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Related Experiment Video

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Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

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Published on: June 7, 2015

Multi-task learning for automated contouring and dose prediction in radiotherapy.

Sangwook Kim^1,2, Aly Khalifa¹, Thomas G Purdie^1,3,4,5

¹Department of Medical Biophysics, University of Toronto, Toronto, Canada.

Physics in Medicine and Biology

|February 4, 2025

Summary

This study integrates automated contouring and dose prediction in radiotherapy using multi-task learning (MTL). The MTL approach enhances treatment planning efficiency and accuracy for prostate and head and neck cancers.

Keywords:

automated contouring automated treatment planning deep learning machine learning multi-task learning

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Area of Science:

Medical Physics
Radiotherapy
Artificial Intelligence

Background:

Deep learning (DL) improves radiotherapy efficiency and accuracy.
Current DL methods treat automated contouring and treatment planning as separate tasks.
Contouring and dose prediction in DL are often performed independently.

Purpose of the Study:

To integrate automated contouring and voxel-based dose prediction using multi-task learning (MTL).
To leverage common information between tasks for increased efficiency.
To improve automated radiotherapy treatment planning.

Main Methods:

Applied a multi-task learning (MTL) framework.
Integrated automated contouring and voxel-based dose prediction.
Utilized in-house prostate cancer and OpenKBP head and neck cancer datasets.

Main Results:

MTL improved dose volume histogram metrics by 19.82% (prostate) and 16.33% (head and neck) compared to sequential DL.
MTL enhanced dose prediction while maintaining/improving contouring accuracy.
MTL achieved higher Dice scores (0.824 prostate, 0.716 head and neck) than baseline contouring.

Conclusions:

The proposed MTL approach effectively integrates contouring and dose prediction.
MTL supports the development of efficient and accurate automated radiotherapy treatment planning.
This method shows significant potential for clinical application.