Utility of Ultrasound-Guided Attenuation Parameter for Grading Steatosis With Reference to MRI-PDFF in a Large Cohort
Authors: Kento Imajo, Hidenori Toyoda, Satoshi Yasuda, Yasuaki Suzuki, Katsutoshi Sugimoto, Hidekatsu Kuroda, Tomoyuki Akita, Junko Tanaka, Yutaka Yasui, Nobuharu Tamaki, Masayuki Kurosaki, Namiki Izumi, Atsushi Nakajima, Takashi Kumada
Journal: Clinical Gastroenterology and Hepatology (2021)

Background & AimsUltrasound-guided attenuation parameter (UGAP) is recently developed for noninvasive evaluation of steatosis. However, reports on its usefulness in clinical practice are limited. This prospective multicenter study analyzed the diagnostic accuracy of grading steatosis with reference to magnetic resonance imaging–based proton density fat fraction (MRI-PDFF), a noninvasive method with high accuracy, in a large cohort.MethodsAltogether, 1010 patients with chronic liver disease who underwent MRI-PDFF and UGAP were recruited and prospectively enrolled from 6 Japanese liver centers. Linearity was evaluated using intraclass correlation coefficients between MRI-PDFF and UGAP values. Bias, defined as the mean difference between MRI-PDFF and UGAP values, was assessed by Bland-Altman analysis. UGAP cutoffs for pairwise MRI-PDFF-based steatosis grade were determined using area under the receiver-operating characteristic curve (AUROC) analyses.ResultsUGAP values were shown to be normally distributed. However, because PDFF values were not normally distributed, they were log-transformed (MRI-logPDFF). UGAP values significantly correlated with MRI-logPDFF (intraclass correlation coefficient = 0.768). Additionally, Bland-Altman analysis showed good agreement between MRI-logPDFF and UGAP with a mean bias of 0.0002% and a narrow range of agreement (95% confidence interval [CI], –0.015 to 0.015). The AUROCs for distinguishing steatosis grade ≥1 (MRI-PDFF ≥5.2%), ≥2 (MRI-PDFF ≥11.3%), and 3 (MRI-PDFF ≥17.1%) were 0.910 (95% CI, 0.891–0.928), 0.912 (95% CI, 0.894–0.929), and 0.894 (95% CI, 0.873–0.916), respectively.ConclusionsUGAP has excellent diagnostic accuracy for grading steatosis with reference to MRI-PDFF. Additionally, UGAP has good linearity and negligible bias, suggesting that UGAP has excellent technical performance characteristics that can be widely used in clinical trials and patient care. (UMIN Clinical Trials Registry, Number: UMIN000041196). Ultrasound-guided attenuation parameter (UGAP) is recently developed for noninvasive evaluation of steatosis. However, reports on its usefulness in clinical practice are limited. This prospective multicenter study analyzed the diagnostic accuracy of grading steatosis with reference to magnetic resonance imaging–based proton density fat fraction (MRI-PDFF), a noninvasive method with high accuracy, in a large cohort. Altogether, 1010 patients with chronic liver disease who underwent MRI-PDFF and UGAP were recruited and prospectively enrolled from 6 Japanese liver centers. Linearity was evaluated using intraclass correlation coefficients between MRI-PDFF and UGAP values. Bias, defined as the mean difference between MRI-PDFF and UGAP values, was assessed by Bland-Altman analysis. UGAP cutoffs for pairwise MRI-PDFF-based steatosis grade were determined using area under the receiver-operating characteristic curve (AUROC) analyses. UGAP values were shown to be normally distributed. However, because PDFF values were not normally distributed, they were log-transformed (MRI-logPDFF). UGAP values significantly correlated with MRI-logPDFF (intraclass correlation coefficient = 0.768). Additionally, Bland-Altman analysis showed good agreement between MRI-logPDFF and UGAP with a mean bias of 0.0002% and a narrow range of agreement (95% confidence interval [CI], –0.015 to 0.015). The AUROCs for distinguishing steatosis grade ≥1 (MRI-PDFF ≥5.2%), ≥2 (MRI-PDFF ≥11.3%), and 3 (MRI-PDFF ≥17.1%) were 0.910 (95% CI, 0.891–0.928), 0.912 (95% CI, 0.894–0.929), and 0.894 (95% CI, 0.873–0.916), respectively. UGAP has excellent diagnostic accuracy for grading steatosis with reference to MRI-PDFF. Additionally, UGAP has good linearity and negligible bias, suggesting that UGAP has excellent technical performance characteristics that can be widely used in clinical trials and patient care. (UMIN Clinical Trials Registry, Number: UMIN000041196).