ArticleFrontiers in oncology2021
Prediction of Microvascular Invasion and Its M2 Classification in Hepatocellular Carcinoma Based on Nomogram Analyses.
Article in Frontiers in oncology, 2021. 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 6 papers.
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Who cites it
6 citing papers in PubMed.
- Advancing microvascular invasion diagnosis: a multi-center investigation of novel MRI-based models for precise detection and classification in early-stage small hepatocellular carcinoma (≤ 3 cm).Abdominal radiology (New York) · 2025Article
- Article
- Preoperative prediction of microvascular invasion classification in hepatocellular carcinoma based on clinical features and MRI parameters.Oncology letters · 2024Article
- Surgical approach to achieve R0 resections in primary and metastatic liver tumors: a literature review.Journal of gastrointestinal oncology · 2023Review
- Time to surgery is not an oncological risk factor in HCC patients undergoing liver resection.Langenbeck's archives of surgery · 2023Article
- Clinical features and prognostic factors in patients with microvascular infiltration of hepatocellular carcinoma: Development and validation of a nomogram and risk stratification based on the SEER database.Frontiers in oncology · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsAs a key pathological factor, microvascular invasion (MVI), especially its M2 grade, greatly affects the prognosis of liver cancer patients. Accurate preoperative prediction of MVI and its M2 classification can help clinicians to make the best treatment decision. Therefore, we aimed to establish effective nomograms to predict MVI and its M2 grade.
methodsA total of 111 patients who underwent radical resection of hepatocellular carcinoma (HCC) from January 2015 to September 2020 were retrospectively collected. We utilized logistic regression and least absolute shrinkage and selection operator (LASSO) regression to identify the independent predictive factors of MVI and its M2 classification. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to select the potential predictive factors from the results of LASSO and logistic regression. Nomograms for predicting MVI and its M2 grade were then developed by incorporating these factors. Area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were respectively used to evaluate the efficacy, accuracy, and clinical utility of the nomograms.
resultsCombined with the results of LASSO regression, logistic regression, and IDI and NRI analyses, we founded that clinical tumor-node-metastasis (TNM) stage, tumor size, Edmondson-Steiner classification, α-fetoprotein (AFP), tumor capsule, tumor margin, and tumor number were independent risk factors for MVI. Among the MVI-positive patients, only clinical TNM stage, tumor capsule, tumor margin, and tumor number were highly correlated with M2 grade. The nomograms established by incorporating the above variables had a good performance in predicting MVI (AUC
conclusionsThe nomograms of this study make it possible to do individualized predictions of MVI and its M2 classification, which may help us select an appropriate treatment plan.
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