ArticleScientific reports2024
Nomogram for predicting early recurrence of hepatocellular carcinoma with narrow resection margin.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Integrated clinicopathological model versus TNM for predicting survival in resected hepatocellular carcinoma: a retrospective cohort study.BMC gastroenterology · 2026Article
- Establishment and Validation of a Nomogram Based on Inflammation-Immunity-Nutrition Biomarker Scores to Predict Postoperative Early Recurrence in Patients with Hepatocellular Carcinoma: A Multicenter Study.Journal of hepatocellular carcinoma · 2026Article
- Machine Learning-Based Models for the Prediction of Postoperative Recurrence Risk in MVI-Negative HCC.Biomedicines · 2025Article
- Nomogram for predicting postoperative recurrence in patients with microvascular invasion-negative hepatocellular carcinoma: development and validation.Frontiers in immunology · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
purposeNarrow resection margin hepatocellular carcinoma (NRM-HCC) has a high incidence of early recurrence. Our study was designed to identify prognostic factors in patients with NRM-HCC, establish and validate a nomogram model to predict early recurrence of NRM-HCC patients.
methodsWe retrospectively analyzed data from 2957 NRM-HCC patients who underwent radical hepatectomy at three medical centers between December 2009 and January 2015. Patients were randomly assigned to a training cohort (n = 2069) and a validation cohort (n = 888). Using univariate and multivariate COX regression to determine early relapse factors in NRM-HCC patients, and used these factors to construct a nomogram. The accuracy of the prediction was evaluated using the C-index, receiver operating characteristic (ROC) and calibration curve. Decision curve analysis (DCA) assessed the predictive value of the models. Finally, the recurrence-free survival of different risks was analyzed using Kaplan-Meier (K-M) method.
resultsThe nomogram of NRM model contains alpha-fetoprotein (AFP), alkaline phosphatase (ALP), tumor size, tumor number, microvascular invasion (MVI), tumor capsular, and satellite nodules. The model shows good discrimination with C-indexes of 0.71 (95% CI: 0.69-0.72) and 0.72 (95% CI: 0.70-0.75) in the train cohort and test cohort respectively. Decision curve analysis demonstrated that the model is clinically useful and the calibration of our model was favorable. Our model stratified patients into two different risk groups, which exhibited significantly different early recurrence. The web-based tools are convenient for clinical practice.
conclusionsNRM model demonstrated favorable performance in predicting early recurrence in NRM-HCC patients. This novel model will be helpful to guide postoperative follow-up and adjuvant therapy.
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