ArticleLiver cancer2021
Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection.
Article in Liver cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05543304 (Predicting Response to Systemic Therapies for Hepatocellular Carcinoma), which is not on this map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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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.
Predicting Response to Systemic Therapies for Hepatocellular Carcinoma(HCC) Based on Clinical Variables and Radiomics Data With Machine Learning Methods
Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for the prediction of posthepatectomy recurrence in hepatocellular carcinoma: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.JMIR perioperative medicine · 2026Article
- Optimal treatment selection for hepatocellular carcinoma in the era of immunotherapy.Journal of gastroenterology · 2026Review
- Prognostic value of CT-derived body composition phenotypes for patients undergoing resection of hepatocellular carcinoma: a multicenter study.Abdominal radiology (New York) · 2026Article
- AI-Driven Predictive Models of Early Recurrence of HCC After Surgical Resection: A Systematic Review.Cancers · 2026Review
- Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.Journal of medical Internet research · 2026Article
- Three-dimensional multimodal imaging for predicting early recurrence of hepatocellular carcinoma after surgical resection.Journal of advanced research · 2026Article
- Development of a CT radiomics and clinical feature combined model for predicting early recurrence of surgical resected hepatocellular carcinoma.Scientific reports · 2026Article
- Individualized Risk Stratification for Early Recurrence of Hepatocellular Carcinoma: A Clinical Tool Derived from a Large-Scale (n=3084) Cohort Study.Journal of hepatocellular carcinoma · 2026Article
- Development and validation of a machine learning model for predicting hypersplenism in Wilson disease patients.Frontiers in medicine · 2026Article
- Liver disease trends in the Asia-Pacific region for the next 50 years.Clinical and molecular hepatology · 2025Review
- MRI-based prediction of the need for wide resection margins in patients with single hepatocellular carcinoma.European radiology · 2025Article
- CT-Based Radiomics Features Combined with AFP for Predicting Vessels Encapsulating Tumor Clusters and Prognosis of Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- Predictive Radiomics-Based Model for Recurrence-Free Survival After Curative Resection in Patients with Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- Association of TIMP-2 Expression with Postoperative Prognosis in Hepatocellular Carcinoma Patients and Development of a Predictive Model.Journal of inflammation research · 2025Article
- Deep Learning with Contrast-Enhanced Ultrasound for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma After Hepatectomy.Journal of multidisciplinary healthcare · 2025Article
- The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review.BMC medical informatics and decision making · 2024Review
- Article
- CT radiomics-based biomarkers can predict response to immunotherapy in hepatocellular carcinoma.Scientific reports · 2024Article
- A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsCurrent prediction models for early recurrence of hepatocellular carcinoma (HCC) after surgical resection remain unsatisfactory. The aim of this study was to develop evolutionary learning-derived prediction models with interpretability using both clinical and radiomic features to predict early recurrence of HCC after surgical resection.
methodsConsecutive 517 HCC patients receiving surgical resection with available contrast-enhanced computed tomography (CECT) images before resection were retrospectively enrolled. Patients were randomly assigned to a training set (
resultsA total of 143 features, including 26 preoperative clinical features, 5 postoperative pathological features, and 112 radiomic features were used to develop GARSL preoperative and postoperative models. The area under the receiver operating characteristic curves (AUCs) for early recurrence of HCC within 2 years were 0.781 and 0.767, respectively, in the training set, and 0.739 and 0.741, respectively, in the test set. The accuracy of GARSL models derived from the evolutionary learning method was significantly better than models derived from other well-known machine learning methods or the early recurrence after surgery for liver tumor (ERASL) preoperative (AUC = 0.687,
conclusionThe GARSL models using both clinical and radiomic features significantly improved the accuracy to predict early recurrence of HCC after surgical resection, which was significantly better than other well-known machine learning-derived models and currently available clinical models.
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