  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.
