ArticleBMC cancer2025
Machine learning predicts post-transplant muscle loss in hepatocellular carcinoma patients without sarcopenia.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Efficacy of combined laparoscopic hepatectomy and splenic artery ligation for hepatocellular carcinoma with portal hypertension.Surgery today · 2026Article
- CD3+ T-cell count prediction for anti-thymocyte globulin treatment monitorization in kidney transplant recipients: a machine learning model.Frontiers in medicine · 2026Article
- Overview of research and practice progress in body composition measurement techniques.Frontiers in medical technology · 2026Review
- Artificial intelligence-based prognostic modeling of immunoradiotherapy in Barcelona clinic liver cancer stage C hepatocellular carcinoma: a multicenter retrospective study.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
objectiveDeveloping a machine learning model to predict post-transplant muscle loss in hepatocellular carcinoma patients.
backgroundLiver transplantation is an effective treatment for selected HCC patients. However, severe muscle loss after liver transplantation is significantly associated with increased risk of mortality and recurrence. However, effective predictive methods remain inadequate.
methodsThis study collected data from hepatocellular carcinoma patients who underwent liver transplantation over the past 2015 to 2020 at two hospitals. Propensity score matching and Cox regression analysis were conducted to establish muscle loss as an independent risk factor for recurrence. To construct the optimal predictive model for post-transplant muscle loss, we compared 50 machine learning models and use Recursive Feature Elimination to identify the most relative features.
resultsData from a total of 248 patients were collected. Kaplan-Meier analysis revealed a significant difference in prognosis between patients with and without sarcopenia before surgery. For patients without sarcopenia, postoperative muscle loss was identified as an independent risk factor for recurrence (HR = 2.38, P = 0.005). The best model was identified as the Imbalanced Random Forest, achieving an AUC of 0.832 on the non-sarcopenia cohort.
conclusionsA highly efficient model based on machine learning was developed to predict postoperative muscle loss in hepatocellular carcinoma patients undergoing liver transplantation, providing a valuable reference for the early detection of adverse events following the procedure.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.