Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T1‐Weighted Imaging: Comparison of Different Radiomics Models

• Published in: Journal of magnetic resonance imaging Vol. 53; no. 4; pp. 1080 - 1089
• Main Authors: Ni, Ming, Wang, Lili, Yu, Haiyang, Wen, Xiaoyi, Yang, Yinghua, Liu, Guangzhen, Hu, Yabin, Li, Zhiming
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2021; Wiley Subscription Services, Inc
• Subjects: Animal models; Bile; Biomarkers; Carbon tetrachloride; Cirrhosis; classifier model; Classifiers; Etiology; Feature extraction; Feature selection; Fibrosis; Field strength; Hepatocellular carcinoma; Image processing; Image segmentation; Liver; Liver cirrhosis; liver fibrosis; Magnetic resonance imaging; Model accuracy; Radiomics; Statistical analysis; Statistical tests; Support vector machines
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.27391

Background Liver fibrosis is a common process resulting from various etiologies. Sustained progression of liver fibrosis leads to cirrhosis, even hepatocellular carcinoma. Thus, noninvasive staging of liver fibrosis is of clinical importance. Radiomics is an emerging approach for staging liver fibrosis. However, the feature selection methods and classifier models are complicated, and may result in a discrepancy of diagnostic performance owing to different radiomics models. Purpose To identify the optimal feature selection and classifier methods for predicting liver fibrosis by using nonenhanced T1‐weighted imaging. Study Type Prospective. Animal Model Wistar rats, total 97. Field Strength/Sequence 3T, 3D T1‐weighted images with fast‐spoiled gradient echo (FSPGR). Assessment Liver fibrosis rats were induced via subcutaneous injection of a mixture of carbon tetrachloride. Rats in the control group were injected with saline. Segmentation and feature extraction were performed by 3D slicer and the image biomarker explorer (IBEX) software package. Data preprocessing, feature selection, model building, and model comparative evaluation were conducted with Python. The liver fibrosis stage was determined by pathological examination. Statistical Tests Receiver operating characteristic curve, fuzzy comprehensive evaluation. Results For discriminating between F0 and F1‐2, F0 and F3‐4, F0 and F1‐4, F0‐1 and F2‐4, F0‐2 and F3‐4, and F0‐3 and F4, the accuracies of 12 radiomics models were 77.27–90.91%, 73.33–86.67%, 80.56–91.67%, 74.07–88.89%, 76.47–88.24%, and 79.49–92.31%, respectively. The AUCs of the radiomics models were 0.86–0.97, 0.85–0.95, 0.89–0.97, 0.81–0.96, 0.82–0.93, and 0.85–0.96, respectively. The least absolute shrinkage and selection operator / support vector machine (LASSO‐SVM) model had high AUCs of 0.93–0.97. For discriminating between F0 and F1‐2, F0 and F3‐4, F0 and F1‐4, F0‐1 and F2‐4, and F0‐2 and F3‐4, the fuzzy comprehensive evaluation showed that the LASSO‐SVM model had a high fuzzy score/order of 0.087–0.091/1. Data Conclusion LASSO‐SVM appears to be the optimal model for predicting liver fibrosis by using nonenhanced T1‐weighted imaging in a rodent model of liver fibrosis. Level of Evidence 2. Technical Efficacy Stage 2.