The UK Government’s report on the copyright and AI consultation was recently published. While the report confirms that balancing the interests of copyrights holders and AI developers is a complex exercise, it also provides an indication of likely scenarios to consider in this fast-evolving environment. The consultation focused on whether AI developers should be permitted
Deep learning-based multimodal radiopathomics for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma – Scientific Reports
- Article
- Open access
- Published:
- Qiuyu Cai1 na1,
- Tianhong Gao2 na1,
- Linyun Zhou2,
- Shengxuming Zhang2,
- Yi Chen3,
- Xianfa Xu4,
- Tian-An Jiang5,
- Jing Zhang1,
- Xiuming Zhang1,
- Zunlei Feng2 &
- …
- Qihan You1
Scientific Reports , Article number: (2026) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
Subjects
Abstract
Papillary thyroid carcinoma (PTC) exhibits a high incidence and a strong propensity for lymph node metastasis (LNM). Accurate preoperative assessment of LNM is crucial for guiding surgical approaches, but conventional ultrasound exhibits suboptimal sensitivity. In this study, we developed a deep learning-based multimodal model to predict LNM in PTC patients. We retrospectively collected fine needle aspiration (FNA) liquid-based cytology specimens (N = 1095) and corresponding ultrasound images (N = 2190). A ResNet-101 architecture was trained using five-fold cross-validation and validated on external datasets from two independent centers. The multimodal model achieved strong predictive performance on the internal validation set (area under the curve, AUC: 0.891; accuracy: 0.821) and external validation set (AUC: 0.875; accuracy: 0.808). It outperformed models based solely on ultrasound or cytology images. Gradient-weighted class activation mapping revealed that nuclear features in FNA images were the most influential for LNM prediction. Our model achieved promising predictive performance and has the potential to guide clinical decision-making, potentially reducing unnecessary lymph node dissections in PTC patients.
Abbreviations
- LNM:
-
Lymph node metastasis
- PTC:
-
Papillary thyroid carcinoma
- FNA:
-
Fine needle aspiration
- Grad-CAM:
-
Gradient-weighted class activation mapping
- LND:
-
Lymph node dissection
- DNN:
-
Deep neural network
- CNNs:
-
Convolutional neural networks
- AI:
-
Artificial intelligence
- WSIs:
-
Whole slide images
- AUC:
-
Area under the curve
- AUCPR:
-
Area under the precision-recall curve
- LBC:
-
Liquid-based cytology
- CT:
-
Computed tomography
- MRI:
-
Magnetic resonance imaging
Funding
This work was supported by the National Natural Science Foundation of China (Grant/Award Number: 62376248, Zunlei Feng) and Natural Science Foundation of Zhejiang Province (Grant/Award Number: LQ20H160048 and LY21H160035, Xiuming Zhang).
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Cai, Q., Gao, T., Zhou, L. et al. Deep learning-based multimodal radiopathomics for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma. Sci Rep (2026). https://doi.org/10.1038/s41598-026-48693-y
Download citation
-
Received:
-
Accepted:
-
Published:
-
DOI: https://doi.org/10.1038/s41598-026-48693-y
Keywords
Related Posts
