| Conventional and machine-learning based risk score for patients with early-stage hepatocellular carcinoma |
| Chun-Ting Ho1, Elise Chia-Hui Tan2, Pei-Chang Lee1,3, Chi-Jen Chu1,3, Yi-Hsiang Huang3,4, Teh-Ia Huo5, Yu-Hui Su6, Ming-Chih Hou1,3, Jaw-Ching Wu4, Chien-Wei Su1,3,4,7 |
1Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
2Department of Health Service Administration, College of Public Health, China Medical University, Taichung, Taiwan
3School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
4Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
5Division of Basic Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
6Department of Accounting, Soochow University, Taipei, Taiwan
7Department of General Medicine, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan |
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Received: February 9, 2024 Revised: April 10, 2024 Accepted: April 10, 2024 |
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| ABSTRACT |
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Background/Aims
The performance of machine-learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups.
Methods
The study retrospectively enrolled 1411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort.
Results
In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-to-monocyte ratio, and albumin bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATS-INF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores.
Conclusions
Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance. |
| KeyWords:
Fibrosis; Hepatocellular carcinoma; Inflammation; Machine learning; Prognosis |
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