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Predictive dose accumulation for HN adaptive radiotherapy.

Donghoon Lee1, Pengpeng Zhang1, Saad Nadeem1

  • 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center New York, NY, United States of America.

Physics in Medicine and Biology
|October 2, 2020
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Summary
This summary is machine-generated.

This study developed a deep neural network to predict head and neck cancer parotid gland changes during radiation therapy. The model accurately forecasts anatomical shifts and dose uncertainties, aiding adaptive radiotherapy decisions.

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Medical Imaging

Background:

  • Parotid gland (PG) shape and volume changes during head and neck (HN) cancer radiation therapy (RT) can cause significant dose deviations.
  • Longitudinal prediction of these anatomical changes is crucial for timely adaptive radiotherapy (ART).

Purpose of the Study:

  • To develop and evaluate a deep neural network for predicting longitudinal parotid gland anatomical changes and associated dose uncertainties during HN cancer RT.
  • To assess the model's accuracy in identifying candidates for ART.

Main Methods:

  • A deep neural network was developed using displacement fields (DFs) from deformable image registration (DIR) between planning CT (pCT) and weekly CBCT.
  • Sixty-three HN cancer patients undergoing volumetric modulated arc therapy were retrospectively analyzed.
  • The network predicted DFs for weeks 4-6, generating predicted PG contours and dose distributions for ART evaluation.

Main Results:

  • The prediction model achieved Dice Similarity Coefficients (DICE) of 0.78-0.81 for PG contours.
  • Predicted accumulated doses demonstrated feasibility in quantifying dose uncertainty due to anatomical changes.
  • The model showed high accuracy (AUC > 0.90) in detecting ART candidates based on dose difference criteria.

Conclusions:

  • The proposed deep neural network accurately predicts future parotid gland anatomical changes and dose uncertainties during RT.
  • The model is suitable for integration into adaptive radiotherapy workflows for improved treatment precision.