Invasiveness identification in pure ground-glass nodules: exploring the generalizability of radiomics based on external validation and stress testing
Author:
Funder
Dalian Science and Technology Innovation Fund
Publisher
Springer Science and Business Media LLC
Subject
Cancer Research,Oncology,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00432-023-05105-2.pdf
Reference28 articles.
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2. D'Amour A, Heller KA, Moldovan DI, et al (2020) Underspecification Presents Challenges for Credibility in Modern Machine Learning. ArXiv 2011.03395. https://arxiv.org/abs/2011.03395.
3. Eche T, Schwartz LH, Mokrane FZ, Dercle L (2021) Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification. Radiol Artif Intell 3(6):e210097. https://doi.org/10.1148/ryai.2021210097
4. Fan L, Fang M, Li Z, Tu W, Wang S, Chen W, Tian J, Dong D, Liu S (2019) Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule. Eur Radiol 29(2):889–897. https://doi.org/10.1007/s00330-018-5530-z
5. Ferretti GR, Arbib F, Roux JF, Bland V, Lantuejoul S (2012) Effect of lung volume and gravity on the attenuation and size of a pure ground-glass nodule. J Thorac Imaging 27(1):W15–W17. https://doi.org/10.1097/RTI.0b013e31820c7929
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