ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Thyroid Endocrinology
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1618902
This article is part of the Research TopicRadiomics and Artificial Intelligence in Oncology ImagingView all 16 articles
Application and Validation of the Machine Learning-Based Multimodal Radiomics Model for Preoperative Prediction of Lateral Lymph Node Metastasis in Papillary Thyroid Carcinoma
Provisionally accepted
- 1First People's Hospital of Changzhou, Changzhou, China
- 2Second Clinical Medical School, Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
- 3Suzhou Municipal Hospital, Suzhou, Jiangsu Province, China
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Background: Papillary thyroid carcinoma (PTC) frequently develops lateral lymph node metastasis (LLNM) in 12.6%-32.8% of patients, increasing recurrence risk and mortality. Current diagnostic methods show significant limitations, with occult LLNM rates of 41.0%-51.7% requiring secondary surgeries. This study aims to develop and validate a multimodal prediction model integrating clinical, ultrasound, and CT radiomics features for accurate preoperative LLNM prediction in PTC patients. Methods: Clinical data, ultrasound and CT images from 799 PTC patients were retrospectively analyzed (524 training, 225 internal validation, 50 external validation). Clinical features were selected through logistic regression after collinearity analysis. A total of 874 ultrasound radiomics features and 1433 CT radiomics features were extracted and selected using LASSO regression. Four machine learning models were constructed and compared, with model interpretability explored using SHAP and LIME analyses. Results: Logistic regression identified five independent clinical risk factors: maximum tumor diameter, multiple lesions, upper pole location, decreased monocyte count, and lower lymphocyte-to-monocyte
Keywords: Papillary thyroid carcinoma, Lateral lymph node metastasis, Radiomics, Multimodal prediction, machine learning
Received: 27 Apr 2025; Accepted: 30 Jul 2025.
Copyright: © 2025 Feng, Yang, Qin, Liu, Qin and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
An-Cheng Qin, Suzhou Municipal Hospital, Suzhou, Jiangsu Province, China
Yong Jiang, First People's Hospital of Changzhou, Changzhou, China
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