AI Article Synopsis
- - The prediction of accurate lymph node clinical target volumes for nasopharyngeal carcinoma radiotherapy is challenging due to contour variability and limited data sharing among experts.
- - To address this, researchers created a dataset of 262 subjects and 440 CT images from various stages and treatment strategies to develop deep learning models for lymph node segmentation.
- - The study manually delineated basic lymph node CTVs using input from six clinical experts, evaluated several algorithm performances, and established a multicenter dataset to assist in future automatic lymph node delineation research.
Article Abstract
The deep learning (DL)-based prediction of accurate lymph node (LN) clinical target volumes (CTVs) for nasopharyngeal carcinoma (NPC) radiotherapy (RT) remains challenging. One of the main reasons is the variability of contours despite standardization processes by expert guidelines in combination with scarce data sharing in the community. Therefore, we retrospectively generated a 262-subjects dataset from four centers to develop the DL models for LN CTVs delineation. This dataset included 440 computed tomography images from different scanning phases, disease stages and treatment strategies. Three clinical expert boards, each comprising two experts (totalling six experts), manually delineated six basic LN CTVs on separate cohorts as the ground truth according to LN involvement and clinical requirements. Several state-of-the-art segmentation algorithms were evaluated on this benchmark, showing promising results for LN CTV segmentation. In conclusion, this work built a multicenter LN CTV segmentation dataset, which may be the first dataset for automatic LN CTV delineation development and evaluation, serving as a benchmark for future research.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452638 | PMC |
| http://dx.doi.org/10.1038/s41597-024-03890-0 | DOI Listing |
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