Predicting lymph node metastasis in thyroid cancer: systematic review and meta-analysis on the CT/MRI-based radiomics and deep learning models.

Abstract

Authors

• Parya Valizadeh
  Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.).
• Payam Jannatdoust
  Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.).
• Delaram J Ghadimi
  Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran.
• Sara Bagherieh
  School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
• Amir Hassankhani
  Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (A.H., M.A.). Electronic address: ah_620@usc.edu.
• Melika Amoukhteh
  Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (A.H., M.A.).
• Paniz Adli
  College of Letters and Science, University of California, Berkeley, CA, USA.
• Ali Gholamrezanezhad
  Department of Radiology, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA.

Keywords

External Resources