Editorial
Editorial for “MRI-based Radiomics: Potential Abilities for Individual Preoperative Predictions of the Recurrence-Free Survival of Patients With Hepatocellular Carcinoma Treated With Conventional Transcatheter Arterial Chemoembolization”
First published: 24 June 2020
No abstract is available for this article.
References
- 1Song W, Yu X, Guo D, et al. MRI-based radiomics: Associations with the recurrence-free survival of patients with hepatocellular carcinoma treated with conventional transcatheter arterial chemoembolization. J Magn Reson Imaging (this issue) 2020. https://doi.org/10.1002/jmri.26977.
- 2Lee JS, Heo J, Libbrecht L, et al. A novel prognostic subtype of human hepatocellular carcinoma derived from hepatic progenitor cells. Nat Med 2006; 12: 410-416.
- 3Hoshida Y, Toffanin S, Lachenmayer A, Villanueva A, Minguez B, Llovet JM. Molecular classification and novel targets in hepatocellular carcinoma: Recent advancements. Semin Liver Dis 2010; 30: 35-51.
- 4Llovet JM, Brú C, Bruix J. Prognosis of hepatocellular carcinoma: The BCLC staging classification. Semin Liver Dis 1999; 19(3): 329-338.
- 5American College of Radiology Liver Imaging Reporting and Data System version 2018 core. Available at: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/LI-RADS.
- 6Segal E, Sirlin CB, Ooi C, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 2007; 25: 675-680.
- 7Zhou W, Zhang L, Wang K, et al. Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging 2017; 45: 1476-1484.
- 8Zhang R, Xu L, Wen X, et al. A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Quant Imaging Med Surg 2019; 9(9): 1503-1515.
- 9Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14: 749-762.
- 10Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning: A primer for radiologists. Radiographics 2017; 37: 2113-2131.