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Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort

Authors :
Pamporaki, Christina
Berends, Annika M A
Filippatos, Angelos
Prodanov, Tamara
Meuter, Leah
Prejbisz, Alexander
Beuschlein, Felix
Fassnacht, Martin
Timmers, Henri J L M
Nölting, Svenja
Abhyankar, Kaushik
Constantinescu, Georgiana
Kunath, Carola
de Haas, Robbert J
Wang, Katharina
Remde, Hanna
Bornstein, Stefan R
Januszewicz, Andrzeij
Robledo, Mercedes
Lenders, Jacques W M
Kerstens, Michiel N
Pacak, Karel
Eisenhofer, Graeme
Source :
The Lancet Digital Health; 20240101, Issue: Preprints
Publication Year :
2024

Abstract

Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field.

Details

Language :
English
ISSN :
25897500
Issue :
Preprints
Database :
Supplemental Index
Journal :
The Lancet Digital Health
Publication Type :
Periodical
Accession number :
ejs63576228
Full Text :
https://doi.org/10.1016/S2589-7500(23)00094-8