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TransAnaNet: Transformer-based Anatomy Change Prediction Network for Head and Neck Cancer Patient Radiotherapy.

Meixu Chen¹, Kai Wang^1,2, Michael Dohopolski¹

¹Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235, USA.

|May 20, 2024

Summary

This summary is machine-generated.

A vision-transformer neural network accurately predicts anatomical changes in head and neck cancer patients during radiotherapy. This technology can help personalize adaptive radiotherapy (ART) for better treatment outcomes.

Keywords:

Adaptive radiation therapy anatomy change prediction head and neck cancers transformer network

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Area of Science:

Medical Imaging
Artificial Intelligence in Oncology
Radiotherapy Physics

Background:

Adaptive radiotherapy (ART) aims to mitigate dosimetric errors from anatomical changes during head and neck cancer (HNC) treatment.
Clinical implementation of ART is challenging due to patient variability and resource constraints.
Early identification of HNC patients likely to experience significant anatomical changes is crucial for optimizing treatment and resource allocation.

Purpose of the Study:

To evaluate the feasibility of a vision-transformer (ViT) neural network for predicting radiotherapy-induced anatomical changes in HNC patients.
To develop a predictive model for anatomical variations encountered during HNC radiotherapy.

Main Methods:

A UNet-style ViT network was developed using retrospective data from 121 HNC patients.
The model utilized planning CT, dose, early and late cone-beam CTs (CBCT01, CBCT21), and tumor/nodal volumes (GTVp, GTVn).
The network predicted the deformation vector field and the resulting anatomical changes, validated using image and volumetric similarity metrics.

Main Results:

The ViT model demonstrated high accuracy in predicting anatomical changes, with superior similarity to the actual CBCT21 compared to other models.
Achieved average Mean Square Error (MSE) of 0.009 and Structural Similarity Index (SSIM) of 0.933 between predicted and actual CBCT.
High Dice coefficients (0.972 for body, 0.792 for GTVp, 0.821 for GTVn) indicated accurate volumetric predictions.

Conclusions:

The proposed ViT-based method shows significant promise for predicting radiotherapy-induced anatomical changes in HNC.
This predictive capability can aid clinical decision-making for implementing adaptive radiotherapy (ART).
The approach has the potential to enhance the efficacy and efficiency of HNC radiotherapy.