Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC). (25th June 2022)

• Record Type: Journal Article
• Authors: Hu, Shuyi; Lyu, Xiajie; Li, Weifeng; Cui, Xiaohan; Liu, Qiaoyu; Xu, Xiaoliang; Wang, Jincheng; Chen, Lin; Zhang, Xudong; Yin, Yin
• Other Names: Teekaraman Yuvaraja Academic Editor.
• Is Part Of: Contrast media & molecular imaging. Volume 2022(2022)
• Publication Date: 2022-06-25
• Subjects: Diagnostic imaging -- Periodicals; Magnetic resonance imaging -- Periodicals; Contrast media (Diagnostic imaging) -- Periodicals; Contrast Media -- Periodicals; Diagnostic Imaging -- Periodicals; Substances de contraste -- Périodiques; Diagnostics moléculaires -- Périodiques; Imagerie médicale; Substance de contraste; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.0754
• DOI: 10.1155/2022/7693631
• Languages: English
• ISSNs: 1555-4309

Abstract

Abstract : Background . To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods . In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results . The radiomic signature we built including 5 radiomic features demonstrated significant differences between the hepatic HH and HCC groups P < 0.05 . The improved model demonstrated a higher net benefit based on only 2 radiomic features. In the validation group, radiomics signature and radiomics index achieved great diagnostic performance with AUC values of 0.716 (95% confidence interval (CI): 0.581, 0.850) and 0.870 (95% CI: 0.782, 0.957), respectively. Conclusions . Our developed radiomics-based model can successfully distinguish HH and HCC patients, which can help clinical decision-making with lower cost.