作者
Lan Jin, Mark Youngblood, Trisha Gupte, Shaurey Vetsa, Arushii Nadar, Tanyeri Barak, Kanat Yalcin, Stephanie Aguilera, Ketu Mishra-Gorur, Nicholas Blondin, S Bulent Omay, Renelle Pointdujour-Lim, Benjamin Judson, Michael Alperovich, Mariam Aboian, Declan McGuone, Murat Gunel, Zeynep Erson-Omay, Robert Fulbright, Jennifer Moliterno
发表日期
2021/11/2
来源
Neuro-Oncology
卷号
23
期号
Supplement_6
页码范围
vi144-vi144
出版商
Oxford University Press
简介
PURPOSE
Differentiating gliomas and Primary CNS Lymphomas (PCNSL) represents a diagnostic challenge with important therapeutic ramifications. MR imaging combined with Machine Learning (ML) has shown promising results in differentiating tumors non-invasively. The purpose of this systematic review is to evaluate and synthesize the findings on the application of ML in differentiating PCNSL and gliomas.
MATERIALS AND METHODS
A systematic search of literature was performed in October 2020 and February 2021 on Ovid Embase, Ovid MEDLINE, Cochrane trials, and Web of Science – Core Collection. The search strategy included keywords and controlled vocabulary including the terms: gliomas, artificial intelligence, machine learning, and related terms. Publications were reviewed and screened by four different reviewers in accordance with TRIPOD.
RESULTS
The literature search yielded 11,727 …
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L Jin, M Youngblood, T Gupte, S Vetsa, A Nadar… - Neuro-Oncology, 2021