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A deep learning model for inter-fraction head and neck anatomical changes in proton therapy.

Tiberiu Burlacu1,2, Mischa Hoogeman1,3,2, Danny Lathouwers1,2

  • 1Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands.

Physics in Medicine and Biology
|February 25, 2025
PubMed
Summary

A deep learning algorithm accurately predicts anatomical changes during head and neck cancer radiotherapy. This daily anatomy model (DAM) generates realistic patient anatomy for improved treatment planning and robustness evaluation.

Keywords:
anatomy changesdeep learningproton therapyvariational autoencoder

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Area of Science:

  • Medical Imaging and Radiation Oncology
  • Artificial Intelligence in Healthcare
  • Computational Anatomy

Background:

  • Inter-fraction anatomical variations significantly impact radiotherapy accuracy in head and neck cancer patients.
  • Accurate prediction of these anatomical changes is crucial for adaptive radiotherapy and robust treatment planning.
  • Current methods for predicting anatomical changes often lack the precision required for real-time adaptation.

Purpose of the Study:

  • To evaluate a probabilistic deep learning algorithm, the daily anatomy model (DAMHN), for predicting inter-fraction anatomical variations.
  • To assess the model's performance in reconstructing patient anatomy and generating realistic anatomical changes during treatment.
  • To explore the utility of DAMHN in enhancing radiotherapy planning and robustness evaluation.

Main Methods:

  • Developed a daily anatomy model (DAMHN) using a variational autoencoder architecture to approximate conditional probability distributions.
  • Trained the model on 315 planning CT (pCT) and repeat CT (rCT) image pairs from 93 head and neck patients.
  • Assessed performance using DICE scores (0.83) and normalized cross-correlation (0.60) on a held-out test set.

Main Results:

  • The DAMHN achieved a DICE score of 0.83 and a normalized cross-correlation of 0.60, indicating high reconstruction accuracy.
  • Generated anatomical changes, including volume and center of mass shifts for organs like parotid glands and spinal cord, closely matched observed clinical data.
  • The model successfully replicated known anatomical shifts, such as medial parotid gland movement, aligning with literature findings.

Conclusions:

  • The probabilistic deep learning algorithm (DAMHN) effectively generates realistic anatomical variations encountered during head and neck radiotherapy.
  • This model shows significant potential for applications in robust optimization and library-based treatment planning.
  • DAMHN offers a valuable tool for improving treatment robustness and adaptability against inter-fractional anatomical changes.