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

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.

Texture analysis of the liver at MDCT for assessing hepatic fibrosis

“…Another CT imaging biomarker that has shown promising in assessing stage of hepatic fibrosis is liver surface nodularity (LSN), which is a simple objective measurement that quantifies the amount of surface nodularity along a section of liver [13]. For significant fibrosis, LSN showed an ROC AUC value of 0.902, for advanced fibrosis 0.932, and for cirrhosis, 0.959 [14].…”

“…A variety of morphologic and volumetric changes in the liver and spleen have been evaluated on CT, recently including the liver segmental volume ratio (LSVR), splenic volume, and liver surface nodularity [13][14][15][16]. These measurements can be performed retrospectively without special equipment and have preliminarily shown performance similar to elastography techniques.…”

Detection of liver fibrosis using qualitative and quantitative MR elastography compared to liver surface nodularity measurement, gadoxetic acid uptake, and serum markers

“…10 Briefly, the user selects a circular ROI and paints along the edge of the liver surface where the liver is bordered by fat or ascites. Details of the LSN software and measurement technique have been published recently.…”

“…Despite these promising results, only a few studies have compared the diagnostic performance of mpMRI for liver fibrosis staging, showing greater performance of MRE against morphologic criteria 4,5 and DCE-MRI and DWI, 6,7 with improved detection of liver fibrosis and cirrhosis when using a multiparametric approach. 10 This LSN software has shown promising results for the detection of liver fibrosis 10,11 and prediction of hepatic decompensation, 12 mostly using computed tomography (CT). 4,5 A recent study introduced a novel noninvasive method of objectively quantifying liver surface nodularity (LSN) by applying semiautomated software.…”

Establishment of a method for calculating standard splenic volume and its use in the evaluation of functional hepatic reserve

“…Since the difference in the amount of this decrease between normal individuals and patients with cirrhosis was significant, the authors believe that the LV/SV ratio could serve as an indicator of liver fibrosis. Currently, with the development of various types of simulation software, Liver Volume (LV) and Splenic Volume (SV) can be accurately measured using Computed Tomography (CT) [2,3].…”