Differentiating between giant cell arteritis and atherosclerosis on [18F]FDG-PET: an explainable machine learning approach

Background This work aims to investigate the feasibility of an explainable machine learning model based on radiomics features to differentiate between giant cell arteritis (GCA) and atherosclerosis in aortic [
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F]FDG-PET scans. Method Twenty [
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F]FDG-PET scans (ten of patients with GCA, ten with atherosclerosis) were retrospectively included. The aorta was delineated into four segments (ascending, arch, descending, and abdominal aorta). In total, 93 radiomic features and two quantitative features were extracted from each of the 80 segments. Four different feature selection methods and four classifiers were used to identify important features for the machine learning model and determine the probability. The model's performance was evaluated using accuracy and AUC. To enhance explainability of the model, feature importance was determined, and an occlusion sensitivity map of the aorta was created. Results The combination of the first-order skewness, GLDM dependence non-uniformity, and GLRLM run entropy features showed the highest accuracy and AUC of, 0.90±0.08 and 0.960±0.029, respectively. Conclusion This study demonstrated the potential of an explainable radiomics-based machine learning model for the differentiation between GCA and atherosclerosis in P
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F]FDG-PET scans.