg.2013.332
3 sg:doiLink http://dx.doi.org/10.1038/ajg.2013.332
4 sg:isFundedPublicationOf grants:06578b6d8c7d753bddf12f88460cc974
5 grants:37105fc5c819d4c546417041d5f2a777
6 grants:49ec6d540ae39cff9c26852593833f41
7 grants:cf47c260100315a8087940168fb689d4
8 sg:issue 11
9 sg:language English
10 sg:license http://scigraph.springernature.com/explorer/license/
11 sg:publicationYear 2013
12 sg:publicationYearMonth 2013-11
13 sg:scigraphId 8ba6c1c2e6d879cef0ab8f9bd973a2fb
14 sg:title Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma.
15 sg:volume 108
16 rdf:type sg:Article
17 rdfs:label Article: Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma.
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