Faculty, Staff and Student Publications
Publication Date
8-5-2025
Journal
Scientific Reports
DOI
10.1038/s41598-025-13601-3
PMID
40764730
PMCID
PMC12325674
PubMedCentral® Posted Date
8-5-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Radiotherapy is the main treatment modality of oropharyngeal cancer (OPC), in which an accurate segmentation of primary gross tumor volume (GTVt) is essential but also challenging due to significant interobserver variability and the time consumed in manual tumor delineation. For such a challenge an interactive deep learning (DL) based approach offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we investigate an interactive DL for GTVt segmentation in OPC by introducing a novel two-stage Interactive Click Refinement (2S-ICR) framework and implementing state-of-the-art algorithms. Using the 2021 HEad and neCK TumOR dataset for development and an external dataset from The University of Texas MD Anderson Cancer Center for evaluation, the 2S-ICR framework achieves a Dice similarity coefficient of 0.722 ± 0.142 without user interaction and 0.858 ± 0.050 after ten interactions, thus outperforming existing methods in both cases.
Keywords
Humans, Oropharyngeal Neoplasms, Tumor Burden, Imaging, Three-Dimensional, Deep Learning, Algorithms
Published Open-Access
yes
Recommended Citation
Saukkoriipi, Mikko; Sahlsten, Jaakko; Jaskari, Joel; et al., "Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer" (2025). Faculty, Staff and Student Publications. 4883.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/4883
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