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

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.

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 patients, of which 49 were selected for analysis. The GTVs were delineated by six experienced readers, each reader performing GTV contouring multiple times for every patient. The confidence maps were calculated by averaging the labels provided by all readers, resulting in values ranging from 0 to 1. We developed and trained a diffusion model-based neural network to predict confidence maps of GTV for soft tissue sarcomas from multi-modality medical images.

Quantitative analysis showed that the proposed diffusion model performed competitively with U-Net-based models, frequently ranking first or second across five evaluation metrics: Dice Index, Hausdorff Distance, Recall, Precision, and Brier Score. Additionally, experiments evaluating the impact of different imaging modalities demonstrated that incorporating multi-modality image inputs provided improved performance compared to single-modality and dual-modality inputs.

The proposed diffusion model is capable of predicting accurate confidence maps of GTV for soft tissue sarcomas from multi-modality inputs.