Qian Li 1, Fan Fu2, Jie Chen1, Yu Zhang2, Biao Li2, Na Zhang1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Presenting Author: Qian Li
Synopsis
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1. Musialek P, Bonati LH, Bulbulia R, et al. Stroke risk management in carotid atherosclerotic disease: A Clinical Consensus Statement of the ESC Council on Stroke and the ESC Working Group on Aorta and Peripheral Vascular Diseases. Cardiovascular research. 2025;121(1):13-43. doi:10.1093/cvr/cvad135. [doi]
2. Dieleman N, van der Kolk AG, Zwanenburg JJ, et al. Imaging intracranial vessel wall pathology with magnetic resonance imaging: current prospects and future directions. Circulation. 2014;130(2):192-201. doi:10.1161/CIRCULATIONAHA.113.006919. [doi]
3. Tarkin JM, Joshi FR, Rudd JH. PET imaging of inflammation in atherosclerosis. Nature Reviews Cardiology. 2014;11(8):443-457. doi:10.1038/nrcardio.2014.80. [doi]
4. Senders ML, Calcagno C, Tawakol A, Nahrendorf M, Mulder WJ, Fayad ZA. PET/MR imaging of inflammation in atherosclerosis. Nature Biomedical Engineering. 2023;7(3):202-220. doi:10.1038/s41551-022-00970-7. [doi]
5. Leccisotti L, Nicoletti P, Cappiello C, Indovina L, Giordano A. PET imaging of vulnerable coronary artery plaques. Clinical and Translational Imaging. 2019;7(4):267-284. doi:10.1007/s40336-019-00334-3. [doi]
6. Zhang R, Zhang Q, Ji A, et al. Identification of high-risk carotid plaque with MRI-based radiomics and machine learning. European radiology. 2021;31(5):3116-3126. doi:10.1007/s00330-020-07361-z. [doi]
7. Li H, Liu J, Dong Z, et al. Identification of high-risk intracranial plaques with 3D high-resolution magnetic resonance imaging-based radiomics and machine learning. Journal of neurology. 2022;269(12):6494-6503. doi:10.1007/s00415-022-11315-4. [doi]
8. Huang S-y, Franc BL, Harnish RJ, et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. NPJ breast cancer. 2018;4(1):24. doi:10.1038/s41523-018-0078-2. [doi]
9. Collarino A, Feudo V, Pasciuto T, et al. Is PET radiomics useful to predict pathologic tumor response and prognosis in locally advanced cervical cancer? Journal of Nuclear Medicine. 2024;65(6):962-970. doi: 10.2967/jnumed.123.267044 [doi]