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ea0081oc13.6 | Oral Communications 13: Adrenal and Cardiovascular Endocrinology 2 | ECE2022

Machine Learning models for the accurate prediction of malignant pheochromocytomas and paragangliomas

Pamporaki Christina , Berends Annika MA , Filippatos Angelos , Prodanov Tamara , Meuter Leah , Prejbisz Aleksander , Beuschlein Felix , Fassnacht Martin , Timmers Henri , Noelting Svenja , Abhyankar Kaushik Ganesh , Contsantinescu Georgiana , Kunath Carola , Wang Katharina , Remde Hanna , Januszewicz Andrzej , Robledo Mercedes , Lenders Jacques , Kerstens Michiel , Pacak Karel , Eisenhofer Graeme

Introduction: Pheochromocytomas and paragangliomas (PPGLs) exhibit an up to 20% malignancy rate. Various clinical, genetic, and pathological features have been proposed as predictors of malignancy. However, until present there are no robust indices to reliably predict metastatic PPGLs.Aim: The aim of the present study was to prospectively validate the value of methoxytyramine as risk marker of metastatic disease and establish a machine learning (ML) mode...

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Machine Learning models for the accurate prediction of malignant pheochromocytomas and paragangliomas

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