ArticleScientific reports2025
Deep learning for hepatocellular carcinoma recurrence before and after liver transplantation: a multicenter cohort study.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Artificial intelligence and personalised medicine in liver cancer.Journal of hepatology · 2026Review
- A Leakage-Free Survival-Modelling Benchmark for Hepatocellular Carcinoma Recurrence After Liver Transplantation: Nested Cross-Validation Against the Milan Criteria.Bioengineering (Basel, Switzerland) · 2026Article
- Emerging applications of traditional and generative artificial intelligence in liver transplantation.Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society · 2026Article
- Improving surgical treatments for hepatocellular carcinoma.Nature reviews. Gastroenterology & hepatology · 2026Review
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- Article
- Noninvasive MRI imaging feature-based prediction of intratumoral tertiary lymphoid structure maturity in hepatocellular carcinoma: a multicenter retrospective study.European radiology · 2026Article
- Multiphasic CT-based multimodal deep learning model for predicting early hepatocellular carcinoma recurrence following liver transplantation.Frontiers in oncology · 2026Article
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Prediction of Recurrence using a Stacked Denoising Autoencoder and Multifaceted Feature Analysis of Pretreatment MRI in Patients with Nasopharyngeal Carcinoma.Current radiopharmaceuticals · 2025Article
- Interpretable machine learning models for pre- and postoperative prediction of early intra-abdominal infections after liver transplantation: a multicenter retrospective cohort study.Therapeutic advances in infectious diseaseArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) is a major contributor to mortality. We developed a recurrence prediction system for HCC patients before and after LT. Data from patients with HCC who underwent LT were retrospectively collected from three specialist centres in China. Pre- and post-operative variables were selected using support vector machine, random forest, and logistic regression (LR). Then, pre- and post-operative models were developed using three machine learning methods: LR, stacking, and two survival-based approaches. Models were evaluated using seven assessment indices, and patients were classified as either high- or low-risk based on recurrence risk. 466 patients were included and followed for a median of 51.0 months (95% CI 47.8-54.2). The pre-DeepSurv model (pre-DSM) had a C-index of 0.790 ± 0.003 during training, 0.775 ± 0.037 during testing, and 0.765 ± 0.001 and 0.819 ± 0.002 during external validation. After incorporating clinicopathologic variables, the post-DeepSurv model (post-DSM) had a 0.835 ± 0.008 C-index during training, 0.812 ± 0.082 during testing, and 0.839 ± 0.001 and 0.831 ± 0.002 during external validation. The post-DSM outperformed the Milan criteria by more accurately identifying patients at high risk of recurrence. Tumour recurrence predictions also improved significantly with DeepSurv. Both pre- and post-DSMs have the potential to guide personalised surveillance strategies for LT patients with HCC.
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.