Literature DB >> 31687918

Liver Fat Content Measurement with Quantitative CT Validated against MRI Proton Density Fat Fraction: A Prospective Study of 400 Healthy Volunteers.

Zhe Guo1, Glen M Blake1, Kai Li1, Wei Liang1, Wei Zhang1, Yong Zhang1, Li Xu1, Ling Wang1, J Keenan Brown1, Xiaoguang Cheng1, Perry J Pickhardt1.   

Abstract

Background Although chemical shift-encoded (CSE) MRI proton density fat fraction (PDFF) is the current noninvasive reference standard for liver fat quantification, the liver is more frequently imaged with CT. Purpose To validate quantitative CT measurements of liver fat against the MRI PDFF reference standard. Materials and Methods In this prospective study, 400 healthy participants were recruited between August 2015 and July 2016. Each participant underwent same-day abdominal unenhanced quantitative CT with a calibration phantom and CSE 3.0-T MRI. CSE MRI liver fat measurements were used to calibrate an equation to adjust CT fat measurements and put them on the PDFF measurement scale. CT and PDFF liver fat measurements were plotted as histograms, medians, and interquartile ranges compared; scatterplots and Bland-Altman plots obtained; and Pearson correlation coefficients calculated. Receiver operating characteristic curves including areas under the curve were evaluated for mild (PDFF, 5%) and moderate (PDFF, 14%) steatosis thresholds for both raw and adjusted CT measurements. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated. Results Four hundred volunteers (mean age, 52.6 years ± 15.2; 227 women) were evaluated. MRI PDFF measurements of liver fat ranged between 0% and 28%, with 41.5% (166 of 400) of participants with PDFF greater than 5%. Both raw and adjusted quantitative CT values correlated well with MRI PDFF (r2 = 0.79; P < .001). Bland-Altman analysis of adjusted CT values showed no slope or bias. Both raw and adjusted CT had areas under the receiver operating characteristic curve of 0.87 and 0.99, respectively, to identify participants with mild (PDFF, >5%) and moderate (PDFF, >14%) steatosis, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value for unadjusted CT was 75.9% (126 of 166), 85.0% (199 of 234), 78.3% (126 of 161), and 83.3% (199 of 239), respectively, for PDFF greater than 5%; and 84.8% (28 of 33), 98.4% (361 of 367), 82.4% (28 of 34), and 98.6% (361 of 366), respectively, for PDFF greater than 14%. Results for adjusted CT were mostly identical. Conclusion Quantitative CT liver fat exhibited good correlation and accuracy with proton density fat fraction measured with chemical shift-encoded MRI. © RSNA, 2019 Online supplemental material is available for this article.

Entities:  

Mesh:

Year:  2019        PMID: 31687918     DOI: 10.1148/radiol.2019190467

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  15 in total

1.  Correlation Between Bone Mineral Density (BMD) and Paraspinal Muscle Fat Infiltration Based on QCT: A Cross-Sectional Study.

Authors:  Xiangwen Li; Yuyang Zhang; Yuxue Xie; Rong Lu; Hongyue Tao; Shuang Chen
Journal:  Calcif Tissue Int       Date:  2022-01-10       Impact factor: 4.333

2.  Relationship between oseteoporosis with fatty infiltration of paraspinal muscles based on QCT examination.

Authors:  Xiangwen Li; Yuxue Xie; Rong Lu; Yuyang Zhang; Hongyue Tao; Shuang Chen
Journal:  J Bone Miner Metab       Date:  2022-03-03       Impact factor: 2.626

Review 3.  Quantitative dual-energy CT techniques in the abdomen.

Authors:  Giuseppe V Toia; Achille Mileto; Carolyn L Wang; Dushyant V Sahani
Journal:  Abdom Radiol (NY)       Date:  2021-09-01

Review 4.  Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease.

Authors:  Jérémy Dana; Aïna Venkatasamy; Antonio Saviano; Joachim Lupberger; Yujin Hoshida; Valérie Vilgrain; Pierre Nahon; Caroline Reinhold; Benoit Gallix; Thomas F Baumert
Journal:  Hepatol Int       Date:  2022-02-09       Impact factor: 9.029

5.  S2FLNet: Hepatic steatosis detection network with body shape.

Authors:  Qiyue Wang; Wu Xue; Xiaoke Zhang; Fang Jin; James Hahn
Journal:  Comput Biol Med       Date:  2021-11-30       Impact factor: 6.698

Review 6.  Dual-energy CT in diffuse liver disease: is there a role?

Authors:  Khaled Y Elbanna; Bahar Mansoori; Achille Mileto; Patrik Rogalla; Luís S Guimarães
Journal:  Abdom Radiol (NY)       Date:  2020-08-08

Review 7.  Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value.

Authors:  Perry J Pickhardt; Peter M Graffy; Alberto A Perez; Meghan G Lubner; Daniel C Elton; Ronald M Summers
Journal:  Radiographics       Date:  2021 Mar-Apr       Impact factor: 5.333

8.  Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly.

Authors:  Alberto A Perez; Victoria Noe-Kim; Meghan G Lubner; Peter M Graffy; John W Garrett; Daniel C Elton; Ronald M Summers; Perry J Pickhardt
Journal:  Radiology       Date:  2021-10-26       Impact factor: 11.105

9.  Influence of Radiation Dose and Reconstruction Kernel on Fat Fraction Analysis in Dual-energy CT: A Phantom Study.

Authors:  Vasiliki Chatzaraki; Corinna Born; Rahel A Kubik-Huch; Johannes M Froehlich; Michael J Thali; Tilo Niemann
Journal:  In Vivo       Date:  2021 Nov-Dec       Impact factor: 2.155

10.  Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom.

Authors:  Ruiyang Zhao; Diego Hernando; David T Harris; Louis A Hinshaw; Ke Li; Lakshmi Ananthakrishnan; Mustafa R Bashir; Xinhui Duan; Mounes Aliyari Ghasabeh; Ihab R Kamel; Carolyn Lowry; Mahadevappa Mahesh; Daniele Marin; Jessica Miller; Perry J Pickhardt; Jean Shaffer; Takeshi Yokoo; Jean H Brittain; Scott B Reeder
Journal:  Med Phys       Date:  2021-07-09       Impact factor: 4.071

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.