Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI Radiomics

Abstract

Microvascular invasion (mVI) is the most significant independent predictor of recurrence for hepatocellular carcinoma (HCC), but its pre-operative assessment is challenging. In this study, we investigate the use of multi-parametric MRI radiomics to predict mVI status before surgery. We retrospectively collected pre-operative multi-parametric liver MRI scans for 99 patients who were diagnosed with HCC. These patients received surgery and pathology-confirmed diagnosis of mVI. We extracted radiomics features from manually segmented HCC regions and built machine learning classifiers to predict mVI status. We compared the performance of such classifiers when built on five MRI sequences used both individually and combined. We investigated the effects of using features extracted from the tumor region only, the peritumoral marginal region only, and the combination of the two. We used the area under the receiver operating characteristic curve (AUC) and accuracy as performance metrics. By combining features extracted from multiple MRI sequences, AUCs are 86.69%, 84.62%, and 84.19% when features are extracted from the tumor only, the peritumoral region only, and the combination of the two, respectively. For tumor-extracted features, the T2 sequence (AUC = 80.84%) and portal venous sequence (AUC = 79.22%) outperform other MRI sequences in single-sequence-based models and their combination yields the highest AUC of 86.69% for mVI status prediction. Our results show promise in predicting mVI from pre-operative liver MRI scans and indicate that information from multi-parametric MRI sequences is complementary in identifying mVI.

Keywords: Hepatocellular carcinoma; MRI; Machine learning; Microvascular invasion; Radiomics.

Fig. 1

Examples of tumor and margin segmentation in five MRI sequences

Fig. 2

Margins we automatically identified for two different tumors of 111 mm of diameter (left) and of 17 mm of diameter (right)

Fig. 3

Sample T2 scans for patients with the highest value of feature #7. Left: diameter: 130 mm, center: diameter: 138 mm, right: diameter: 138 mm