Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma

Ho, Chun-Ting; Tan, Elise Chia-Hui; Lee, Pei-Chang; Chu, Chi-Jen; Huang, Yi-Hsiang; Huo, Teh-Ia; Su, Yu-Hui; Hou, Ming-Chih; Wu, Jaw-Ching; Su, Chien-Wei

doi:10.3350/cmh.2024.0103

Clin Mol Hepatol

2024

DOI: 10.3350/cmh.2024.0103

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Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma

Chun-Ting Ho,

Elise Chia-Hui Tan,

Pei-Chang Lee

et al.

Abstract: Informed consent was waived by the IRB since this was a retrospective observational cohort study, and patient information was de-identified before the study commenced.

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Cited by 4 publications

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Risk predictive model for the development of hepatocellular carcinoma before initiating long‐term antiviral therapy in patients with chronic hepatitis B virus infection

Chen,

Feng,

et al. 2024

Journal of Medical Virology

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It is generally acknowledged that antiviral therapy can reduce the incidence of hepatitis B virus (HBV)‐related hepatocellular carcinoma (HCC), there remains a subset of patients with chronic HBV infection who develop HCC despite receiving antiviral treatment. This study aimed to develop a model capable of predicting the long‐term occurrence of HCC in patients with chronic HBV infection before initiating antiviral therapy. A total of 1450 patients with chronic HBV infection, who received initial antiviral therapy between April 2006 and March 2023 and completed long‐term follow‐ups, were nonselectively enrolled in this study. Least absolute shrinkage and selection operator (LASSO) and Cox regression analysis was used to construct the model. The results were validated in an external cohort (n = 210) and compared with existing models. The median follow‐up time for all patients was 60 months, with a maximum follow‐up time of 144 months, during which, 32 cases of HCC occurred. The nomogram model for predicting HCC based on GGT, AFP, cirrhosis, gender, age, and hepatitis B e antibody (TARGET‐HCC) was constructed, demonstrating a good predictive performance. In the derivation cohort, the C‐index was 0.906 (95% CI = 0.869–0.944), and in the validation cohort, it was 0.780 (95% CI = 0.673–0.886). Compared with existing models, TARGET‐HCC showed promising predictive performance. Additionally, the time‐dependent feature importance curve indicated that gender consistently remained the most stable predictor for HCC throughout the initial decade of antiviral therapy. This simple predictive model based on noninvasive clinical features can assist clinicians in identifying high‐risk patients with chronic HBV infection for HCC before the initiation of antiviral therapy.

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Risk predictive model for the development of hepatocellular carcinoma before initiating long‐term antiviral therapy in patients with chronic hepatitis B virus infection

Chen,

Feng,

et al. 2024

Journal of Medical Virology

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The association between proton‐pump inhibitor use and recurrence of hepatocellular carcinoma after hepatectomy

Ho,

Fu,

Tan

et al. 2024

J of Gastro and Hepatol

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Background and AimThe association between long‐term proton‐pump inhibitors (PPIs) use and malignancies had long been discussed, but it still lacks consensus. Our study investigated the association between PPI use and hepatocellular carcinoma (HCC) recurrence following curative surgery.MethodsWe retrospectively enrolled 6037 patients with HCC who underwent hepatectomy. Patients were divided into four groups according to their PPI usage. (non‐users: < 28 cumulative defined daily dose [cDDD]; short‐term users: 28–89 cDDD; mid‐term users: 90–179 cDDD, and long‐term users: ≥ 180 cDDD, respectively). Recurrence‐free survival (RFS) and overall survival (OS) were analyzed using Kaplan–Meier method and Cox proportional hazard models.ResultsAmong the 6037 HCC patients, 2043 (33.84%) were PPI users. PPI users demonstrated better median RFS (3.10 years, interquartile range [IQR] 1.49–5.01) compared with non‐users (2.73 years, IQR 1.20–4.74; with an adjusted hazard ratio [aHR] of 0.57, 95% confidence interval [CI] 0.44–0.74, P < 0.001). When considering the cumulative dosage of PPI, only long‐term PPI users had significant lower risk of HCC recurrence than non‐PPI group (adj‐HR: 0.50; 95% CI: 0.35–0.70; P < 0.001). Moreover, the impact of long‐term PPIs use on improving RFS was significant in most of the subgroup analysis, except in patients with advanced tumor stages, with non‐cirrhosis, or with a history of chronic kidney disease. However, there were no significant differences in median OS between PPI users and non‐users (4.23 years, IQR 2.73–5.86 vs 4.04 years, IQR 2.51–5.82, P = 0.369).ConclusionLong‐term PPI use (≥ 180 cDDD) may be associated with a better RFS in HCC patients after hepatectomy.

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Unlocking the future: Machine learning sheds light on prognostication for early-stage hepatocellular carcinoma: Editorial on “Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma”

Dai,

Lai,

Wong

et al. 2024

Clin Mol Hepatol

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Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma

Abstract: Informed consent was waived by the IRB since this was a retrospective observational cohort study, and patient information was de-identified before the study commenced.

Cited by 4 publications

References 50 publications

Risk predictive model for the development of hepatocellular carcinoma before initiating long‐term antiviral therapy in patients with chronic hepatitis B virus infection

Risk predictive model for the development of hepatocellular carcinoma before initiating long‐term antiviral therapy in patients with chronic hepatitis B virus infection

The association between proton‐pump inhibitor use and recurrence of hepatocellular carcinoma after hepatectomy

Unlocking the future: Machine learning sheds light on prognostication for early-stage hepatocellular carcinoma: Editorial on “Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma”

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