Radiomics-Based Machine Learning to Predict Recurrence in Glioma Patients Using Magnetic Resonance Imaging-Reference-Cited by-同舟云学术

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Radiomics-Based Machine Learning to Predict Recurrence in Glioma Patients Using Magnetic Resonance Imaging

Published:2022-09-22 Issue:1 Volume:47 Page:129-135
ISSN:1532-3145
Container-title:Journal of Computer Assisted Tomography
language:en
Short-container-title:J Comput Assist Tomogr

Author:

Hu Guanjie,Hu Xinhua,Yang Kun,Yu Yun,Jiang Zijuan,Liu Yong,Liu Dongming,Hu Xiao,Xiao Hong,Zou Yuanjie,You Yongping,Liu Hongyi,Chen Jiu

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Radiology, Nuclear Medicine and imaging

Reference59 articles.

1. The 2016 World Health Organization classification of tumors of the central nervous system: a summary;Acta Neuropathol,2016

2. Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas;Lancet Oncol,2011

3. Emerging role of plexins signaling in glioma progression and therapy;Cancer Lett,2018

4. Isocitrate dehydrogenase mutations in gliomas;Neuro Oncol,2016

5. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors;N Engl J Med,2015

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2. Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation;Diagnostics;2023-08-14

3. Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning;Balkan Medical Journal;2023-01-24

4. Research Status and Progress of Imaging Radiomics in Acute Pancreatitis;Advances in Clinical Medicine;2023

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