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ea0099oc11.3 | Oral Communications 11: Adrenal and Cardiovascular Endocrinology | Part II | ECE2024

Prediction of adrenal masses nature through texture analysis and deep learning: Preliminary results from ENS@T RADIO-AI multicentric study

Tucci Lorenzo , Vara Giulio , Morelli Valentina , Menendez Torre Edelmiro Luis , Dischinger Ulrich , Markou Athina , Terzolo Massimo , Spyroglou Ariadni , Parazzoli Chiara , Carmen Aresta , Chiodini Iacopo , Rivas Diego , Gutierrez Alba , Schlotelburg Wiebke , Alexandraki Krystallenia , Puglisi Soraya , Improta Ilaria , De Leo Antonio , Selva Saverio , Alberici Laura , De Giglio Andrea , Pantaleo Maria Abbondanza , Balacchi Caterina , Mosconi Cristina , Vicennati Valentina , Pagotto Uberto , Di Dalmazi Guido

Background: Current parameters of conventional radiology have several limitations in defining the nature of adrenal masses. Radiomics, or texture analysis, has shown high diagnostic performance in recent pilot studies, although confirmatory studies are needed. Moreover, the effect of combination of radiomics with hormonal secretion on diagnostic performance is poorly explored.Aim: To evaluate the accuracy of radiomics in predicting adrenal masses nature ...

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Prediction of adrenal masses nature through texture analysis and deep learning: Preliminary results from ENS@T RADIO-AI multicentric study

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