Developing and validating a deep learning and radiomic model for glioma grading using multiplanar reconstructed magnetic resonance contrast-enhanced T1-weighted imaging: a robust, multi-institutional study-Reference-Cited by-同舟云学术

Developing and validating a deep learning and radiomic model for glioma grading using multiplanar reconstructed magnetic resonance contrast-enhanced T1-weighted imaging: a robust, multi-institutional study

Published:2022-02 Issue:2 Volume:12 Page:1517-1528
ISSN:2223-4292
Container-title:Quantitative Imaging in Medicine and Surgery
language:
Short-container-title:Quant Imaging Med Surg

Author:

Ding Jialin,Zhao Rubin,Qiu Qingtao,Chen Jinhu,Duan Jinghao,Cao Xiujuan,Yin Yong

Publisher

AME Publishing Company

Subject

Radiology, Nuclear Medicine and imaging

Link

https://qims.amegroups.com/article/download/83146/pdf

Cited by 22 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The Current Diagnostic Performance of MRI-Based Radiomics for Glioma Grading: A Meta-Analysis;Journal of Integrative Neuroscience;2024-05-14

2. One-stop detection of anterior cruciate ligament injuries on magnetic resonance imaging using deep learning with multicenter validation;Quantitative Imaging in Medicine and Surgery;2024-05

3. MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas;Journal of Imaging Informatics in Medicine;2024-01-24

4. Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology;Cancers;2024-01-10

5. A fusion model integrating magnetic resonance imaging radiomics and deep learning features for predicting alpha-thalassemia X-linked intellectual disability mutation status in isocitrate dehydrogenase–mutant high-grade astrocytoma: a multicenter study;Quantitative Imaging in Medicine and Surgery;2024-01