Radiomics Analysis of Contrast-enhanced CT for Staging Liver Fibrosis: An Update for Image Biomarker

Abstract

BackgroundTo establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images.Materials and MethodsThis 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).ResultsIn 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.ConclusionRadiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis.

CT radiomics for Predicting Survival in Patients with Unresectable Pancreatic Ductal Adenocarcinoma after Chemotherapy

“…In patients with unresectable PDAC, the average OS after chemotherapy was (9.99 ± 6.68) months in the training group and (11.00 ± 7.15) months in the test group which was generally consistent with that of current chemoradiotherapy [20], making our study more clinically applicable. In the era of precision medicine, radiomics can provide a large amount of information about shape, signal strength, and texture [21,22]. Our study fully mined the digital information of unresectable PDAC, extracted a large number of radiomics signatures, and further screened out the 15 most stable features, including morphological features and wavelet features.…”