Purpose：In this study, the aim was to assess the imaging features and radiomics of microvascular infiltration (MVI) of primary liver cancer (PLC) under the control of a seven-point pathological sampling method.
Methods: The data of 37 patients with PLC who underwent surgical resection in our hospital from October 2018 to September 2019 were retrospectively collected. Postoperative pathological specimens were collected using a seven-point sampling method to determine the presence of MVI. Preoperative CT and MRI scans were performed to characterize the tumors. Findings from the imaging studies were imported into the radiomics platform, and 70% and 30% of the data were randomly assigned to the training and validation sets, respectively. Lastly, support vector machine (SVM) classifiers were used to classify liver lesions into their respective pathological types.
Results: Differences in tumor morphology and satellite lesions were statistically significant between the MVI positive and MVI negative groups on CT images. On MRI, there were statistically significant differences between the MVI positive and MVI negative groups in peripheral enhancement of the arterial phase (AP) and peripheral low signal in the hepatobiliary phase (HBP). In the radiomics analysis, the imaging features extracted from the AP had strong predictive power in both groups (CT and MRI). For the phase images, 15 and 12 valuable features from CT and MRI were selected to develop the radiomics signature, respectively. The AUCs of the training set were 0.965 (sensitivity: 0.979; specificity: 0.931; precision: 0.939) and 0.962 (sensitivity: 0.963; specificity: 0.897; precision: 0.923) , the validation set were 0.842 (sensitivity: 0.967; specificity: 0.733; precision: 0.714) and 0.769 (sensitivity: 0.846; specificity: 0.727; precision: 0.727). The PVP also performed well on CT (AUC: 0.851/0.891) and MRI (AUC: 0.886/0.846). The predictive power was not enhanced by combining the features of multi-phase images.
Conclusions: This was a controlled study on preoperative CT and MRI imaging and radiomics based on a seven-point pathological sampling method can avoid false-negative results caused by traditional pathological sampling. The imaging analysis results obtained and the radiomics prediction model established in this study may be more accurate than conventional models.