Artificial intelligence in assessment of hepatocellular carcinoma treatment response
Author:
Publisher
Springer Science and Business Media LLC
Subject
Urology,Gastroenterology,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Link
https://link.springer.com/content/pdf/10.1007/s00261-021-03056-1.pdf
Reference84 articles.
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2. Shropshire EL, Chaudhry M, Miller CM, et al. (2019) LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy. Radiology 292(1):226–234. https://doi.org/10.1148/radiol.2019182135
3. Abdel Razek AAK, El-Serougy LG, Saleh GA, Shabana W, Abd El-Wahab R (2020) Reproducibility of LI-RADS treatment response algorithm for hepatocellular carcinoma after locoregional therapy. Diagn Interv Imaging 101(9):547–553. https://doi.org/10.1016/j.diii.2020.03.008
4. Liew C (2018) The future of radiology augmented with Artificial Intelligence: A strategy for success. Eur J Radiol 102:152–156. https://doi.org/10.1016/j.ejrad.2018.03.019
5. Hardy M, Harvey H (2019) Artificial intelligence in diagnostic imaging: impact on the radiography profession. The British Journal of Radiology 93(1108):20190840. https://doi.org/10.1259/bjr.20190840
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