To construct a nomogram model based on machine learning of MRI radiomics to predict the risk of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) before surgery, and to assist the selection of individualized treatment.

Clinical data of 176 patients pathologically diagnosed with HCC who underwent surgical resection in the First Affiliated Hospital of China Medical University from June 2019 to December 2022 were retrospectively analyzed. The informed consents of all patients were obtained and the local ethical committee approval was received. Among them, 148 patients were male and 28 female, aged from 32 to 82 years, with a median age of 61 years. Clinicopathological data of 176 HCC patients were collected and analyzed. All patients were randomly divided into the training set (n=123) and test set (n=53) according to a ratio of 7∶3 (simple random sampling method). The sklearn software package in Python was used to fit five machine learning models: K-nearest neighbor, random forest, logistic regression, support vector machine and naive Bayes in the training set. The accuracy, sensitivity, specificity, F1 score and the area under the ROC curve (AUC) were compared between the training and test sets to evaluate the prediction efficiency of each machine learning model, and the model with the optimal comprehensive performance was selected to generate the radiomics score. Clinicopathological features of MVI-positive patients were analyzed by Chi-square test. Multivariate Logistic regression analysis was employed to analyze the risk factors of MVI and establish a nomogram prediction model. ROC, decision curve and calibration curve were utilized to evaluate the predictive ability and clinical value of these models.

Among 176 patients, 54 cases (30.7%) were positive for MVI, including 42 cases of grade M1 and 12 of grade M2. 122 patients (69.3%) were negative for MVI. Positive MVI was associated with age ≤50 years, increased AFP, the maximum tumor diameter >5 cm, multiple tumors, peritumoral enhancement in arterial phase and intra-tumoral necrosis (χ²=0.049, 0.047, 0.002, 0.049, 0.031, 0.016; all P<0.05). The maximum tumor diameter >5 cm, peritumoral enhancement in arterial phase and radiomics score were the independent risk factors for MVI in HCC (OR=3.733,3.130, 2.007; all P<0.05). The AUC of each nomogram model was 0.856 (training set) and 0.772 (test set). The decision curve and calibration curve indicated that the models possessed high clinical practicability.

The nomogram model based on machine learning of MRI radiomics has high clinical value for predicting the risk of MVI of HCC before surgery and guiding decision-making of diagnosis and treatment.