Dual-energy CT (DECT) has emerged as a novel imaging modality that offers a multiparametric approach for noninvasive adrenal lesion characterization. This narrative review examines recent advances in DECT—including virtual non-contrast imaging, iodine density quantification, spectral curve analysis, and material density mapping—for differentiating benign adrenal adenomas from metastases. Conventional CT techniques rely primarily on unenhanced attenuation measurements and contrast washout kinetics; however, these methods may be limited in evaluating lipid-poor adenomas, and in cases where imaging features overlap with metastatic lesions. Although virtual non-contrast imaging with DECT tends to overestimate attenuation relative to true non-contrast scans, the recalibration of diagnostic thresholds and integration with complementary parameters, such as the iodine density-to-virtual non-contrast attenuation ratio, can significantly enhance sensitivity and specificity. Additional parameters, including fat fraction analysis and the evaluation of attenuation changes across energy spectra, further refine tissue characterization by quantifying intracellular lipid content and vascularity. Material density analysis has demonstrated near-perfect diagnostic accuracy in select studies. By tailoring imaging evaluation to the unique spectral and compositional features of each adrenal lesion, DECT contributes to a more personalized diagnostic approach. This individualization allows for better differentiation between benign and malignant findings, potentially avoiding unnecessary interventions and enabling more targeted clinical management. Despite these promising developments, challenges remain regarding the standardization of acquisition protocols, optimization of diagnostic thresholds, and minimization of interobserver variability. Emerging radiomics and machine learning applications may further automate lesion classification and improve diagnostic accuracy. Thus, DECT holds considerable potential to improve diagnostic confidence, reduce radiation exposure, and streamline the management of patients with adrenal incidentalomas, although further multicenter validation is warranted.

The Role of Dual-Energy CT in Differentiating Adrenal Adenomas from Metastases: A Comprehensive Narrative Review

Tiralongo, Francesco;Foti, Pietro Valerio;Calogero, Aldo Eugenio;La Vignera, Sandro;Ini', Corrado;David, Emanuele;Palmucci, Stefano;Basile, Antonio
2025-01-01

Abstract

Dual-energy CT (DECT) has emerged as a novel imaging modality that offers a multiparametric approach for noninvasive adrenal lesion characterization. This narrative review examines recent advances in DECT—including virtual non-contrast imaging, iodine density quantification, spectral curve analysis, and material density mapping—for differentiating benign adrenal adenomas from metastases. Conventional CT techniques rely primarily on unenhanced attenuation measurements and contrast washout kinetics; however, these methods may be limited in evaluating lipid-poor adenomas, and in cases where imaging features overlap with metastatic lesions. Although virtual non-contrast imaging with DECT tends to overestimate attenuation relative to true non-contrast scans, the recalibration of diagnostic thresholds and integration with complementary parameters, such as the iodine density-to-virtual non-contrast attenuation ratio, can significantly enhance sensitivity and specificity. Additional parameters, including fat fraction analysis and the evaluation of attenuation changes across energy spectra, further refine tissue characterization by quantifying intracellular lipid content and vascularity. Material density analysis has demonstrated near-perfect diagnostic accuracy in select studies. By tailoring imaging evaluation to the unique spectral and compositional features of each adrenal lesion, DECT contributes to a more personalized diagnostic approach. This individualization allows for better differentiation between benign and malignant findings, potentially avoiding unnecessary interventions and enabling more targeted clinical management. Despite these promising developments, challenges remain regarding the standardization of acquisition protocols, optimization of diagnostic thresholds, and minimization of interobserver variability. Emerging radiomics and machine learning applications may further automate lesion classification and improve diagnostic accuracy. Thus, DECT holds considerable potential to improve diagnostic confidence, reduce radiation exposure, and streamline the management of patients with adrenal incidentalomas, although further multicenter validation is warranted.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/672590
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