Gross tumor volume confidence maps prediction for soft tissue sarcomas from multi-modality medical images using a diffusion model.

ABSTRACT:

Background and purpose

Accurate delineation of the gross tumor volume (GTV) is essential for radiotherapy of soft tissue sarcomas. However, manual GTV delineation from multi-modality images is time-consuming. Furthermore, GTV delineation is subject to inter- and intra-reader variability, which reduces the reproducibility of treatment planning. To address these issues, this work aims to develop a highly accurate automatic delineation technique modeling reader variability for soft tissue sarcomas using deep learning.

Materials and methods

We employed a publicly available soft tissue sarcoma dataset consisting of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET), X-ray Computed Tomography (CT), and pre-contrast T1-weighted Magnetic Resonance Imaging (MRI) scans for 51 p

SUBMITTER: Dong Y 

PROVIDER: S-EPMC11926426 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature