A nomogram model for predicting microvascular invasion of hepatocellular carcinoma based on multi-sequence MRI radiomics score and clinical-pathology-image parameters

Abstract

Abstract Objective: Established aradiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model formicrovascular invasion (MVI) of hepatocellular carcinoma (HCC). Method: The preoperative liver MRI data and clinical information of 130 HCC patients with pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf-SVM with the best predictive performance was selected to construct the radiomics score (R-score). Finally, we combined R-score and clinical-pathology-image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC). Result: Alpha-fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95%CI: 0.920-1.000) constructed by radiometry score (R-score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance. Conclusion: This multi-parameter combined nomogram model hada good performance in predicting MVIof HCC, and hadcertain auxiliary value for the formulation of surgical plan and evaluation of prognosis.