Radiomic Features at Contrast-Enhanced CT Predict Virus-Driven Liver Fibrosis: A Multi-Institutional Study

• Published in: Clinical and translational gastroenterology 2024-10, Vol.15 (10), p.e1
• Main Authors: Wang, Jincheng, Tang, Shengnan, Wu, Jin, Xu, Shanshan, Sun, Qikai, Zhou, Zheyu, Xu, Xiaoliang, Liu, Yang, Liu, Qiaoyu, Mao, Yingfan, He, Jian, Zhang, Xudong, Yin, Yin
• Format: Article
• Language: English
• Subjects: Abdomen; Adult; Aged; Biomarkers; Biopsy; Contrast Media; Female; Hepatitis B; Hepatitis C; Humans; Liver; Liver - diagnostic imaging; Liver - pathology; Liver cancer; Liver cirrhosis; Liver Cirrhosis - diagnostic imaging; Liver Cirrhosis - pathology; Liver Cirrhosis - virology; Liver diseases; Machine Learning; Magnetic resonance imaging; Male; Medical imaging; Middle Aged; Radiomics; Regression analysis; Reproducibility; Reproducibility of Results; Retrospective Studies; Spleen; Spleen - diagnostic imaging; Spleen - pathology; Tomography, X-Ray Computed
• ISSN: 2155-384X; 2155-384X
• DOI: 10.14309/ctg.0000000000000712

Liver fibrosis is a major cause of morbidity and mortality among in patients with chronic hepatitis. Radiomics, particularly of the spleen, may improve diagnostic accuracy and treatment strategies. External validations are necessary to ensure reliability and generalizability. In this retrospective study, we developed 3 radiomics models using contrast-enhanced computed tomography scans from 167 patients with liver fibrosis (training group) between January 2020 and December 2021. Radiomic features were extracted from arterial venous, portal venous, and equilibrium phase images. Recursive feature selection random forest and the least absolute shrinkage and selection operator logistic regression were used for feature selection and dimensionality reduction. Performance was assessed by area under the curve, C-index, calibration plots, and decision curve analysis. External validation was performed on 114 patients from 2 institutions. Twenty-five radiomic features were significantly associated with fibrosis stage, with 80% of the top 10 features originating from portal venous phase spleen images. The radiomics models showed good performance in the validation cohort (C-indices 0.723-0.808) and excellent calibration. Decision curve analysis indicated clinical benefits, with machine learning-based radiomics models (Random Forest score and support vector machine based radiomics score) providing more significant advantages. Radiomic features offer significant benefits over existing serum indices for staging virus-driven liver fibrosis, underscoring the value of radiomics in enhancing diagnostic accuracy. Specifically, radiomics analysis of the spleen presents additional noninvasive options for assessing fibrosis, highlighting its potential in improving patient management and outcomes.