Abstract
BackgroundAccurate 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.MethodsIn 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.ResultsA radiomics signature consisting of eight features was significantly associated with the differentiation of HCCs from benign nodules. Both LI-RADS algorithm (area under ROC [Az] = 0.898) and the MRI-Based radiomics signature (Az = 0.917) demonstrated good discrimination, and the nomogram model showed a superior classification performance (Az = 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).ConclusionsMRI-based radiomics analysis showed additive value to the LI-RADS v 2018 algorithm for differentiating small HCCs from benign nodules in the cirrhotic liver.
Highlights
Accurate characterization of small nodules in a cirrhotic liver is challenging
Patient characteristics Of the 150 patients, patients (74%) with nodules were diagnosed as having hepatocellular carcinoma (HCC), and 105 nodules were confirmed by resection, while 7 nodules were confirmed by aspiration biopsy
Performance of Magnetic resonance imaging (MRI)‐based radiomics analysis Of these 837 features, 301 features with interclass correlation coefficients (ICC) values ≥ 0.80 were selected for further reduction, of which 57 texture parameters with p values less than 0.05 by using Mann– Whitney U test remained for subsequent least absolute shrinkage and selection operator (LASSO) analysis, and these features measured by the two radiologists were averaged
Summary
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. Hepatocarcinogenesis in cirrhosis usually shows a multistep progression from benign nodules to small HCCs (≤ 3 cm), and. Accurate characterization of small HCCs and benign nodules is challenging due to the overlap of imaging features during the hepatocarcinogenesis process [1, 2]. LI-RADS reflects the relative probability of HCC development by assigning categories ranging from LR-1 to LR-5 (definitely HCC) or LR-TIV (definite tumor in vein) based on the presence of specific imaging features [5, 6]
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