Abstract
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.
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URL
https://arxiv.org/abs/2211.14396
