ArticleBMC medical imaging2025
Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Predictive Efficacy of a Combined Triphasic CT Radiomics and Clinical Feature Model for Ki-67 Expression in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Multimodal imaging techniques in Yttrium-90 radioembolization for hepatocellular carcinoma: a modality-oriented clinical review.Frontiers in oncology · 2026Review
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8 authors.
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Abstract
objectiveTo preoperatively predict microvascular invasion (MVI) and relapse-free survival (RFS) in hepatocellular carcinoma (HCC) ≥3 cm by constructing and externally validating a combined radiomics model using preoperative enhanced CT images.
methodsThis retrospective study recruited adults who underwent surgical resection between September 2016 and August 2020 in our hospital with pathologic confirmation of HCC ≥3 cm and MVI status. For external validation, adults who underwent surgical resection between September 2020 and August 2021 in our hospital were included. Histopathology was the reference standard. The HCC area was segmented on the arterial and portal venous phase CT images to develop a CT radiomics model. A combined model was developed using selected radiomics features, demographic information, laboratory index and radiological features. Analysis of variance and support vector machine were used as features selector and classifier. Receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were used to evaluate models' performance. The Kaplan-Meier method and log-rank test were used to evaluate the predictive value for RFS.
resultsA total of 202 patients were finally enrolled (median age, 59 years, 173 male). Thirteen and 24 features were selected for the CT radiomics model and the combined model, and the area under the ROC curves (AUC) were 0.752 (95 %CI 0.615, 0.889) and 0.890 (95 %CI 0.794, 0.985) in the external validation set, respectively. Calibration curves and DCA showed a higher net clinical benefit of the combined model. The high-risk group (P < 0.001) was an independent predictor for RFS.
conclusionsThe combined model showed high accuracy for preoperatively predicting MVI and RFS in HCC ≥3 cm.
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