MRI radiomics-based approach to predict pituitary neuroendocrine tumor invasiveness

Abstract Objectives To assess the diagnostic potential of magnetic resonance imaging (MRI) radiomics and machine learning models using T2-weighted and contrast-enhanced (CE)-T1-weighted images, individually and combined, to predict the invasiveness of pituitary neuroendocrine tumors (PitNETs). Mater...

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Autors principals:	Rosalinda Calandrelli, Huong Elena Tran, Edda Boccia, Elia Oliva, Gabriella D’Apolito, Luca Boldrini, Pier Paolo Mattogno, Sabrina Chiloiro, Marco Gessi, Francesco Doglietto, Simona Gaudino
Format:	Artigo
Idioma:	Inglês
Publicat:	SpringerOpen 2026-05-01
Col·lecció:	European Radiology Experimental
Matèries:	Magnetic resonance imaging Machine learning Neuroendocrine tumors Pituitary gland Radiomics
Accés en línia:	https://doi.org/10.1186/s41747-026-00736-9
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