Comparative study of radiologists vs machine learning in differentiating biopsy-proven pseudoprogression and true progression in diffuse gliomas-Reference-Cited by-同舟云学术

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Comparative study of radiologists vs machine learning in differentiating biopsy-proven pseudoprogression and true progression in diffuse gliomas

Published:2022-09 Issue:3 Volume:2 Page:100088
ISSN:2772-5286
Container-title:Neuroscience Informatics
language:en
Short-container-title:Neuroscience Informatics

Author:

Turk Sevcan^ORCID,Wang Nicholas C.^ORCID,Kitis Omer,Mohammed Shariq,Ma Tianwen,Lobo Remy,Kim John^ORCID,Camelo-Piragua Sandra^ORCID,Johnson Timothy D.,Kim Michelle M.^ORCID,Junck Larry^ORCID,Moritani Toshio,Srinivasan Ashok,Rao Arvind,Bapuraj Jayapalli R.^ORCID

Publisher

Elsevier BV

Reference22 articles.

1. Pseudoprogression of brain tumors;Thust;J. Magn. Reson. Imaging,2018

2. Neurologic complications of oncologic therapy;Arrillga-Romany,2016

3. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group;Wen;J. Clin. Oncol.,2010

4. Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning;Lee;Sci. Rep.,2020

5. Supporting information retrieval from electronic health records: a report of University of Michigan's nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE);Hanauer;J. Biomed. Inform.,2015

Cited by 1 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. CFINet: Cross-Modality MRI Feature Interaction Network for Pseudoprogression Prediction of Glioblastoma;Journal of Computational Biology;2024-07-08

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