Non-invasive Liver Fibrosis Screening on CT Images using Radiomics

TL;DR

This study develops a radiomics machine learning model using non-contrast CT images to non-invasively detect liver fibrosis with high sensitivity, potentially enabling earlier diagnosis and treatment.

Contribution

The paper introduces a novel radiomics-based machine learning approach specifically optimized for non-contrast CT images to screen for liver fibrosis.

Findings

Non-contrast CT outperformed contrast-enhanced CT in AUC.

The best model achieved an AUC of 0.7833 with high sensitivity.

Radiomics features from non-contrast CT can effectively detect liver fibrosis.

Abstract

Objectives: To develop and evaluate a radiomics machine learning model for detecting liver fibrosis on CT of the liver. Methods: For this retrospective, single-centre study, radiomic features were extracted from Regions of Interest (ROIs) on CT images of patients who underwent simultaneous liver biopsy and CT examinations. Combinations of contrast, normalization, machine learning model, and feature selection method were determined based on their mean test Area Under the Receiver Operating Characteristic curve (AUC) on randomly placed ROIs. The combination and selected features with the highest AUC were used to develop a final liver fibrosis screening model. Results: The study included 101 male and 68 female patients (mean age = 51.2 years $\pm$ 14.7 [SD]). When averaging the AUC across all combinations, non-contrast enhanced (NC) CT (AUC, 0.6100; 95% CI: 0.5897, 0.6303) outperformed contrast-enhanced CT (AUC, 0.5680; 95% CI: 0.5471, 0.5890). The combination of hyperparameters and features that yielded the highest AUC was a logistic regression model with inputs features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with $\gamma$ = 1.5 (AUC, 0.7833; 95% CI: 0.7821, 0.7845), (sensitivity, 0.9091; 95% CI: 0.9091, 0.9091). Conclusions: Radiomics-based machine learning models allow for the detection of liver fibrosis with reasonable accuracy and high sensitivity on NC CT. Thus, these models can be used to non-invasively screen for liver fibrosis, contributing to earlier detection of the disease at a potentially curable stage.