CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study

CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study

Abstract Objectives This study aimed to develop and validate CT-based radiomics deep learning signatures for the non-invasive prediction of metastatic potential in pheochromocytomas and paragangliomas (PPGLs). Methods We conducted a retrospective analysis of 249 PPGL patients from three institutions...

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Bibliographic Details
Main Authors: Yongjie Zhou, Yuan Zhan, Jinhong Zhao, Linhua Zhong, Fei Zou, Xuechao Zhu, Qiao Zeng, Jiayu Nan, Lianggeng Gong, Yongming Tan, Lan Liu
Format: Article
Language:English
Published: SpringerOpen 2025-04-01
Series:Insights into Imaging
Subjects:
Pheochromocytoma
Paraganglioma
Metastatic potential
Radiomics
Deep learning
Online Access:https://doi.org/10.1186/s13244-025-01952-4
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https://doi.org/10.1186/s13244-025-01952-4

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