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Updated: Mar 29, 2026

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

Multi-Patient Vision Transformer for Markerless Tumor Motion Forecasting.

Gauthier Rotsart de Hertaing¹, Dani Manjah¹, Benoît Macq¹

¹Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université Catholique de Louvain, Place de l'Université 1, 1348 Louvain-la-Neuve, Belgium.

|March 28, 2026

Summary

This summary is machine-generated.

This study uses Vision Transformers for accurate lung tumor motion forecasting in radiotherapy. Fine-tuning a multi-patient model with limited data offers an efficient strategy for personalized predictions.

Keywords:

digitally reconstructed radiographs lung tumor forecasting markerless tracking real-time tumor tracking vision transformer

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Published on: August 22, 2025

Area of Science:

Medical Imaging
Artificial Intelligence in Oncology
Radiotherapy Physics

Background:

Accurate lung tumor motion forecasting is critical for effective radiotherapy delivery.
Markerless tracking methods using deep learning show promise but lack predictive capabilities for future tumor trajectories.
This research addresses the need for predicting lung tumor motion using advanced AI techniques.

Purpose of the Study:

To develop and evaluate a Vision Transformer-based approach for short-term, three-dimensional lung tumor motion forecasting.
To compare the performance of multi-patient and patient-specific models for tumor motion prediction.
To investigate the efficacy of fine-tuning a general model with limited patient-specific data.

Main Methods:

A Vision Transformer model was trained on digitally reconstructed radiographs (DRRs) from 4D CT scans of 12 lung cancer patients.
Multi-patient (MP) and patient-specific (PS) models were trained and compared.
MP models were fine-tuned with limited patient-specific treatment images under clinical constraints.

Main Results:

Low-resolution inputs with larger patch sizes yielded better forecasting performance by reducing image noise.
Patient-specific models required substantial data to achieve performance comparable to multi-patient models.
Fine-tuning the multi-patient model with minimal patient-specific data achieved high forecasting accuracy cost-effectively.

Conclusions:

Vision Transformers effectively extend markerless tracking for accurate short-term lung tumor motion forecasting.
Model fine-tuning presents an efficient and effective strategy for personalized radiotherapy predictions.
This approach enhances precision in radiotherapy by enabling better prediction of tumor movement.