Dynamic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma.

Area of Science:

• Oncology
• Surgical Oncology
• Medical Informatics

Background:

• Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer.
• Lateral lymph node metastasis (LLNM) is a significant prognostic factor in PTC.
• Accurate preoperative prediction of LLNM is crucial for effective treatment planning.

Purpose of the Study:

• To develop and validate a dynamic nomogram for predicting LLNM in PTC based on preoperative clinical data.
• To identify independent risk factors associated with LLNM in PTC.

Main Methods:

• Retrospective analysis of 477 patients with PTC from two centers.
• Identification of preoperative clinical factors influencing LLNM using univariable and multivariable analyses.
• Construction and validation of a predictive dynamic nomogram using ROC analysis and calibration curves.

Main Results:

• Independent risk factors for LLNM included male sex, tumor size ≥10.5 mm, thyroid nodules, irregular tumor shape, rich lymph node vascularity, and lymph node location.
• The dynamic nomogram demonstrated high predictive performance with an AUC of 0.956 in the training set and 0.915 in the validation set.
• The nomogram effectively identified high-risk patients for LLNM.

Conclusions:

• The developed dynamic nomogram provides a reliable tool for the preoperative prediction of LLNM in papillary thyroid carcinoma.
• This nomogram can assist surgeons in stratifying patients into high-risk and low-risk groups.
• Individualized treatment strategies can be developed based on the nomogram's predictions, potentially improving patient outcomes.