Clinical Report: 3D Imaging in Diagnosis and Management of Adrenal Disorders
Overview
This systematic review evaluates the clinical utility of three-dimensional (3D) imaging techniques, including volumetric reconstructions and artificial intelligence (AI)-assisted segmentation, in diagnosing and managing adrenal disorders. Findings highlight improved anatomical visualization, enhanced surgical planning, and potential reduction in unnecessary adrenalectomies through better lesion characterization.
Background
Adrenal abnormalities are frequently detected incidentally due to widespread use of high-resolution imaging, with a prevalence of 10% in elderly populations. Differentiating benign from malignant lesions remains challenging, often leading to overtreatment and unnecessary total adrenalectomies. Conventional imaging methods rely on subjective assessments, which may be inconsistent. Emerging 3D imaging techniques, including volume rendering, surface rendering, and AI-assisted segmentation, offer improved anatomical detail and quantitative analysis to support diagnosis and surgical decision-making.
Data Highlights
The review included studies employing 3D volumetric reconstructions derived from CT or MRI in patients with adrenal disease. Techniques ranged from manual to fully automatic segmentation, with applications in diagnosis, surgical planning, and post-treatment surveillance. AI-based methods facilitated semi-automatic and automatic segmentation, improving efficiency and reproducibility. Augmented reality and 3D printing were also utilized for intraoperative guidance and surgical training. Risk of bias was assessed using QUADAS-2, and studies were categorized by IDEAL stages to indicate technology maturity.
Key Findings
3D volumetric reconstructions enhance anatomical visualization beyond conventional 2D imaging, aiding in precise surgical planning and navigation.
AI-assisted segmentation reduces manual workload and improves reproducibility in adrenal gland and lesion volume delineation.
Radiomics and texture analysis derived from 3D imaging support differential diagnosis between benign and malignant adrenal lesions.
Use of 3D imaging and augmented reality facilitates cortical-sparing adrenalectomy, potentially preserving functional adrenal tissue and reducing morbidity.
3D-printed models provide valuable ex vivo training tools and improve surgeon familiarity with complex adrenal anatomy.
Despite promising applications, a lack of large prospective studies limits definitive conclusions on impact in reducing unnecessary surgeries.
Clinical Implications
Incorporating 3D imaging techniques into adrenal disease management can improve diagnostic accuracy and surgical outcomes by providing detailed anatomical and volumetric information. AI-supported segmentation streamlines workflow and may reduce overtreatment by better characterizing lesions preoperatively. Surgeons can leverage augmented reality and 3D-printed models for enhanced intraoperative guidance and training, particularly in complex or cortical-sparing procedures.
Conclusion
3D imaging technologies, especially when combined with AI, represent a significant advancement in the diagnosis and management of adrenal disorders. Further prospective validation is needed to confirm their role in minimizing unnecessary adrenalectomies and improving patient outcomes.
References
Systematic Review Authors/2024 -- A Systematic Review of 3D Imaging Techniques in the Diagnosis and Management of Adrenal Disorders
