Clinical Scorecard: 3D Imaging Techniques in the Diagnosis and Management of Adrenal Disorders

At a Glance

Key Highlights

• Adrenal lesions are common in elderly with 10% prevalence; malignancy rates vary from 2-3% in general to up to 30% in oncology cohorts.
• 3D imaging including volume rendering, surface rendering, and 3D printing enhances anatomical visualization and surgical planning.
• AI-based semi-automatic and automatic segmentation improves efficiency and accuracy of adrenal lesion characterization.

Guideline-Based Recommendations

Diagnosis

• Use comprehensive diagnostic workup including clinical, genetic, hormonal, and imaging studies for adrenal lesions.
• Apply 3D imaging techniques such as volumetric reconstructions and radiomics to improve non-invasive lesion characterization.
• Employ AI-assisted segmentation to enhance diagnostic accuracy and reduce subjective variability.

Management

• Base surgical decisions primarily on hormonal excess and suspicion of malignancy.
• Consider cortical-sparing adrenalectomy when appropriate to preserve functional adrenal tissue.
• Utilize 3D models and augmented reality for surgical planning, navigation, and intraoperative guidance.

Monitoring & Follow-up

• Monitor adrenal lesion volume changes over time using 3D imaging and deep-learning-assisted RECIST scoring.
• Use texture analysis radiomics to support differential diagnosis and lesion growth assessment.

Risks

• High overtreatment rate with 30–55% of adrenal surgeries performed on benign lesions.
• Unnecessary total adrenalectomy may lead to loss of functional adrenal tissue and increased morbidity.

Patient & Prescribing Data

Patients with incidentally discovered or clinically suspected adrenal lesions

3D imaging aids in distinguishing benign from malignant lesions, guiding surgical decisions and potentially reducing unnecessary adrenalectomies.

Clinical Best Practices

• Integrate 3D volumetric imaging with clinical and hormonal data for comprehensive adrenal lesion evaluation.
• Adopt AI-based segmentation tools to improve efficiency and reproducibility in adrenal imaging analysis.
• Use patient-specific 3D reconstructions and augmented reality technologies to optimize surgical planning and intraoperative navigation.
• Prefer cortical-sparing approaches when feasible to minimize loss of adrenal function.
• Implement longitudinal 3D imaging follow-up to monitor lesion progression and treatment response.

References

• Prevalence and malignancy rates of adrenal lesions
• Radiomics and AI in adrenal imaging
• 3D imaging techniques and surgical applications
• Systematic review registration PROSPERO CRD42024500783
• PRISMA guidelines for systematic reviews