Medical image foundation models in assisting diagnosis of brain tumors: a pilot study-Reference-Cited by-同舟云学术

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Medical image foundation models in assisting diagnosis of brain tumors: a pilot study

Published:2024-04-16 Issue:10 Volume:34 Page:6667-6679
ISSN:1432-1084
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Chen Mengyao,Zhang Meng,Yin Lijuan,Ma Lu,Ding Renxing,Zheng Tao,Yue Qiang,Lui Su,Sun Huaiqiang^ORCID

Funder

National Natural Science Foundation of China

Natural Science Foundation of Sichuan Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00330-024-10728-1.pdf

Reference31 articles.

1. Qin JB, Liu Z, Zhang H et al (2017) Grading of gliomas by using radiomic features on multiple magnetic resonance imaging (MRI) sequences. Med Sci Monit 23:2168–2178. https://doi.org/10.12659/MSM.901270

2. Kabir Anaraki A, Ayati M, Kazemi F (2019) Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybern Biomed Eng 39:63–74. https://doi.org/10.1016/j.bbe.2018.10.004

3. Nishikawa T, Ohka F, Aoki K et al (2023) Easy-to-use machine learning system for the prediction of IDH mutation and 1p/19q codeletion using MRI images of adult-type diffuse gliomas. Brain Tumor Pathol 40:85–92. https://doi.org/10.1007/s10014-023-00459-4

4. Forghani R (2020) Precision digital oncology: emerging role of radiomics-based biomarkers and artificial intelligence for advanced imaging and characterization of brain tumors. Radiol Imaging Cancer 2:e190047. https://doi.org/10.1148/rycan.2020190047

5. Rudie JD, Rauschecker AM, Bryan RN et al (2019) Emerging applications of artificial intelligence in neuro-oncology. Radiology 290:607–618. https://doi.org/10.1148/radiol.2018181928

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