ArticleJHEP reports : innovation in hepatology2023
Predicting post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma.
Article in JHEP reports : innovation in hepatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Dual-energy CT-derived multiparameters for noninvasive prediction of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- Assessment prior to liver tumor resection: what a radiologist needs to know.Abdominal radiology (New York) · 2026Review
- Preoperative contrast-enhanced CT prediction of distinct vascular patterns in solitary early-stage hepatocellular carcinoma and its prognostic value.Insights into imaging · 2026Article
- An Interpretable MRI-Based Machine Learning Model for Preoperative Identification of the Vascular Dissemination Phenotype in Hepatocellular Carcinoma: A Dual-Center Study.Journal of hepatocellular carcinoma · 2026Article
- Anatomical resection is associated with reduced early recurrences in HCC: A machine learning analysis.JHEP reports : innovation in hepatology · 2025Article
- Preliminary study on the ability ofQuantitative imaging in medicine and surgery · 2025Article
- VETC predicting model based on CE-CT can predict prognosis and assisting treatment plan for solitary HCC: better together with radiomics.BMC cancer · 2025Article
- Folate receptor 1 is a stemness trait-associated diagnostic and prognostic marker for hepatocellular carcinoma.Biomarker research · 2025Article
- Intra- and Peritumoral Radiomic Signatures on CECT: Prediction of Aggressive Hepatocellular Carcinoma Subtypes and 2-Year Recurrence.Journal of hepatocellular carcinoma · 2025Article
- Intraindividual comparison of CT and MRI for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.European radiology · 2025Article
- Development of a deep learning model for predicting recurrence of hepatocellular carcinoma after liver transplantation.Frontiers in medicine · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background & Aims: Distinct vascular patterns, including microvascular invasion (MVI) and vessels encapsulating tumour clusters (VETC), are associated with poor outcomes of hepatocellular carcinoma (HCC). Imaging surrogates of these vascular patterns potentially help to predict post-resection recurrence. Herein, a prognostic model integrating imaging-based surrogates of these distinct vascular patterns was developed to predict postoperative recurrence-free survival (RFS) in patients with HCC. Methods: Clinico-radiological data of 1,285 patients with HCC from China undergoing surgical resection were retrospectively enrolled from seven medical centres between 2014 and 2020. A prognostic model using clinical data and imaging-based surrogates of MVI and VETC patterns was developed (n = 297) and externally validated (n = 373) to predict RFS. The surrogates (i.e. MVI and VETC scores) were individually built from preoperative computed tomography using two independent cohorts (n = 360 and 255). Whether the model's stratification was associated with postoperative recurrence following anatomic resection was also evaluated. Results: The MVI and VETC scores demonstrated effective performance in their respective training and validation cohorts (AUC: 0.851-0.883 for MVI and 0.834-0.844 for VETC). The prognostic model incorporating serum alpha-foetoprotein, tumour multiplicity, MVI score, and VETC score achieved a C-index of 0.748-0.764 for the developing and external validation cohorts and generated three prognostically distinct strata. For patients at model-predicted medium risk, anatomic resection was associated with improved RFS ( Conclusions: The proposed model integrating imaging-based surrogates of distinct vascular patterns enabled accurate prediction for RFS. It can potentially be used to identify HCC surgical candidates who may benefit from anatomic resection. Impact and implications: MVI and VETC are distinct vascular patterns of HCC associated with aggressive biological behaviour and poor outcomes. Our multicentre study provided a model incorporating imaging-based surrogates of these patterns for preoperatively predicting RFS. The proposed model, which uses imaging detection to estimate the risk of MVI and VETC, offers an opportunity to help shed light on the association between tumour aggressiveness and prognosis and to support the selection of the appropriate type of surgical resection.
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