References
Letzen B, Wang CJ, Chapiro J. The role of artificial intelligence in interventional oncology: a primer. J Vasc Interv Radiol. 2019;30(1):38–41.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.
Kim J, Choi SJ, Lee SH, et al. Predicting survival using pretreatment CT for patients with hepatocellular carcinoma treated with transarterial chemoembolization: comparison of models using radiomics. AJR Am J Roentgenol. 2018;211(5):1026–34.
Kuo MD, Gollub J, Sirlin CB, Ooi C, Chen X. Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. J Vasc Interv Radiol. 2007;18(7):821–31.
Sailer AM, van Kuijk SM, Nelemans PJ, et al. Computed tomography imaging features in acute uncomplicated stanford type-B aortic dissection predict late adverse events. Circ Cardiovasc Imaging. 2017;10(4):e005709.
Alawieh A, Zaraket F, Alawieh MB, Chatterjee AR, Spiotta A. Using machine learning to optimize selection of elderly patients for endovascular thrombectomy. J Neurointerv Surg. 2019.
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Sailer, A.M., Tipaldi, M.A. & Krokidis, M. AI in Interventional Radiology: There is Momentum for High-Quality Data Registries. Cardiovasc Intervent Radiol 42, 1208–1209 (2019). https://doi.org/10.1007/s00270-019-02249-y
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DOI: https://doi.org/10.1007/s00270-019-02249-y