Multimodality MRI Radiomics Based on Machine Learning for Identifying True Tumor Recurrence and Treatment-Related Effects in Patients with Postoperative Glioma

Multimodality MRI Radiomics Based on Machine Learning for Identifying True Tumor Recurrence and Treatment-Related Effects in Patients with Postoperative Glioma

Abstract Introduction Conventional magnetic resonance imaging (MRI) features have difficulty distinguishing glioma true tumor recurrence (TuR) from treatment-related effects (TrE). We aimed to develop a machine-learning model based on multimodality MRI radiomics to help improve the efficiency of ide...

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Bibliographic Details
Main Authors: Jinfa Ren, Xiaoyang Zhai, Huijia Yin, Fengmei Zhou, Ying Hu, Kaiyu Wang, Ruifang Yan, Dongming Han
Format: Article
Language:English
Published: Adis, Springer Healthcare 2023-07-01
Series:Neurology and Therapy
Subjects:
Glioma
Machine learning
Radiomics
Treatment-related effects
True tumor recurrence
Online Access:https://doi.org/10.1007/s40120-023-00524-2

Internet

https://doi.org/10.1007/s40120-023-00524-2

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