University of Pittsburgh

A radiomics approach to microvascular invasion prediction in hepatocellular carcinoma from pre-operative multiphase MRI

graduate student
Friday, March 8, 2019 - 1:00pm - 1:30pm

Introduction: Micro vascular 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-phase MRI to predict micro vascular invasion before surgery.
Methods: We retrospectively gathered pre-operative multi-phasic MRI scans for 99 patients who were diagnosed with HCC and received surgery, thus allowing mVI diagnosis by pathological examination. We extracted radiomics features from the manually segmented HCC regions in each MRI sequence and we built Machine Learning classifiers to predict mVI. We investigated the use of features extracted from the tumor region only, the peritumoral marginal region only, and the combination of the two.
Results: By combining information extracted from different MRI sequences, we were able to achieve AUCs of 86.69%, 84.62%, and 84.19% when considering features extracted from the tumor only, the peritumoral region only, and the combination of the two, respectively.
Conclusions: Our results indicate that mVI prediction may be feasible from pre-operative MRI scans. In addition, information from different MRI sequences is complementary in identifying mVI. From our experiments, marginal information does not improve prediction, possibly because automatic computation of the margin may include extra-hepatic areas that introduce noise.

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