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

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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.

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  1. https://pyradiomics.readthedocs.io/en/latest/features.html, last accessed Jan. 7th, 2019

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Funding

This work was supported by National Institutes of Health (NIH)/National Cancer Institute (NCI) grants (#1R01CA193603, #3R01CA193603-03S1, and #1R01CA218405), a Radiological Society of North America (RSNA) Research Scholar Grant (#RSCH1530), an Amazon AWS Machine Learning Research Award, and a University of Pittsburgh Physicians (UPP) Academic Foundation Award.

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Author notes

  1. Giacomo Nebbia and Qian Zhang contributed equally to this work.

Authors and Affiliations

  1. Intelligent Systems Program, University of Pittsburgh, 3362 Fifth Ave, Rm. 130, Pittsburgh, PA, 15213, USA

    Giacomo Nebbia & Shandong Wu

  2. Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Qian Zhang

  3. Department of Radiology, University of Pittsburgh, 3362 Fifth Ave, Rm. 130, Pittsburgh, PA, 15213, USA

    Dooman Arefan & Shandong Wu

  4. Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, 374 Dianmian Road, Wuhua District, Kunming, 650101, Yunnan, China

    Xinxiang Zhao

  5. Department of Bioengineering, University of Pittsburgh, 3362 Fifth Ave, Rm. 130, Pittsburgh, PA, 15213, USA

    Shandong Wu

  6. Department of Biomedical Informatics, University of Pittsburgh, 3362 Fifth Ave, Rm. 130, Pittsburgh, PA, 15213, USA

    Shandong Wu

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  1. Giacomo Nebbia
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Correspondence to Xinxiang Zhao or Shandong Wu.

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Nebbia, G., Zhang, Q., Arefan, D. et al. Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI Radiomics. J Digit Imaging 33, 1376–1386 (2020). https://doi.org/10.1007/s10278-020-00353-x

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