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Automatic lung dose painting for functional lung avoidance radiotherapy through multi-modality-guided dose

Tianyu Xiong1, Guangping Zeng1, Zhi Chen1

  • 1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China, People's Republic of China.

Physics in Medicine and Biology
|December 29, 2025
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Summary

A novel auto-planning algorithm for Functional Lung Avoidance Radiotherapy (FLART) accurately predicts radiation dose using multi-modality imaging. This approach enhances planning efficiency and quality by leveraging lung function data.

Keywords:
automatic planningdeep learningdose predictionfunctional lung avoidance radiotherapylung cancer

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Medical Imaging

Background:

  • Functional Lung Avoidance Radiotherapy (FLART) aims to minimize radiation dose to healthy lung tissue.
  • Accurate prediction of dose distribution is crucial for effective FLART planning.
  • Current auto-planning methods may not fully utilize voxel-wise lung function information.

Purpose of the Study:

  • To develop a multi-modality-guided dose prediction (MMDP)-based auto-planning algorithm for FLART.
  • To leverage voxel-wise lung function images for enhanced dose prediction and plan generation.
  • To improve the efficiency, consistency, and quality of FLART planning.

Main Methods:

  • Developed a novel MMDP model extracting complementary features from multi-modality images.
  • Implemented an instance-weighting anatomy-to-function training strategy to improve prediction accuracy.
  • Utilized a function-guided voxelwise dose mimicking algorithm to create MMDP-FLART plans.
  • Validated the algorithm on retrospective and prospective patient data with SPECT ventilation (V) and perfusion (Q) images.

Main Results:

  • MMDP achieved accurate dose predictions with median errors within ±1Gy/±1% for DVH metrics.
  • The MMDP model and training strategy significantly reduced prediction errors for functionally weighted mean lung dose (fMLD).
  • MMDP-FLART plans demonstrated significant reductions in fMLD compared to conventional radiotherapy (ConvRT) plans.
  • MMDP-FLART plans showed comparable or lower fMLD than manual FLART plans, with reduced dose to organs at risk.

Conclusions:

  • The MMDP model with instance-weighting anatomy-to-function training enables accurate dose prediction for FLART.
  • The MMDP-based auto-planning algorithm effectively generates FLART plans using voxel-wise lung function data.
  • This approach shows potential to enhance FLART planning efficiency, consistency, and overall quality.