Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11547-023-01655-0.pdf
Reference111 articles.
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2. Grochans S et al (2022) Epidemiology of glioblastoma multiforme-literature review. Cancers (Basel) 14(10):2412. https://doi.org/10.3390/cancers14102412
3. Ou A, Yung WKA, Majd N (2020) Molecular mechanisms of treatment resistance in glioblastoma. Int J Mol Sci 22(1):351. https://doi.org/10.3390/ijms22010351
4. Minniti G, Tini P, Giraffa M, Capone L, Raza G, Russo I, Cinelli E, Gentile P, Bozzao A, Paolini S, Esposito V (2022) Evaluation of different tumor delineation strategies in patients with glioblastoma receiving standard chemoradiation: impact of reduced treatment volumes on patterns of recurrence and radiation dose to healthy brain tissue. Radiother Oncol. https://doi.org/10.1016/j.radonc.2022.11.024
5. Fan Y, Zhao Z, Wang X, Ai H, Yang C, Luo Y, Jiang X (2022) Radiomics for prediction of response to EGFR-TKI based on metastasis/brain parenchyma (M/BP)-interface. Radiol Med 127(12):1342–1354. https://doi.org/10.1007/s11547-022-01569-3
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