Abstract
Background: Medical imaging analysis has evolved to facilitate the development of AI-enhanced methods for high-throughput extraction of quantitative features that can potentially contribute to the diagnostic and treatment paradigm of adrenal pathology. Adrenal lesions can be classified as benign/malignant or originating in the cortex/medulla. Qualitative radiological features can contribute to diagnosis however histopathological assessment is required to definitively establish the type of lesion. There is a need for non-invasive diagnostic markers due to the impracticality of adrenal biopsy. The aim of this study was to develop and validate a radiomic classifier to non-invasively classify adrenal lesions on CT.
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