AI-enabled precision evaluation of adrenal masses: radiomics, deep learning, and explainable imaging biomarkers
TL;DR
The workflow of radiomic feature extraction and model development is systematically described, emphasizing their roles in differentiating key lesions such as pheochromocytomas/paragangliomas, adrenal cortical adenomas, and adrenal cortical carcinomas.
Abstract
Accurate evaluation of adrenal masses remains a significant challenge in endocrinology and radiology, as differential diagnosis involves a wide spectrum of benign and malignant lesions. Radiomics and deep learning (DL) have emerged as promising tools to enhance the precision of adrenal mass assessment by extracting high-dimensional imaging features and enabling automated, data-driven analysis. This review summarizes the latest advancements in the application of radiomics and DL techniques for adrenal mass evaluation. We systematically describe the workflow of radiomic feature extraction and model development, emphasizing their roles in differentiating key lesions such as pheochromocytomas/paragangliomas (PPGLs), adrenal cortical adenomas, and adrenal cortical carcinomas. Additionally, the utility of these approaches in genotype prediction and prognostic evaluation is highlighted. The review further explores the advantages and potential of DL, particularly convolutional neural networks (CNNs), in automated segmentation, feature learning, and end-to-end diagnostic frameworks. Finally, current challenges including technical limitations, clinical translation barriers, and future research directions are discussed, aiming to provide a theoretical foundation for constructing intelligent and precise adrenal mass evaluation systems.