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Transformer-Integrated Hybrid Convolutional Neural Network for Dose Prediction in Nasopharyngeal Carcinoma

Xiangchen Li1, Yanhua Liu2, Feixiang Zhao3

  • 1College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, 610059, China.

Journal of Imaging Informatics in Medicine
|October 18, 2024
PubMed
Summary

This study introduces a new hybrid deep learning model for faster and more accurate radiotherapy dose prediction in nasopharyngeal carcinoma treatment. The novel approach improves plan quality and efficiency for clinical use.

Keywords:
Attention mechanismDeep learningDose predictionRadiotherapy

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Area of Science:

  • Medical Physics
  • Artificial Intelligence in Medicine
  • Radiotherapy Planning

Background:

  • Radiotherapy is the primary treatment for nasopharyngeal carcinoma.
  • Accurate dose prediction is crucial for efficient radiotherapy planning.
  • Existing deep learning models (CNNs, Transformers) have limitations in capturing spatial and long-distance information.

Purpose of the Study:

  • To develop a novel hybrid Convolutional Neural Network (CNN) and Transformer model for improved radiotherapy dose prediction.
  • To enhance feature transmission and preserve spatial information in dose prediction models.
  • To improve the efficiency and quality of radiotherapy treatment planning.

Main Methods:

  • Proposed a hybrid CNN-Transformer model incorporating a hierarchical dense recurrent encoder with channel attention.
  • Designed a progressive decoder for layer-wise reconstruction of feature maps.
  • Implemented object-driven skip connections to facilitate encoder-decoder information flow.

Main Results:

  • The proposed model outperformed baseline methods in key dosimetric criteria.
  • Image analysis metrics (PSNR, SSIM, NRMSE) indicated consistency with ground truth and superior visual quality.
  • Demonstrated significant improvements over advanced methods in dose prediction accuracy.

Conclusions:

  • The novel hybrid model offers superior performance for radiotherapy dose prediction.
  • This approach can serve as a valuable clinical tool for physicists, enhancing radiotherapy planning efficiency.
  • The model shows promise for improving treatment outcomes in nasopharyngeal carcinoma.