Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm

• Published in: BMC gastroenterology Vol. 21; no. 1; pp. 155 - 10
• Main Authors: Zhong, Xi, Guan, Tianpei, Tang, Danrui, Li, Jiansheng, Lu, Bingui, Cui, Shuzhong, Tang, Hongsheng
• Format: Journal Article
• Language: English
• Published: London BioMed Central 07.04.2021; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Benign; Biopsy; Calibration; Carcinoma, Hepatocellular - complications; Carcinoma, Hepatocellular - diagnostic imaging; Carcinoma, Hepatocellular - pathology; Cirrhosis; Computer-aided medical diagnosis; Diagnosis; Diagnosis, Differential; Diffusion coefficient; Feature selection; Gastroenterology; Hepatocellular carcinoma; Hepatology; Hepatoma; Humans; Internal Medicine; Liver; Liver - diagnostic imaging; Liver - pathology; Liver cancer; Liver cirrhosis; Liver Cirrhosis - complications; Liver Cirrhosis - diagnostic imaging; Liver Cirrhosis - pathology; Liver Neoplasms - complications; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - pathology; Magnetic Resonance Imaging; Medicine; Medicine & Public Health; Metastasis; Methods; Nodules; Nomograms; Patients; Radiomics; Research Article; Retrospective Studies; Sensitivity and Specificity; Software; China; Hepatocellular carcinoma; Diagnosis; Magnetic resonance imaging; Liver cirrhosis
• ISSN: 1471-230X; 1471-230X
• DOI: 10.1186/s12876-021-01710-y

Background Accurate characterization of small nodules in a cirrhotic liver is challenging. We aimed to determine the additive value of MRI-based radiomics analysis to Liver Imaging Reporting and Data System version 2018 (LI-RADS v 2018) algorithm in differentiating small (≤ 3 cm) hepatocellular carcinomas (HCCs) from benign nodules in cirrhotic liver. Methods In this retrospective study, 150 cirrhosis patients with histopathologically confirmed small liver nodules (HCC, 112; benign nodules, 44) were evaluated from January 2013 to October 2018. Based on the LI-RADS algorithm, a LI-RADS category was assigned for each lesion. A radiomics signature was generated based on texture features extracted from T1-weighted, T2W, and apparent diffusion coefficient (ADC) images by using the least absolute shrinkage and selection operator regression model. A nomogram model was developed for the combined diagnosis. Diagnostic performance was assessed using receiver operating characteristic curve (ROC) analysis. Results A radiomics signature consisting of eight features was significantly associated with the differentiation of HCCs from benign nodules. Both LI-RADS algorithm (area under ROC [A z ] = 0.898) and the MRI-Based radiomics signature (A z  = 0.917) demonstrated good discrimination, and the nomogram model showed a superior classification performance (A z  = 0.975). Compared with LI-RADS alone, the combined approach significantly improved the specificity (97.7% vs 81.8%, p  = 0.030) and positive predictive value (99.1% vs 92.9%, p  = 0.031) and afforded comparable sensitivity (97.3% vs 93.8%, p  = 0.215) and negative predictive value (93.5% vs 83.7%, p  = 0.188). Conclusions MRI-based radiomics analysis showed additive value to the LI-RADS v 2018 algorithm for differentiating small HCCs from benign nodules in the cirrhotic liver.