Trial reportBMC surgery2023
Preoperative prediction model for microvascular invasion in HBV-related intrahepatic cholangiocarcinoma.
Trial report in BMC surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Development and validation of a SHAP-explainable preoperative predictive model for microvascular invasion in hepatocellular carcinoma.BMC cancer · 2026Article
- Preoperative prediction model of microvascular invasion in intrahepatic cholangiocarcinoma patients based on CT radiomics can assist clinical surgical decision-making: a multicenter study.European radiology · 2026Article
- Review
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Authors and funding
9 authors.
Funding
Abstract
BACKGROUND AND
aimsPreoperative prediction of microvascular invasion (MVI) using a noninvasive method remain unresolved, especially in HBV-related in intrahepatic cholangiocarcinoma (ICC). This study aimed to build and validate a preoperative prediction model for MVI in HBV-related ICC.
methodsPatients with HBV-associated ICC undergoing curative surgical resection were identified. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors of MVI in the training cohort. Then, a prediction model was built by enrolling the independent risk factors. The predictive performance was validated by receiver operator characteristic curve (ROC) and calibration in the validation cohort.
resultsConsecutive 626 patients were identified and randomly divided into the training (418, 67%) and validation (208, 33%) cohorts. Multivariate analysis showed that TBIL, CA19-9, tumor size, tumor number, and preoperative image lymph node metastasis were independently associated with MVI. Then, a model was built by enrolling former fiver risk factors. In the validation cohort, the performance of this model showed good calibration. The area under the curve was 0.874 (95% CI: 0.765-0.894) and 0.729 (95%CI: 0.706-0.751) in the training and validation cohort, respectively. Decision curve analysis showed an obvious net benefit from the model.
conclusionBased on clinical data, an easy model was built for the preoperative prediction of MVI, which can assist clinicians in surgical decision-making and adjuvant therapy.
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