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Sanz, Inigo GarciaAuthorAlvarez-Escola, CristinaAuthor

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An Integrated CT and MRI Imaging Model to Differentiate between Adrenal Adenomas and Pheochromocytomas

Publicated to:Cancers. 15 (14): 3736- - 2023-07-01 15(14), DOI: 10.3390/cancers15143736

Authors: Araujo-Castro, Marta; Sanz, Inigo Garcia; Ojeda, Cesar Minguez; Calatayud, Maria; Hanzu, Felicia A; Mora, Mireia; Delgado, Almudena Vicente; Carrera, Concepcion Blanco; Novoa, Paz de Miguel; Garcia, Maria del Carmen Lopez; Manjon-Miguelez, Laura; Gomez, Pablo Rodriguez de Vera; Tous, Maria del Castillo; Millan, Rebeca Barahona San; Recansens, Monica; Fernandez-Ladreda, Mariana Tome; Valdes, Nuria; Gimeno, Paola Gracia; Lazaro, Cristina Robles; Michalopoulou, Theodora; Dos Santos, Victoria Gomez; Alvarez-Escola, Cristina; Centeno, Rogelio Garcia; Lamas, Cristina; Herrera-Martinez, Aura

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Abstract

Simple Summary The information obtained in the CT and MRI allows adequate differentiation of adrenal adenomas and pheochromocytomas (PHEOs) with a high diagnostic accuracy. Our study confirms that our predictive model combining tumor size and lipid content has high re-liability for the prediction of PHEO when atypical adrenal lesions are excluded. However, for atypical adrenal lesions with >10 HU in an unenhanced CT scan, MRI information is necessary for a proper exclusion of the PHEO diagnosis. We observed that in the whole cohort (including atypical adenomas), when MRI information was included in the model, the diagnostic accuracy increased to up to 85% when the variables tumor size, high lipid content in an unenhanced CT scan, and hyperintensity in the T2 sequence in MRI were included. Purpose: to perform an external validation of our predictive model to rule out pheochromocytoma (PHEO) based on unenhanced CT in a cohort of patients with PHEOs and adenomas who underwent adrenalectomy. Methods: The predictive model was previously developed in a retrospective cohort of 1131 patients presenting with adrenal lesions. In the present study, we performed an external validation of the model in another cohort of 214 patients with available histopathological results. Results: For the external validation, 115 patients with PHEOs and 99 with adenomas were included. Our previously described predictive model combining the variables of high lipid content and tumor size in unenhanced CT (AUC-ROC: 0.961) had a lower diagnostic accuracy in our current study population for the prediction of PHEO (AUC: 0.750). However, when we excluded atypical adenomas (with Hounsfield units (HU) > 10, n = 39), the diagnostic accuracy increased to 87.4%. In addition, in the whole cohort (including atypical adenomas), when MRI information was included in the model, the diagnostic accuracy increased to up to 85% when the variables tumor size, high lipid content in an unenhanced CT scan, and hyperintensity in the T2 sequence in MRI were included. The probability of PHEO was 10 HU and without hyperintensity in T2. Conclusion: Our study confirms that our predictive model combining tumor size and lipid content has high reliability for the prediction of PHEO when atypical adrenal lesions are excluded. However, for atypical adrenal lesions with >10 HU in an unenhanced CT scan, MRI information is necessary for a proper exclusion of the PHEO diagnosis.

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