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Early Differentiation of Irreversible Electroporation Ablation Regions With Radiomics Features of Conventional MRI

  • Author Footnotes
    † These authors contributed equally to this work.
    Aydin Eresen
    Footnotes
    † These authors contributed equally to this work.
    Affiliations
    Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois

    Department of Radiological Sciences, University of California Irvine, Irvine, California
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  • Author Footnotes
    † These authors contributed equally to this work.
    Chong Sun
    Footnotes
    † These authors contributed equally to this work.
    Affiliations
    Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois

    Department of Orthopedics, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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  • Kang Zhou
    Affiliations
    Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois

    Department of Radiology, Peking Union Medical College Hospital, Beijing, China
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  • Junjie Shangguan
    Affiliations
    Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
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  • Bin Wang
    Affiliations
    Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois

    Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
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  • Liang Pan
    Affiliations
    Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois

    Department of Radiology, Third Affiliated Hospital of Suzhou University, Changzhou, Jiangsu, China
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  • Su Hu
    Affiliations
    Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois

    Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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  • Quanhong Ma
    Affiliations
    Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois

    Department of Radiological Sciences, University of California Irvine, Irvine, California
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  • Jia Yang
    Affiliations
    Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
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  • Zhuoli Zhang
    Affiliations
    Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois

    Department of Radiological Sciences, University of California Irvine, Irvine, California

    Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, Illinois

    Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California
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  • Vahid Yaghmai
    Correspondence
    Address correspondence to: V.Y
    Affiliations
    Department of Radiological Sciences, University of California Irvine, Irvine, California

    Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California
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  • Author Footnotes
    † These authors contributed equally to this work.
Published:December 18, 2021DOI:https://doi.org/10.1016/j.acra.2021.11.020

      Rationale and Objectives

      Irreversible electroporation (IRE) is a promising non-thermal ablation technique for the treatment of patients with hepatocellular carcinoma. Early differentiation of the IRE zone from surrounding reversibly electroporated (RE) penumbra is vital for the evaluation of treatment response. In this study, an advanced statistical learning framework was developed by evaluating standard MRI data to differentiate IRE ablation zones, and to correlate with histological tumor biomarkers.

      Materials and Methods

      Fourteen rabbits with VX2 liver tumors were scanned following IRE ablation and forty-six features were extracted from T1w and T2w MRI. Following identification of key imaging variables through two-step feature analysis, multivariable classification and regression models were generated for differentiation of IRE ablation zones, and correlation with histological markers reflecting viable tumor cells, microvessel density, and apoptosis rate. The performance of the multivariable models was assessed by measuring accuracy, receiver operating characteristics curve analysis, and Spearman correlation coefficients.

      Results

      The classifiers integrating four radiomics features of T1w, T2w, and T1w+T2w MRI data distinguished IRE from RE zones with an accuracy of 97%, 80%, and 97%, respectively. Also, pixelwise classification models of T1w, T2w, and T1w+T2w MRI labeled each voxel with an accuracy of 82.8%, 66.5%, and 82.9%, respectively. Regression models obtained a strong correlation with behavior of viable tumor cells (0.62 ≤ r2 ≤ 0.85, p < 0.01), apoptosis (0.40 ≤ r2 ≤ 0.82, p < 0.01), and microvessel density (0.48 ≤ r2 ≤ 0.58, p < 0.01).

      Conclusion

      MRI radiomics features provide descriptive power for early differentiation of IRE and RE zones while observing strong correlations among multivariable MRI regression models and histological tumor biomarkers.

      Key Words

      Abbreviation:

      AUC (Area under the receiver operating characteristics curve), FDA (Food and Drug Administration), FOS (First order statistics), GLCM (Gray-level co-occurrence matrix), GLRLM (Gray-level run-length matrix), GLSZM (Gray-level size-zone matrix), HCC (Hepatocellular carcinoma), H&E (Hematoxylin-eosin), ICI (Immune checkpoint inhibitor), IRE (Irreversible electroporation), NGTDM (Neighborhood gray-tone difference matrix), RE (Reversible electroporated), RF (Random forest), RMSE (Root mean squared error), ROC (Receiver operating characteristics), ROI (Region of interest), TKI (tyrosine kinase inhibitor), TRIP MRI (Transcatheter intra-arterial perfusion MRI), TUNEL (Terminal deoxynucleotidyl transferase dUTP nick end labeling), VGEF (Anti-vascular endothelial growth factor)
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