Perry J Pickhardt1, Kyle Malecki1, John Kloke1, Meghan G Lubner1. 1. 1 Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252.
Abstract
OBJECTIVE: The purpose of this study was to investigate objective semiautomated measurement of liver surface nodularity on MDCT for prediction of underlying hepatic fibrosis (stages F0-F4). MATERIALS AND METHODS: Contrast-enhanced abdominal MDCT scans were assessed with an independently validated semiautomated surface nodularity tool. A series of 10 or more consecutive ROI measurements along the anterior aspect of the liver totaling a length of 80 cm or more were made with the left lateral segment as the default. All intermediate stages of fibrosis (F1-F3) were based on liver biopsy results within 1 year of MDCT. RESULTS: The study participants were 367 patients (191 men, 176 women; mean age, 51.1 years) divided into a healthy (F0) control group (n = 118) and patients with fibrosis in stages F1 (n = 47), F2 (n = 38), F3 (n = 67), and F4, which constituted cirrhosis (n = 97). MDCT-based liver surface nodularity scores increased with stage of fibrosis: F0, 2.01 ± 0.28; F1, 2.34 ± 0.39; F2, 2.37 ± 0.39; F3, 2.88 ± 0.68; and F4, 4.11 ± 0.95. For discriminating significant fibrosis (≥ F2), advanced fibrosis (≥ F3), and cirrhosis (≥ F4), the ROC AUCs were 0.902, 0.932, and 0.959, respectively. The sensitivity and specificity for significant fibrosis (≥ F2; liver surface nodularity threshold, 2.38) were 80.2% and 80.0%, for advanced fibrosis (≥ F3; liver surface nodularity threshold, 2.53) were 89.0% and 84.2%, and for cirrhosis (≥ F4; liver surface nodularity threshold, 2.81) were 97.9% and 84.8%. CONCLUSION: Objective quantification of liver surface nodularity at MDCT allows accurate discrimination between stages of hepatic fibrosis, especially at more advanced levels. Although the results are comparable to those of elastography, this simple semiautomated biomarker can be measured retrospectively without additional equipment or patient time.
OBJECTIVE: The purpose of this study was to investigate objective semiautomated measurement of liver surface nodularity on MDCT for prediction of underlying hepatic fibrosis (stages F0-F4). MATERIALS AND METHODS: Contrast-enhanced abdominal MDCT scans were assessed with an independently validated semiautomated surface nodularity tool. A series of 10 or more consecutive ROI measurements along the anterior aspect of the liver totaling a length of 80 cm or more were made with the left lateral segment as the default. All intermediate stages of fibrosis (F1-F3) were based on liver biopsy results within 1 year of MDCT. RESULTS: The study participants were 367 patients (191 men, 176 women; mean age, 51.1 years) divided into a healthy (F0) control group (n = 118) and patients with fibrosis in stages F1 (n = 47), F2 (n = 38), F3 (n = 67), and F4, which constituted cirrhosis (n = 97). MDCT-based liver surface nodularity scores increased with stage of fibrosis: F0, 2.01 ± 0.28; F1, 2.34 ± 0.39; F2, 2.37 ± 0.39; F3, 2.88 ± 0.68; and F4, 4.11 ± 0.95. For discriminating significant fibrosis (≥ F2), advanced fibrosis (≥ F3), and cirrhosis (≥ F4), the ROC AUCs were 0.902, 0.932, and 0.959, respectively. The sensitivity and specificity for significant fibrosis (≥ F2; liver surface nodularity threshold, 2.38) were 80.2% and 80.0%, for advanced fibrosis (≥ F3; liver surface nodularity threshold, 2.53) were 89.0% and 84.2%, and for cirrhosis (≥ F4; liver surface nodularity threshold, 2.81) were 97.9% and 84.8%. CONCLUSION: Objective quantification of liver surface nodularity at MDCT allows accurate discrimination between stages of hepatic fibrosis, especially at more advanced levels. Although the results are comparable to those of elastography, this simple semiautomated biomarker can be measured retrospectively without additional equipment or patient time.
Authors: Perry J Pickhardt; Ronald M Summers; Sungwon Lee; Daniel C Elton; Alexander H Yang; Christopher Koh; David E Kleiner; Meghan G Lubner Journal: Radiol Artif Intell Date: 2022-08-24
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
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
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