title: Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma.
abstract: OBJECTIVE
  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.
authors.affiliation: 1] Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA [2] Department of Clinical Sciences, University of Texas Southwestern, Dallas, Texas, USA [3] Harold C. Simmons Cancer Center, UT Southwestern Medical Center, Dallas, Texas, USA.
authors.forename: Amit G
authors.initials: AG
authors.lastname: Singal
authors.forename: Ashin
authors.initials: A
authors.lastname: Mukherjee
authors.forename: B Joseph
authors.initials: BJ
authors.lastname: Elmunzer
authors.forename: Peter D R
authors.initials: PD
authors.lastname: Higgins
authors.forename: Anna S
authors.initials: AS
authors.lastname: Lok
authors.forename: Ji
authors.initials: J
authors.lastname: Zhu
authors.forename: Jorge A
authors.initials: JA
authors.lastname: Marrero
authors.forename: Akbar K
authors.initials: AK
authors.lastname: Waljee
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006528:Carcinoma, Hepatocellular / Q000473:pathology*; D005260:Female; D019694:Hepatitis B, Chronic / Q000473:pathology*; D019698:Hepatitis C, Chronic / Q000473:pathology*; D006801:Humans; D008103:Liver Cirrhosis / Q000473:pathology; D008113:Liver Neoplasms / Q000473:pathology*; D016031:Liver Transplantation; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D012044:Regression Analysis; D012307:Risk Factors