A Predictive Model for Lateral Cervical Lymph Node Metastasis in Thyroid Cancer

Shu Wang; Yanqiang Zhang; Ziyue Fu; Chuanlu Shen; Siyu Jia; Shugang Zhao; Kaile Wu

doi:10.71321/5r3ppj45

Authors

Shu Wang The First Affiliated Hospital of Anhui Medical University
Yanqiang Zhang The First Affiliated Hospital of Anhui Medical University
Ziyue Fu The First Affiliated Hospital of Anhui Medical University
Chuanlu Shen The First Affiliated Hospital of Anhui Medical University
Siyu Jia Hefei Cancer Hospital, Chinese Academy of Sciences
Shugang Zhao Hefei Cancer Hospital, Chinese Academy of Sciences
Kaile Wu The First Affiliated Hospital of Anhui Medical University

DOI:

https://doi.org/10.71321/5r3ppj45

Keywords:

Papillary thyroid carcinoma, Lateral lymph node metastasis, Machine learning, Clinical prediction model, SHapley Additive exPlanations (SHAP)

Abstract

Objective: To investigate the predictive factors for lateral lymph node metastasis (LLNM) in patients with papillary thyroid carcinoma (PTC) and to develop an individualized prediction model.
Methods: Clinical data from 241 PTC patients who underwent lateral neck dissection were analyzed. Logistic regression and machine learning methods were employed to identify predictive factors and construct a model. The predictive value of three-dimensional morphological parameters (total tumor surface area and total tumor volume) was also evaluated.
Results: Maximum tumor diameter, central lymph node metastasis, preoperative Thyroid-Stimulating Hormone (TSH), and tumor location were identified as independent predictors of LLNM. A baseline combined model based on maximum tumor diameter showed good predictive performance (AUC 0.832). Furthermore, three-dimensional parameters (total surface area and total volume) demonstrated complementary predictive potential compared to the baseline model.
Conclusion: An effective clinical prediction model for assessing LLNM risk was successfully developed. Three-dimensional morphological parameters represent promising predictive indicators with potential complementary value.

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