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 2017 to December 2019 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 (AUCMVI = 0.926) and its M2 classification (AUCM2 = 0.803). The calibration curve confirmed that predictions and actual observations were in good agreement. Significant clinical utility of our nomograms was demonstrated by DCA.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.
Highlights
Primary liver cancer is one of the most common cancers worldwide and globally ranks fifth and fourth in morbidity and mortality, respectively [1]
The criteria for the exclusion of patients were as follows: 1) abdominal contrastenhanced CT and blood index tests were performed more than 1 week before surgery; 2) the surgical margin was not confirmed to be R0 defined in a previous report [23]; 3) patients underwent hepatectomy more than one time; 4) patients received radiofrequency ablation (RFA), transarterial chemoembolization (TACE), neoadjuvant chemotherapy, and/or radiotherapy before surgery; 5) patients who have a history of other malignant tumors; 6) Microvascular invasion (MVI) status was not evaluated by histopathological examination; 7) hepatocellular carcinoma (HCC) with macrovascular or extrahepatic invasion; and 8) incomplete clinical data
We developed model B, model C, and model D by respectively adding clinical TNM stage, aspartate transaminase (AST), and tumor number to the base model A and found that model B and model D are better than model A for predicting M2 grade in the presence of MVI, whereas model C did not show any superiority in M2 prediction (Table 4), suggesting that clinical TNM stage and tumor number can be definitely considered as the risk factors of M2 grade when MVI is present
Summary
Primary liver cancer is one of the most common cancers worldwide and globally ranks fifth and fourth in morbidity and mortality, respectively [1]. In China, liver cancer was reported as the fourth most common cancer in 2015, and its mortality ranked second among malignant tumors [2], with approximately 466,100 new cases and 422,000 deaths [3]. As the most common type of liver cancer, hepatocellular carcinoma (HCC) has high invasiveness, and its 5-year recurrence rate after surgery is nearly 70% [4, 5], which results in a poor prognosis [6]. MVI is considered as a critical pathological factor correlated with tumor recurrence and survival [12] and has been used as a prognostic reference index in the treatment options for both primary and recurrent HCC [13, 14]. As a key pathological factor, microvascular invasion (MVI), especially its M2 grade, greatly affects the prognosis of liver cancer patients. We aimed to establish effective nomograms to predict MVI and its M2 grade
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