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Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma.

Publication ,  Journal Article
Ni, R; Zhou, T; Ren, G; Zhang, Y; Yang, D; Tam, VCW; Leung, WS; Ge, H; Lee, SWY; Cai, J
Published in: Int J Radiat Oncol Biol Phys
July 1, 2022

PURPOSE: Radiation dermatitis (RD) is a common, unpleasant side effect of patients receiving radiation therapy. In clinical practice, the severity of RD is graded manually through visual inspection, which is labor intensive and often leads to large interrater variations. To overcome these shortcomings, this study aimed to develop an automatic RD assessment based on deep learning (DL) techniques that could efficiently assist the RD severity classification in clinical application. METHODS AND MATERIALS: A total of 1205 photographs of the head and neck region were collected from patients with nasopharyngeal carcinoma (NPC) undergoing radiation therapy. The severity of RD in these photographs was graded by 5 qualified assessors based on the Radiation Therapy Oncology Group guidance. An end-to-end RD grading framework was developed by combining a DL-based segmentation network and a DL-based RD severity classifier, which are used for segmenting the neck region from the camera-captured photographs and grading, respectively. U-Net was used for segmentation and another convolutional neural network classifier (DenseNet-121) was applied to RD severity classification. Dice similarity coefficient was used to evaluate the performance of segmentation. Severity classification was evaluated by several metrics, including overall accuracy, precision, recall, and F1 score. RESULTS: Results of segmentation showed that the averaged dice similarity coefficients were 91.2% and 90.8% for front and side view, respectively. For RD severity classification, the overall accuracy of test photographs was 83.0%. Our method accurately classified 90.5% of grade 0, 67.2% of grade 1, 93.8% of grade 2, and 100% of above grade 2 cases. The overall prediction performance was comparable with human assessors. There was no significant difference in accuracy when using manually or automatically segmented regions (P = .683). CONCLUSIONS: We have successfully demonstrated a DL-based method for automatic assessment of RD severity in patients with NPC. This method holds great potential for efficient and effective assessing and monitoring of RD in patients with NPC.

Duke Scholars

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Published In

Int J Radiat Oncol Biol Phys

DOI

EISSN

1879-355X

Publication Date

July 1, 2022

Volume

113

Issue

3

Start / End Page

685 / 694

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Radiodermatitis
  • Organs at Risk
  • Oncology & Carcinogenesis
  • Nasopharyngeal Neoplasms
  • Nasopharyngeal Carcinoma
  • Image Processing, Computer-Assisted
  • Humans
  • Deep Learning
  • 5105 Medical and biological physics
 

Citation

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Ni, R., Zhou, T., Ren, G., Zhang, Y., Yang, D., Tam, V. C. W., … Cai, J. (2022). Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys, 113(3), 685–694. https://doi.org/10.1016/j.ijrobp.2022.03.011
Ni, Ruiyan, Ta Zhou, Ge Ren, Yuanpeng Zhang, Dongrong Yang, Victor C. W. Tam, Wan Shun Leung, Hong Ge, Shara W. Y. Lee, and Jing Cai. “Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma.Int J Radiat Oncol Biol Phys 113, no. 3 (July 1, 2022): 685–94. https://doi.org/10.1016/j.ijrobp.2022.03.011.
Ni R, Zhou T, Ren G, Zhang Y, Yang D, Tam VCW, et al. Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys. 2022 Jul 1;113(3):685–94.
Ni, Ruiyan, et al. “Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma.Int J Radiat Oncol Biol Phys, vol. 113, no. 3, July 2022, pp. 685–94. Pubmed, doi:10.1016/j.ijrobp.2022.03.011.
Ni R, Zhou T, Ren G, Zhang Y, Yang D, Tam VCW, Leung WS, Ge H, Lee SWY, Cai J. Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys. 2022 Jul 1;113(3):685–694.
Journal cover image

Published In

Int J Radiat Oncol Biol Phys

DOI

EISSN

1879-355X

Publication Date

July 1, 2022

Volume

113

Issue

3

Start / End Page

685 / 694

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Radiodermatitis
  • Organs at Risk
  • Oncology & Carcinogenesis
  • Nasopharyngeal Neoplasms
  • Nasopharyngeal Carcinoma
  • Image Processing, Computer-Assisted
  • Humans
  • Deep Learning
  • 5105 Medical and biological physics