Classification of parotid gland tumors by using multimodal MRI and deep learning

NMR Biomed. 2021 Jan;34(1):e4408. doi: 10.1002/nbm.4408. Epub 2020 Sep 4.


Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T2 -weighted, postcontrast T1 -weighted, and diffusion-weighted images. In this study, we present a fully automatic system for the diagnosis of parotid gland tumors by using deep learning methods trained on multimodal MRI images. We used a two-dimensional convolution neural network, U-Net, to segment and classify parotid gland tumors. The U-Net model was trained with transfer learning, and a specific design of the batch distribution optimized the model accuracy. We also selected five combinations of MRI contrasts as the input data of the neural network and compared the classification accuracy of parotid gland tumors. The results indicated that the deep learning model with diffusion-related parameters performed better than those with structural MR images. The performance results (n = 85) of the diffusion-based model were as follows: accuracy of 0.81, 0.76, and 0.71, sensitivity of 0.83, 0.63, and 0.33, and specificity of 0.80, 0.84, and 0.87 for Warthin tumors, pleomorphic adenomas, and malignant tumors, respectively. Combining diffusion-weighted and contrast-enhanced T1 -weighted images did not improve the prediction accuracy. In summary, the proposed deep learning model could classify Warthin tumor and pleomorphic adenoma tumor but not malignant tumor.

Keywords: MRI; deep learning; head and neck; parotid gland tumor; transfer learning.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Deep Learning*
  • Diffusion Magnetic Resonance Imaging
  • Female
  • Humans
  • Magnetic Resonance Imaging*
  • Male
  • Middle Aged
  • Multimodal Imaging
  • Parotid Gland / diagnostic imaging*
  • Parotid Gland / pathology*
  • Parotid Neoplasms / classification*
  • Parotid Neoplasms / diagnostic imaging*