ArticleCancer medicine2022
Machine learning to predict waitlist dropout among liver transplant candidates with hepatocellular carcinoma.
Article in Cancer medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Machine learning prediction of surgical approach and clinical heterogeneity in cervical spondylotic myelopathy.BMC musculoskeletal disorders · 2026Article
- Improving surgical treatments for hepatocellular carcinoma.Nature reviews. Gastroenterology & hepatology · 2026Review
- Artificial intelligence in liver transplantation: Opportunities and challenges.World journal of gastrointestinal surgery · 2025Review
- Mechanisms of ferroptosis in primary hepatocellular carcinoma and progress of artificial intelligence-based predictive modeling in hepatocellular carcinoma.World journal of gastroenterology · 2025Review
- Sarcopenia and frailty: An in-depth analysis of the pathophysiology and effect on liver transplant candidates.World journal of hepatology · 2025Review
- Machine learning in solid organ transplantation: Charting the evolving landscape.World journal of transplantation · 2025Article
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
- Correspondence to editorial on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".Clinical and molecular hepatology · 2024Article
- Recent Advances in Liver Transplantation for Hepatocellular Carcinoma.Current treatment options in oncology · 2024Review
- Construction of a predictive model for blood transfusion in patients undergoing total hip arthroplasty and identification of clinical heterogeneity.Scientific reports · 2024Article
- Predicting waitlist dropout in hepatocellular carcinoma: a narrative review.Translational gastroenterology and hepatology · 2024Review
- Use of machine learning models for identification of predictors of survival and tumour recurrence in liver transplant recipients with hepatocellular carcinoma.Annals of translational medicine · 2023Article
- Machine learning to predict waitlist dropout among liver transplant candidates with hepatocellular carcinoma.Cancer medicine · 2022Article
- Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics.Frontiers in surgery · 2022Article
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Authors and funding
10 authors.
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Abstract
backgroundAccurate prediction of outcome among liver transplant candidates with hepatocellular carcinoma (HCC) remains challenging. We developed a prediction model for waitlist dropout among liver transplant candidates with HCC.
methodsThe study included 18,920 adult liver transplant candidates in the United States listed with a diagnosis of HCC, with data provided by the Organ Procurement and Transplantation Network. The primary outcomes were 3-, 6-, and 12-month waitlist dropout, defined as removal from the liver transplant waitlist due to death or clinical deterioration.
resultsUsing 1,181 unique variables, the random forest model and Spearman's correlation analyses converged on 12 predictive features involving 5 variables, including AFP (maximum and average), largest tumor size (minimum, average, and most recent), bilirubin (minimum and average), INR (minimum and average), and ascites (maximum, average, and most recent). The final Cox proportional hazards model had a concordance statistic of 0.74 in the validation set. An online calculator was created for clinical use and can be found at: http://hcclivercalc.cloudmedxhealth.com/.
conclusionIn summary, a simple, interpretable 5-variable model predicted 3-, 6-, and 12-month waitlist dropout among patients with HCC. This prediction can be used to appropriately prioritize patients with HCC and their imminent need for transplant.
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