ArticleEuropean radiology2025
Predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma: nomograms based on deep learning analysis of gadoxetic acid-enhanced MRI.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping the knowledge domains of literature on hepatocellular carcinoma and liver failure: a bibliometric approach.Frontiers in oncology · 2025Pooled it
- Artificial intelligence and personalised medicine in liver cancer.Journal of hepatology · 2026Review
- Prediction of post-hepatectomy liver failure risk in patients with colorectal cancer liver metastases utilizing gadoxetic acid-enhanced MRI radiomics with automatic segmentation and image registration.Abdominal radiology (New York) · 2026Article
- Beyond morphology: functional imaging of the liver with gadoxetic acid-enhanced magnetic resonance imaging.Abdominal radiology (New York) · 2026Review
- Which Method Best Predicts Postoperative Complications: Deep Learning, Machine Learning, or Conventional Logistic Regression?Annals of gastroenterological surgery · 2026Review
- [Estimation of the parenchymal reserve-Volumetric and functional before resection].Chirurgie (Heidelberg, Germany) · 2026Review
- Predictive model development and validation of functional liver imaging score for prognosis of patients with hepatocellular carcinoma after surgical resection: a multicenter study.La Radiologia medica · 2026Article
- Preoperative evaluation of post-hepatectomy liver failure in hepatocellular carcinoma based on gadoxetic acid-enhanced MRI.Frontiers in oncology · 2026Article
- Emerging Role of MRI-Based Artificial Intelligence in Individualized Treatment Strategies for Hepatocellular Carcinoma: A Narrative Review.Journal of magnetic resonance imaging : JMRI · 2026Review
- The value of prealbumin in predicting post hepatectomy liver failure for patients with hepatocellular carcinoma undergoing major hepatectomy.Scientific reports · 2025Article
- Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024.JHEP reports : innovation in hepatology · 2024Review
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Authors and funding
8 authors.
Funding
Abstract
objectivesThis study aimed to develop nomograms for predicting post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC), using deep learning analysis of Gadoxetic acid-enhanced hepatobiliary (HBP) MRI.
methodsThis retrospective study analyzed patients who underwent gadoxetic acid-enhanced MRI and hepatectomy for HCC between 2016 and 2020 at two referral centers. Using a deep learning algorithm, volumes and signal intensities of whole non-tumor liver, expected remnant liver, and spleen were measured on HBP images. Two multivariable logistic regression models were formulated to predict PHLF, defined and graded by the International Study Group of Liver Surgery: one based on whole non-tumor liver measurements (whole liver model) and the other on expected remnant liver measurements (remnant liver model). The models were presented as nomograms and a web-based calculator. Discrimination performance was evaluated using the area under the receiver operating curve (AUC), with internal validation through 1000-fold bootstrapping.
resultsThe study included 1760 patients (1395 male; mean age ± standard deviation, 60 ± 10 years), with 137 (7.8%) developing PHLF. Nomogram predictors included sex, gamma-glutamyl transpeptidase, prothrombin time international normalized ratio, platelets, extent of liver resection, and MRI variables derived from the liver volume, liver-to-spleen signal intensity ratio, and spleen volume. The whole liver and the remnant liver nomograms demonstrated strong predictive performance for PHLF (optimism-corrected AUC of 0.78 and 0.81, respectively) and symptomatic (grades B and C) PHLF (optimism-corrected AUC of 0.81 and 0.84, respectively).
conclusionNomograms based on deep learning analysis of gadoxetic acid-enhanced HBP images accurately stratify the risk of PHLF. KEY POINTS: Question Can PHLF be predicted by integrating clinical and MRI-derived volume and functional variables through deep learning analysis of gadoxetic acid-enhanced MRI? Findings Whole liver and remnant liver nomograms demonstrated strong predictive performance for PHLF with the optimism-corrected area under the curve of 0.78 and 0.81, respectively. Clinical relevance These nomograms can effectively stratify the risk of PHLF, providing a valuable tool for treatment decisions regarding hepatectomy for HCC.
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