Integration of clinical parameters and CT-based radiomics improves machine learning assisted subtyping of primary hyperaldosteronism.

Nabeel Mansour,Andreas Mittermeier, Roman Walter,Balthasar Schachtner,Jan Rudolph,Bernd Erber,Vanessa F Schmidt,Daniel Heinrich, Denise Bruedgam,Lea Tschaidse,Hanna Nowotny, Martin Bidlingmaier,Sonja L Kunz,Christian Adolf,Jens Ricke, Martin Reincke,Nicole Reisch,Moritz Wildgruber,Michael Ingrisch

Frontiers in endocrinology（2023）

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Abstract

Integration of clinical parameters into a radiomics machine learning model improves prediction of the source of aldosterone overproduction and subtyping in patients with PA.

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Key words

machine learning, hyperaldosteronism, adrenal venous sampling, integrated diagnostics, venous interventions

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