Abstract
Background: To establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images. Materials and Methods: This retrospective study developed two radiomics-based models (R-score: radiomics signature; R-fibrosis: integrate radiomic and serum variables) in a training cohort of 332 patients (median age, 59 years; interquartile range, 51-67 years; 256 men) with biopsy-proven liver fibrosis who underwent contrast-enhanced CT between January 2017 and December 2020. Radiomic features were extracted from non-contrast, arterial and portal phase CT images and selected by using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3-F4 from stage F0-F2. Optimal cutoffs to diagnose significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4) and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis. An internal validation was conducted in 111 randomly assigned patients (median age, 58 years; interquartile range, 49-66 years; 89 men). Results: In the validation cohort, R-score and R-fibrosis (Obuchowski index, 0.843 and 0.846, respectively) significantly outperformed aspartate transaminase-to-platelet ratio (APRI) (Obuchowski index, 0.651; P < .001) and fibrosis-4 index (FIB-4) (Obuchowski index, 0.676; P < .001) for staging liver fibrosis. By using the cutoffs, R-fibrosis and R-score had a sensitivity range of 70%-87%, specificity range of 71%-97%, and accuracy range of 82%-86% in diagnosing significant fibrosis, advanced fibrosis and cirrhosis. Conclusion: Radiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis. Funding Information: This work was supported by grants from the National Natural Science Youth Foundation (Grant Number 81902415 to Y.Y.), Natural Science Youth Foundation of Jiangsu Province (Grant Number BK20190116 to Y.Y.) and Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province (No. YXJR202002, to J.W). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: This retrospective study was approved by the institutional review board of the Affiliated Drum Tower Hospital of Nanjing University Medical School. The requirement for written informed consent was waived due to its retrospective nature.
Highlights
Radiomic features were extracted from noncontrast, arterial and portal phase computed tomography (CT) images and selected by using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3-F4 from stage F0-F2
Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis
Liver fibrosis is an important cause of morbidity and mortality in patients with chronic insults and complications mainly occur in advanced fibrosis[1]
Summary
Liver fibrosis is an important cause of morbidity and mortality in patients with chronic insults (e.g. viral hepatitis, alcohol and non-alcoholic fatty liver diseases [NAFLD]) and complications mainly occur in advanced fibrosis[1]. Fibrosis staging is an essential step in the clinical assessment of patients with chronic liver disease to identify those who require treatment[2]. Liver biopsy is the current reference method for staging fibrosis, but it has defects including invasiveness, sample biases and interobserver variability[3,4,5,6]. 2018 practice guidance of the American Association for the Study of Liver Diseases (AASLD) recommended multiphase CT or MRI for initial diagnostic testing in at-risk patients with abnormal surveillance test results[7]. Several studies have evaluated the ability of contrast-enhanced CT imaging to determine the severity of liver fibrosis[8,9,10]. To establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images
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