Dell Tatjana, Mesropyan Narine, Layer Yannik, Tischler Verena, Weinhold Leonie, Chang Johannes, Jansen Christian, Schmidt Bernhard, Jürgens Markus, Isaak Alexander, Kupczyk Patrick, Pieper Claus Christian, Meyer Carsten, Luetkens Julian, Kuetting Daniel
Department of Diagnostic and Interventional Radiology and Quantitative Imaging Lab Bonn, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
Institute of Pathology, University Hospital Bonn, Bonn, Germany.
Radiology. 2025 Mar;314(3):e241677. doi: 10.1148/radiol.241677.
Background Steatosis is a critical health problem, creating a growing need for opportunistic screening. Early detection may allow for effective treatment and prevention of further liver complications. Purpose To evaluate photon-counting CT (PCCT) fat quantification on contrast-enhanced scans and validate the results against fat quantification via histopathologic assessment, controlled attenuation parameter (CAP) from transient elastography, and MRI proton density fat fraction (PDFF). Materials and Methods In this prospective, observational clinical study, PCCT-derived fat fraction quantification was assessed in participants with known or suspected liver disease. Participants underwent PCCT between February 2022 and January 2024. Participants also underwent biopsy, US with CAP measurement, or MRI with a PDFF sequence for hepatic fat fraction quantification. Liver fat fraction was measured on virtual noncontrast PCCT images using spectral processing software with a three-material decomposition algorithm for fat, liver tissue, and iodine. Steatosis was graded for each modality. Correlation between PCCT-based steatosis grades and biopsy- and CAP-based grades was assessed with the Spearman correlation coefficient. Agreement between PCCT and MRI PDFF measurements was assessed with the intraclass correlation coefficient. Receiver operating characteristic curve analysis was conducted to determine the optimal PCCT fat fraction threshold for distinguishing between participants with and those without steatosis. Results The study included 178 participants, of whom 27 (mean age, 60.7 years ± 15.2 [SD]; 18 male participants) underwent liver biopsy, 26 (mean age, 60.0 years ± 18.3; 15 male participants) underwent CAP measurement, and 125 (mean age, 61.2 years ± 13.1; 70 male participants) underwent MRI PDFF measurement. There was excellent agreement between PCCT and MRI PDFF assessment of liver fat fraction (intraclass correlation coefficient, 0.91 [95% CI: 0.87, 0.94]). In stratified analysis, the intraclass correlation coefficient was 0.84 (95% CI: 0.63, 0.93) in participants with known fibrosis and 0.92 (95% CI: 0.88, 0.94) in participants without fibrosis. There was moderate correlation of PCCT-based steatosis grade with histologic (ρ = 0.65) and CAP-based (ρ = 0.45) steatosis grade. Based on the Youden index, the PCCT fat fraction threshold that best discriminated between participants with and those without steatosis was 4.8%, with a maximum achievable sensitivity of 81% (38 of 47) and a specificity of 71% (55 of 78). Conclusion PCCT in a standard clinical setting allowed for accurate estimation of liver fat fraction compared with MRI PDFF-based reference standard measurements. © RSNA, 2025 See also the editorial by Kartalis and Grigoriadis in this issue.
脂肪变性是一个严重的健康问题,对机会性筛查的需求日益增加。早期检测有助于有效治疗并预防进一步的肝脏并发症。目的:评估对比增强扫描时的光子计数CT(PCCT)脂肪定量,并通过组织病理学评估、瞬时弹性成像的控制衰减参数(CAP)以及MRI质子密度脂肪分数(PDFF)验证结果。材料与方法:在这项前瞻性观察性临床研究中,对已知或疑似肝病的参与者进行了PCCT衍生的脂肪分数定量评估。参与者于2022年2月至2024年1月期间接受了PCCT检查。参与者还接受了活检、测量CAP的超声检查或带有PDFF序列的MRI检查以进行肝脏脂肪分数定量。使用具有脂肪、肝组织和碘的三物质分解算法的光谱处理软件,在虚拟平扫PCCT图像上测量肝脏脂肪分数。对每种检查方法的脂肪变性进行分级。采用Spearman相关系数评估基于PCCT的脂肪变性分级与基于活检和CAP的分级之间的相关性。采用组内相关系数评估PCCT与MRI PDFF测量结果之间的一致性。进行受试者操作特征曲线分析,以确定区分有脂肪变性和无脂肪变性参与者的最佳PCCT脂肪分数阈值。结果:该研究纳入了178名参与者,其中27名(平均年龄60.7岁±15.2[标准差];18名男性参与者)接受了肝脏活检,26名(平均年龄60.0岁±18.3;15名男性参与者)进行了CAP测量,125名(平均年龄61.2岁±13.1;70名男性参与者)进行了MRI PDFF测量。PCCT与MRI PDFF对肝脏脂肪分数的评估具有极好的一致性(组内相关系数为0.91[95%CI:0.87,0.94])。在分层分析中,已知有纤维化的参与者组内相关系数为0.84(95%CI:0.63,0.93),无纤维化的参与者组内相关系数为0.92(95%CI:0.88,0.94)。基于PCCT的脂肪变性分级与组织学(ρ = 0.65)和基于CAP的(ρ = 0.45)脂肪变性分级存在中度相关性。基于约登指数,区分有脂肪变性和无脂肪变性参与者的最佳PCCT脂肪分数阈值为4.8%,最大可实现敏感性为81%(47名中的38名),特异性为71%(78名中的55名)。结论:在标准临床环境中,与基于MRI PDFF的参考标准测量相比,PCCT能够准确估计肝脏脂肪分数。© RSNA, 2025 另见本期Kartalis和Grigoriadis的社论。