Editorial for “deep learning radiomics model of dynamic contrast-enhanced MRI for evaluating vessels encapsulating tumor clusters and prognosis in hepatocellular carcinoma” Editorial
| Author: | Chernyak, V. |
| Title: | Editorial for “deep learning radiomics model of dynamic contrast-enhanced MRI for evaluating vessels encapsulating tumor clusters and prognosis in hepatocellular carcinoma” |
| Keywords: | immunohistochemistry; cancer survival; treatment outcome; excision; overall survival; sorafenib; hepatitis b; liver cell carcinoma; clinical practice; cd34 antigen; metastasis; clinical assessment; editorial; validation study; prediction; health care utilization; artificial intelligence; cancer cell; clinical evaluation; cell migration; stroma; high risk population; radiofrequency ablation; dynamic contrast-enhanced magnetic resonance imaging; cell invasion; predictive value; recurrence free survival; epithelial mesenchymal transition; learning algorithm; cancer prognosis; gadoxetic acid; human; deep learning; radiomics; blood vessel wall |
| Journal Title: | Journal of Magnetic Resonance Imaging |
| Volume: | 59 |
| Issue: | 1 |
| ISSN: | 1053-1807 |
| Publisher: | Wiley Blackwell |
| Date Published: | 2024-01-01 |
| Start Page: | 120 |
| End Page: | 121 |
| Language: | English |
| DOI: | 10.1002/jmri.28775 |
| PUBMED: | 37165916 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Editorial -- Source: Scopus |
