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A hybrid optimization strategy for deliverable intensity-modulated radiotherapy plan generation using deep

Zihan Sun^1,2,3, Xiang Xia^1,2,3, Jiawei Fan^1,2,3

¹Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.

Medical Physics

|January 19, 2022

Summary

This study introduces an automatic radiotherapy planning solution using deep learning and voxel-based optimization for improved treatment plans. The hybrid approach demonstrates clinical feasibility and generates acceptable plans efficiently.

Keywords:

IMRT autoplanning NPC and rectal cancer deep learning hybrid objective function voxel-based plan optimization

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Area of Science:

Radiation Oncology
Medical Physics
Artificial Intelligence in Medicine

Background:

External beam intensity-modulated radiotherapy (IMRT) planning is complex and time-consuming.
Automatic planning solutions are needed to improve efficiency and consistency in IMRT.
Deep learning and novel optimization strategies offer potential for automated treatment planning.

Purpose of the Study:

To develop and evaluate a clinically feasible automatic planning solution for external beam intensity-modulated radiotherapy.
To integrate deep learning for dose prediction with a voxel-based optimization strategy.
To assess the performance of a hybrid optimization approach for treatment planning.

Main Methods:

A U-Net-based deep learning network was used for dose prediction based on patient anatomy.
A voxel-based optimization strategy divided body voxels into planning target volume (PTV) and non-PTV regions.
A hybrid strategy combined voxel-based optimization with fixed dose-volume objectives for individualized planning.
Plans were generated for nasopharyngeal cancer (NPC) and rectal cancer patients and compared to clinical plans.

Main Results:

The hybrid strategy reduced deviations in homogeneity and conformity indices for the PTV compared to manual and voxel-only plans.
Automatic plan generation, including optimization, was completed within 1 minute per patient.
Similar dose-volume histogram (DVH) distributions were observed between predicted and clinical plans, with some differences in organs at risk.
Generated plans were deliverable on a clinical linear accelerator (uRT-linac 506c).

Conclusions:

A voxel-based optimization strategy integrated into a commercial treatment planning system (TPS) can generate deliverable radiotherapy plans.
The proposed hybrid optimization method is clinically feasible and generates acceptable IMRT plans.
Automatic planning solutions incorporating deep learning and voxel-based optimization show promise for efficient and effective radiotherapy treatment.