Editorial for "Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma"
- PMID: 37165916
- DOI: 10.1002/jmri.28775
Editorial for "Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma"
Comment on
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Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma.J Magn Reson Imaging. 2024 Jan;59(1):108-119. doi: 10.1002/jmri.28745. Epub 2023 Apr 20. J Magn Reson Imaging. 2024. PMID: 37078470
References
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- Lee S, Kim YY, Shin J, et al. Percentages of hepatocellular carcinoma in LI-RADS categories with CT and MRI: A systematic review and meta-analysis. Radiology 2023;307(1):e220646.
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- Marrero JA, Kulik LM, Sirlin CB, et al. Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American Association for the Study of Liver Diseases. Hepatology 2018;68(2):723-750.
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- Dong X, Yang J, Zhang B, et al. Deep learning radiomics model of dynamic contrast-enhanced MRI for evaluating vessels encapsulating tumor clusters and prognosis in hepatocellular carcinoma. J Magn Reson Imaging 2024;59(1):108-119.
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- Fang JH, Xu L, Shang LR, et al. Vessels that encapsulate tumor clusters (VETC) pattern is a predictor of sorafenib benefit in patients with hepatocellular carcinoma. Hepatology 2019;70(3):824-839.
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