  Objectives: Early detection of liver fibrosis can help cure the disease or
prevent disease progression. We perform a comprehensive study of machine
learning-based fibrosis detection in CT images using radiomic features to
develop a non-invasive approach to fibrosis detection.
  Methods: Two sets of radiomic features were extracted from spherical ROIs in
CT images of 182 patients who underwent simultaneous liver biopsy and CT
examinations, one set corresponding to biopsy locations and another distant
from biopsy locations. Combinations of contrast, normalization, machine
learning model, feature selection method, bin width, and kernel radius were
investigated, each of which were trained and evaluated 100 times with
randomized development and test cohorts. The best settings were evaluated based
on their mean test AUC and the best features were determined based on their
frequency among the best settings.
  Results: Logistic regression models with NC images normalized using Gamma
correction with $\gamma = 1.5$ performed best for fibrosis detection. Boruta
was the best for radiomic feature selection method. Training a model using
these optimal settings and features consisting of first order energy, first
order kurtosis, and first order skewness, resulted in a model that achieved
mean test AUCs of 0.7549 and 0.7166 on biopsy-based and non-biopsy ROIs
respectively, outperforming a baseline and best models found during the initial
study.
  Conclusions: Logistic regression models trained on radiomic features from NC
images normalized using Gamma correction with $\gamma = 1.5$ that underwent
Boruta feature selection are effective for liver fibrosis detection. Energy,
kurtosis, and skewness are particularly effective features for fibrosis
detection.

Computer Vision and Pattern Recognition;Machine Learning;Quantitative Methods