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Modeling inter-reader variability in clinical target volume delineation for soft tissue sarcomas using diffusion

Yafei Dong^1,2, Thibault Marin^1,2,3, Yue Zhuo^1,2

¹Yale Biomedical Imaging Institute, Yale University School of Medicine, New Haven, Connecticut, USA.

Medical Physics

|May 3, 2025

Summary

This summary is machine-generated.

This study introduces a diffusion model to generate multiple clinical target volume (CTV) contours for soft tissue sarcomas, mimicking real-world inter-reader variability. This deep learning approach enhances radiotherapy treatment planning by accounting for contouring uncertainties.

Keywords:

clinical target volume deep learning diffusion model gross tumor volume sarcoma

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Area of Science:

Medical Imaging
Radiotherapy
Deep Learning

Background:

Accurate clinical target volume (CTV) delineation is crucial for soft tissue sarcoma radiotherapy.
Inter-reader variability in CTV contouring leads to inconsistencies in treatment planning.
Existing automatic methods fail to capture this variability, generating only single CTVs.

Purpose of the Study:

To develop a deep learning technique for generating multiple CTV contours per case.
To simulate the inter-reader variability observed in clinical practice.
To improve the consistency and accuracy of radiotherapy treatment planning.

Main Methods:

Utilized a dataset of multi-modality scans (FDG-PET, CT, MRI) from 51 soft tissue sarcoma patients.
Developed a diffusion model incorporating a GTV encoder to extract critical tumor information.
Generated multiple plausible CTVs to mimic inter-reader variability.

Main Results:

The diffusion model achieved superior performance, with the highest Dice Index (0.902) and best Generalized Energy Distance (GED) (0.209).
Demonstrated strong recall and precision metrics among ambiguous image segmentation models.
Ablation studies confirmed the importance of GTV information for accurate CTV delineation.

Conclusions:

The proposed diffusion model effectively generates multiple, plausible CTV contours for soft tissue sarcomas.
The method successfully captures and simulates inter-reader variability in CTV delineation.
This approach has the potential to enhance radiotherapy treatment planning for soft tissue sarcomas.