Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis

Grace Lai Hung Wong, Vicki Wing-Ki Hui, Qingxiong Tan, Jingwen Xu, Hye Won LEE, Terry Cheuk-Fung Yip, Baoyao Yang, Yee Kit Tse, Chong Yin, Fei Lyu, Jimmy Che-To Lai, Grace Chung-Yan Lui, Henry Lik-Yuen Chan, Pong Chi Yuen*, Vincent Wai-Sun Wong*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background & Aims: Accurate hepatocellular carcinoma (HCC) risk prediction facilitates appropriate surveillance strategy and reduces cancer mortality. We aimed to derive and validate novel machine learning models to predict HCC in a territory-wide cohort of patients with chronic viral hepatitis (CVH) using data from the Hospital Authority Data Collaboration Lab (HADCL). 

Methods: This was a territory-wide, retrospective, observational, cohort study of patients with CVH in Hong Kong in 2000–2018 identified from HADCL based on viral markers, diagnosis codes, and antiviral treatment for chronic hepatitis B and/or C. The cohort was randomly split into training and validation cohorts in a 7:3 ratio. Five popular machine learning methods, namely, logistic regression, ridge regression, AdaBoost, decision tree, and random forest, were performed and compared to find the best prediction model. 

Results: A total of 124,006 patients with CVH with complete data were included to build the models. In the training cohort (n = 86,804; 6,821 HCC), ridge regression (area under the receiver operating characteristic curve [AUROC] 0.842), decision tree (0.952), and random forest (0.992) performed the best. In the validation cohort (n = 37,202; 2,875 HCC), ridge regression (AUROC 0.844) and random forest (0.837) maintained their accuracy, which was significantly higher than those of HCC risk scores: CU-HCC (0.672), GAG-HCC (0.745), REACH-B (0.671), PAGE-B (0.748), and REAL-B (0.712) scores. The low cut-off (0.07) of HCC ridge score (HCC-RS) achieved 90.0% sensitivity and 98.6% negative predictive value (NPV) in the validation cohort. The high cut-off (0.15) of HCC-RS achieved high specificity (90.0%) and NPV (95.6%); 31.1% of patients remained indeterminate. 

Conclusions: HCC-RS from the ridge regression machine learning model accurately predicted HCC in patients with CVH. These machine learning models may be developed as built-in functional keys or calculators in electronic health systems to reduce cancer mortality. 

Lay summary: Novel machine learning models generated accurate risk scores for hepatocellular carcinoma (HCC) in patients with chronic viral hepatitis. HCC ridge score was consistently more accurate than existing HCC risk scores. These models may be incorporated into electronic medical health systems to develop appropriate cancer surveillance strategies and reduce cancer death.

Original languageEnglish
Article number100441
Number of pages12
JournalJHEP Reports
Volume4
Issue number3
Early online date21 Jan 2022
DOIs
Publication statusPublished - 1 Mar 2022

Scopus Subject Areas

  • Gastroenterology
  • Internal Medicine
  • Immunology and Allergy
  • Hepatology

User-Defined Keywords

  • Antiviral treatment
  • Cirrhosis
  • Liver cancer
  • Mortality
  • World Health Organization

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