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Evaluation of Artificial Intelligence-Calculated Hepatorenal Index for Diagnosing Mild and Moderate Hepatic Steatosis in Non-Alcoholic Fatty Liver Disease. Medicina (B Aires) 2023; 59:medicina59030469. [PMID: 36984470 PMCID: PMC10058464 DOI: 10.3390/medicina59030469] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/12/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
Background and Objectives: This study aims to evaluate artificial intelligence-calculated hepatorenal index (AI-HRI) as a diagnostic method for hepatic steatosis. Materials and Methods: We prospectively enrolled 102 patients with clinically suspected non-alcoholic fatty liver disease (NAFLD). All patients had a quantitative ultrasound (QUS), including AI-HRI, ultrasound attenuation coefficient (AC,) and ultrasound backscatter-distribution coefficient (SC) measurements. The ultrasonographic fatty liver indicator (US-FLI) score was also calculated. The magnetic resonance imaging fat fraction (MRI-PDFF) was the reference to classify patients into four grades of steatosis: none < 5%, mild 5–10%, moderate 10–20%, and severe ≥ 20%. We compared AI-HRI between steatosis grades and calculated Spearman’s correlation (rs) between the methods. We determined the agreement between AI-HRI by two examiners using the intraclass correlation coefficient (ICC) of 68 cases. We performed a receiver operating characteristics (ROC) analysis to estimate the area under the curve (AUC) for AI-HRI. Results: The mean AI-HRI was 2.27 (standard deviation, ±0.96) in the patient cohort. The AI-HRI was significantly different between groups without (1.480 ± 0.607, p < 0.003) and with mild steatosis (2.155 ± 0.776), as well as between mild and moderate steatosis (2.777 ± 0.923, p < 0.018). AI-HRI showed moderate correlation with AC (rs = 0.597), SC (rs = 0.473), US-FLI (rs = 0.5), and MRI-PDFF (rs = 0.528). The agreement in AI-HRI was good between the two examiners (ICC = 0.635, 95% confidence interval (CI) = 0.411–0.774, p < 0.001). The AI-HRI could detect mild steatosis (AUC = 0.758, 95% CI = 0.621–0.894) with fair and moderate/severe steatosis (AUC = 0.803, 95% CI = 0.721–0.885) with good accuracy. However, the performance of AI-HRI was not significantly different (p < 0.578) between the two diagnostic tasks. Conclusions: AI-HRI is an easy-to-use, reproducible, and accurate QUS method for diagnosing mild and moderate hepatic steatosis.
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Rónaszéki AD, Budai BK, Csongrády B, Stollmayer R, Hagymási K, Werling K, Fodor T, Folhoffer A, Kalina I, Győri G, Maurovich-Horvat P, Kaposi PN. Tissue attenuation imaging and tissue scatter imaging for quantitative ultrasound evaluation of hepatic steatosis. Medicine (Baltimore) 2022; 101:e29708. [PMID: 35984128 PMCID: PMC9387959 DOI: 10.1097/md.0000000000029708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
We aimed to assess the feasibility of ultrasound-based tissue attenuation imaging (TAI) and tissue scatter distribution imaging (TSI) for quantification of liver steatosis in patients with nonalcoholic fatty liver disease (NAFLD). We prospectively enrolled 101 participants with suspected NAFLD. The TAI and TSI measurements of the liver were performed with a Samsung RS85 Prestige ultrasound system. Based on the magnetic resonance imaging proton density fat fraction (MRI-PDFF), patients were divided into ≤5%, 5-10%, and ≥10% of MRI-PDFF groups. We determined the correlation between TAI, TSI, and MRI-PDFF and used multiple linear regression analysis to identify any association with clinical variables. The diagnostic performance of TAI, TSI was determined based on the area under the receiver operating characteristic curve (AUC). The intraclass correlation coefficient (ICC) was calculated to assess interobserver reliability. Both TAI (rs = 0.78, P < .001) and TSI (rs = 0.68, P < .001) showed significant correlation with MRI-PDFF. TAI overperformed TSI in the detection of both ≥5% MRI-PDFF (AUC = 0.89 vs 0.87) and ≥10% (AUC = 0.93 vs 0.86). MRI-PDFF proved to be an independent predictor of TAI (β = 1.03; P < .001), while both MRI-PDFF (β = 50.9; P < .001) and liver stiffness (β = -0.86; P < .001) were independent predictors of TSI. Interobserver analysis showed excellent reproducibility of TAI (ICC = 0.95) and moderate reproducibility of TSI (ICC = 0.73). TAI and TSI could be used successfully to diagnose and estimate the severity of hepatic steatosis in routine clinical practice.
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Affiliation(s)
- Aladár D. Rónaszéki
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- *Correspondence: Aladár D. Rónaszéki, MD, Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Korányi Sándor str. 2., H-1082 Budapest, Hungary (e-mail: )
| | - Bettina K. Budai
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Barbara Csongrády
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Róbert Stollmayer
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Krisztina Hagymási
- Department of Surgery, Transplantation and Gastroenterology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Klára Werling
- Department of Surgery, Transplantation and Gastroenterology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Tamás Fodor
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Anikó Folhoffer
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Ildikó Kalina
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Gabriella Győri
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Pál Maurovich-Horvat
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Pál N. Kaposi
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
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