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)To read this article in full you will need to make a payment
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Article Info
Publication History
Published online: December 18, 2021
Accepted:
November 22,
2021
Received in revised form:
November 19,
2021
Received:
September 8,
2021
Identification
Copyright
© 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
