Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma

Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma

Abstract Background Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI. Methods We retrospectively included a tota...

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Bibliographic Details
Main Authors: Bin Zhang, Zhouyang Lian, Liming Zhong, Xiao Zhang, Yuhao Dong, Qiuying Chen, Lu Zhang, Xiaokai Mo, Wenhui Huang, Wei Yang, Shuixing Zhang
Format: Article
Language:English
Published: BMC 2020-06-01
Series:BMC Cancer
Subjects:
Radiation-induced temporal lobe injury
Nasopharyngeal carcinoma
Radiomics
Machine learning
Magnetic resonance imaging
Online Access:http://link.springer.com/article/10.1186/s12885-020-06957-4

Internet

http://link.springer.com/article/10.1186/s12885-020-06957-4

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