Aydin Eresen1, Chong Sun2, Kang Zhou3, Junjie Shangguan4, Bin Wang5, Liang Pan6, Su Hu7, Quanhong Ma1, Jia Yang4, Zhuoli Zhang8, Vahid Yaghmai9. 1. Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California. 2. Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Orthopedics, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China. 3. Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Peking Union Medical College Hospital, Beijing, China. 4. Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois. 5. 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. 6. Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Third Affiliated Hospital of Suzhou University, Changzhou, Jiangsu, China. 7. Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China. 8. 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. 9. Department of Radiological Sciences, University of California Irvine, Irvine, California; Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California. Electronic address: vyaghmai@hs.uci.edu.
Abstract
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.
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.