Literature DB >> 27089026

Liver Surface Nodularity Quantification from Routine CT Images as a Biomarker for Detection and Evaluation of Cirrhosis.

Andrew D Smith1, Cody R Branch1, Kevin Zand1, Charu Subramony1, Haowei Zhang1, Katherine Thaggard1, Richard Hosch1, Jason Bryan1, Amit Vasanji1, Michael Griswold1, Xu Zhang1.   

Abstract

Purpose To determine the accuracy, reproducibility, and intra- and interobserver agreement of a computer-based quantitative method to measure liver surface nodularity (LSN) from routine computed tomographic (CT) images as a biomarker for detection and evaluation of cirrhosis. Materials and Methods For this institutional review board-approved HIPAA-compliant retrospective study, adult patients with healthy livers (n = 24) or various stages of hepatitis C virus-induced chronic liver disease (n = 70) with routine nonenhanced and portal venous phase contrast agent-enhanced liver CT imaging with thick-section (5.0 mm) and thin-section (1.25-1.50 mm) axial images obtained between January 1, 2006, and March 31, 2011, were identified from the electronic medical records. A computer algorithm was developed to measure LSN and derive a score. LSN scores, splenic volume, and the ratio of left lateral segment (LLS) to total liver volume (TLV) were measured from the same multiphasic liver CT examinations. Accuracy for differentiating cirrhotic from noncirrhotic livers was assessed by area under the receiver operating characteristic curve. Intra- and interobserver agreement was assessed by intraclass correlation coefficient. Results Median LSN scores from nonenhanced thick-section CT images in cirrhotic livers (3.16; 56 livers) were significantly higher than in noncirrhotic livers (2.11; 38 livers; P < .001). LSN scores from the four CT imaging types (94 patients for each type) were very strongly correlated (range of Spearman r, 0.929-0.960). LSN scores from portal venous phase contrast-enhanced thick-section CT images had significantly higher accuracy (area under the receiver operating characteristic curve, 0.929) than splenic volume (area under the receiver operating characteristic curve, 0.835) or LLS-to-TLV ratio measurements (area under the receiver operating characteristic curve, 0.753) for differentiating cirrhotic from noncirrhotic livers (P = .038 and .003, respectively; n = 94). Intra- and interobserver agreements that used nonenhanced thick CT images were very good (intraclass correlation coefficient, 0.963 and 0.899, respectively). Conclusion Quantitative measurement of LSN on routine CT images accurately differentiated cirrhotic from noncirrhotic livers and was highly reproducible. (©) RSNA, 2016 Online supplemental material is available for this article.

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Year:  2016        PMID: 27089026     DOI: 10.1148/radiol.2016151542

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


  32 in total

1.  CT-based liver surface nodularity for the detection of clinically significant portal hypertension: defining measurement quality criteria.

Authors:  Riccardo Sartoris; Marie Lazareth; Arianna Nivolli; Marco Dioguardi Burgio; Valérie Vilgrain; Maxime Ronot
Journal:  Abdom Radiol (NY)       Date:  2020-09

Review 2.  Imaging of Hepatic Fibrosis.

Authors:  Rishi Philip Mathew; Sudhakar Kundapur Venkatesh
Journal:  Curr Gastroenterol Rep       Date:  2018-08-29

3.  Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features.

Authors:  Enming Cui; Wansheng Long; Juanhua Wu; Qing Li; Changyi Ma; Yi Lei; Fan Lin
Journal:  Abdom Radiol (NY)       Date:  2021-03-22

4.  Multiparametric CT for Noninvasive Staging of Hepatitis C Virus-Related Liver Fibrosis: Correlation With the Histopathologic Fibrosis Score.

Authors:  Perry J Pickhardt; Peter M Graffy; Adnan Said; Daniel Jones; Brandon Welsh; Ryan Zea; Meghan G Lubner
Journal:  AJR Am J Roentgenol       Date:  2019-01-15       Impact factor: 3.959

Review 5.  Putting it all together: established and emerging MRI techniques for detecting and measuring liver fibrosis.

Authors:  Suraj D Serai; Andrew T Trout; Alexander Miethke; Eric Diaz; Stavra A Xanthakos; Jonathan R Dillman
Journal:  Pediatr Radiol       Date:  2018-08-04

6.  Diagnostic value of MRI-derived liver surface nodularity score for the non-invasive quantification of hepatic fibrosis in non-alcoholic fatty liver disease.

Authors:  Roberta Catania; Alessandro Furlan; Andrew D Smith; Jaideep Behari; Mitchell E Tublin; Amir A Borhani
Journal:  Eur Radiol       Date:  2020-08-05       Impact factor: 5.315

7.  Imaging Biomarkers of Hepatic Fibrosis: Reliability and Accuracy of Hepatic Periportal Space Widening and Other Morphologic Features on MRI.

Authors:  Daniel R Ludwig; Tyler J Fraum; David H Ballard; Vamsi R Narra; Anup S Shetty
Journal:  AJR Am J Roentgenol       Date:  2021-03-17       Impact factor: 3.959

8.  Combining hepatic surface nodularity and serum tests better predicts hepatic fibrosis stages in chronic liver disease.

Authors:  Hyo Jung Cho; Jaewon Choi; Bohyun Kim; JeongGil Ko; Joon-Il Choi; Jimi Huh; Jei Hee Lee; Jai Keun Kim
Journal:  Abdom Radiol (NY)       Date:  2021-05-12

9.  CT texture analysis of the liver for assessing hepatic fibrosis in patients with hepatitis C virus.

Authors:  Meghan G Lubner; Daniel Jones; John Kloke; Adnan Said; Perry J Pickhardt
Journal:  Br J Radiol       Date:  2018-10-11       Impact factor: 3.039

Review 10.  Fibrosis imaging: Current concepts and future directions.

Authors:  Maike Baues; Anshuman Dasgupta; Josef Ehling; Jai Prakash; Peter Boor; Frank Tacke; Fabian Kiessling; Twan Lammers
Journal:  Adv Drug Deliv Rev       Date:  2017-11-20       Impact factor: 15.470

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