The power of the radiologist’s last word: can deep learning models accurately differentiate between high-grade gliomas and metastasis through natural language processing on radiology reports?
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/s00330-023-10245-7.pdf
Reference11 articles.
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3. Ye Z, Price RL, Liu X et al (2020) Diffusion histology imaging combining diffusion basis spectrum imaging (DBSI) and machine learning improves detection and classification of glioblastoma pathology. Clin Cancer Res 26. https://doi.org/10.1158/1078-0432.CCR-20-0736
4. Hosny A, Bitterman DS, Guthier CV et al (2022) Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study. Lancet Digit Health 4:e657–e666. https://doi.org/10.1016/S2589-7500(22)00129-7
5. Kazmierski M, Welch M, Kim S et al (2023) Multi-institutional prognostic modeling in head and neck cancer: evaluating impact and generalizability of deep learning and radiomics. Cancer Res Commun 3:1140–1151. https://doi.org/10.1158/2767-9764.CRC-22-0152
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