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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Head and Neck Cancer
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1457660

Interpretable Machine Learning Models for Predicting Skip Metastasis in cN0 Papillary Thyroid Cancer Based on Clinicopathological and Elastography Radiomics Features

Provisionally accepted
Xiaohua Yao Xiaohua Yao 1*Mingming Tang Mingming Tang 2*Min Lu Min Lu 1*Jie Zhou Jie Zhou 1*Debin Yang Debin Yang 1*
  • 1 Department of Ultrasound, Jiading District Central Hospital, Shanghai, China
  • 2 Department of Endocrinology, Jiading District Central Hospital, Shanghai, China

The final, formatted version of the article will be published soon.

    Background: Skip lymph node metastasis (SLNM) in papillary thyroid cancer (PTC) involves cancer cells bypassing central nodes to directly metastasize to lateral nodes, often undetected by standard preoperative ultrasonography. Although multiple models exist to identify SLNM, they are inadequate for clinically node-negative (cN0) patients, resulting in underestimated metastatic risks and compromised treatment effectiveness. Our study aims to develop and validate a machine learning (ML) model that combines elastography radiomics with clinicopathological data to predict pre-surgical SLNM risk in cN0 PTC patients with increased risk of lymph node metastasis (LNM), improving their treatment strategies. Methods: Our study conducted a retrospective analysis of 485 newly diagnosed primary PTC patients, divided into training and external validation cohorts. Patients were categorized into SLNM and non-SLNM groups based on follow-up outcomes and postoperative pathology. We collected preoperative clinicopathological data and extracted, standardized radiomics features from elastography imaging to develop various ML models. These models were internally validated using radiomics and clinicopathological data, with the optimal model’s feature importance analyzed through the Shapley Additive Explanations (SHAP) approach and subsequently externally validated. Results: In our study of 485 patients, 67 (13.8%) exhibited SLNM. The extreme gradient boosting (XGBoost) model, integrating elastography radiomics with clinicopathological data, demonstrated superior performance in both internal and external validations. SHAP analysis identified five key determinants of SLNM: three radiomics features from elastography images, one clinical variable, and one pathological variable. Conclusion: Our evaluation highlights the XGBoost model, which integrates elastography radiomics and clinicopathological data, as the most effective ML approach for the prediction of SLNM in cN0 PTC patients with increased risk of LNM. This innovative model significantly enhances the accuracy of risk assessments for SLNM, enabling personalized treatments that could reduce postoperative metastases in these patients.

    Keywords: Papillary thyroid cancer, machine learning, clinically node-negative (cN0), Skip lymph node metastasis, Radiomics

    Received: 01 Jul 2024; Accepted: 10 Dec 2024.

    Copyright: © 2024 Yao, Tang, Lu, Zhou and Yang. 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:
    Xiaohua Yao, Department of Ultrasound, Jiading District Central Hospital, Shanghai, China
    Mingming Tang, Department of Endocrinology, Jiading District Central Hospital, Shanghai, China
    Min Lu, Department of Ultrasound, Jiading District Central Hospital, Shanghai, China
    Jie Zhou, Department of Ultrasound, Jiading District Central Hospital, Shanghai, China
    Debin Yang, Department of Ultrasound, Jiading District Central Hospital, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.