Adrenal masses are found in approximately 5% of patients undergoing cross-sectional imaging. Determining whether an incidental adrenal mass is malignant or benign is a critical part of the diagnostic process. Advanced imaging techniques often reveal adrenal tumors in both symptomatic and asymptomatic patients. Cross-sectional imaging, particularly CT, provides valuable insights into the composition of adrenal masses. However, many large but benign adrenal tumors are surgically removed for pathological diagnosis, while smaller malignant tumors goes undetected. This study aims to analyze the accuracy of CT imaging through radiologic-pathologic correlation and to identify new criteria for more effective diagnosis, which can aid in guiding treatment decisions.

This is a retrospective study in which patient data were collected from hospital records in the Departments of Urology and Pathology at Muljibhai Patel Urological Hospital, Nadiad, India. The study encompassed all patients who underwent surgery for adrenal masses over a 20-year period. CT data from 95 adrenalectomy specimens were retrospectively analyzed and correlated with Various CT parameters. New Parameters were compared to predict histopathological outcomes. Data analysis was performed using SPSS Version 25.

A comprehensive analysis was conducted using the entire adrenal mass specimen, rather than just a biopsy tissue sample. This enabled us to include preoperative parameters for identifying new correlational analytics. The distribution of individual phase Hounsfield Unit (HU) values varied significantly across different conditions, limiting the effectiveness of HU values for diagnosing and differentiating lesions. While using absolute washout values, resulted in sensitivity of diagnosing it as being adenoma reached upto 60% Combining an HU enhancement cutoff of 30 with absolute washout values of 65,it yielded a sensitivity of 90% and a specificity of 85.%.

The diagnostic challenge for adrenal lesions on CT imaging arises from the difficulty in distinguishing benign lesions from potentially malignant tumors due to overlapping imaging characteristics. By combining parameters—specifically, using an absolute washout cutoff of 65 and an HU enhancement cutoff of 30—the specificity for identifying malignancy exceeds 85%. Utilizing these newer parameters can significantly enhance decision-making in managing adrenal lesions promptly. Future directions include leveraging AI-driven analysis to improve diagnostic precision by using advanced computational techniques to enhance lesion characterization and differentiation on imaging.