Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study

Abstract Background Determining the grade and molecular marker status of intramedullary gliomas is important for assessing treatment outcomes and prognosis. Invasive biopsy for pathology usually carries a high risk of tissue damage, especially to the spinal cord, and there are currently no non-invas...

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Main Authors: Chao Ma, Liyang Wang, Dengpan Song, Chuntian Gao, Linkai Jing, Yang Lu, Dongkang Liu, Weitao Man, Kaiyuan Yang, Zhe Meng, Huifang Zhang, Ping Xue, Yupeng Zhang, Fuyou Guo, Guihuai Wang
Format: Article
Language:English
Published: BMC 2023-05-01
Series:BMC Medicine
Subjects:
Intramedullary gliomas
Alpha thalassemia/mental retardation syndrome X-linked
Tumor protein p53
Multimodal
Machine learning
Online Access:https://doi.org/10.1186/s12916-023-02898-4

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