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Predicting anatomical variations in radiotherapy with a vector quantized variational autoencoder generative model.

Yue Zou1,2,3, Zhenhao Li1,2,3, Menghan Zhang1,2,3

  • 1Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.

Medical Physics
|September 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel generative model using vector quantized variational autoencoder (VQ-VAE) to predict anatomical changes in nasopharyngeal cancer patients undergoing radiotherapy. The VQ-VAE model accurately forecasts daily anatomical variations, aiding adaptive radiotherapy decisions.

Keywords:
adaptive radiotherapyanatomical changesdeep learningnasopharyngeal cancer

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Anatomical variations during radiotherapy can alter radiation delivery.
  • Predicting these changes is crucial for effective adaptive radiotherapy (ART) in nasopharyngeal cancer (NPC).

Purpose of the Study:

  • To develop and evaluate a vector quantized variational autoencoder (VQ-VAE) based generative model for predicting anatomical changes in NPC patients.

Main Methods:

  • The proposed model integrates VQ-VAE with Adaptive Instance Normalization (AdaIN).
  • A convolutional neural network (CNN) extracts latent codes from planning CT images to capture anatomical variations.
  • AdaIN modulates the VQ-VAE latent space to generate daily CT images reflecting anatomical changes.
  • The model was trained on 522 CT images from 90 NPC patients and tested on 102 CT images from 18 patients.

Main Results:

  • Generated daily CT images showed no significant differences in organ-at-risk (OAR) volume distributions compared to actual images at the individual patient level.
  • Population-level predicted mean ROI volumes closely matched ground truth values and outperformed the previous Daily Anatomy Model (DAM).
  • High Pearson correlation coefficients (0.87-0.93) were observed between actual and generated daily CT ROI volumes for parotid and submandibular glands.

Conclusions:

  • The VQ-VAE model demonstrates efficacy in predicting anatomical changes during radiotherapy based on planning CT scans.
  • This predictive capability holds significant potential for informing adaptive decision-making in radiotherapy for nasopharyngeal cancer.