A nomogram as a predictive tool for lymph node metastasis in papillary thyroid carcinoma.
Yimeng Liu1, Tianxiang Liu2, Yi Long2
1Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Basic and Translational Medicine on Head & Neck Cancer, Tianjin, China.
Frontiers in Endocrinology
|July 1, 2026
Summary
This study developed a predictive model for lymph node metastasis in papillary thyroid cancer. The nomogram aids clinicians in assessing metastasis risk, improving patient care.
Area of Science:
- Oncology
- Medical Statistics
- Predictive Modeling
Background:
- Lymph node metastasis is a common spread pattern in thyroid cancer.
- Current methods for predicting metastasis are insufficient for clinical practice.
Purpose of the Study:
- To develop and validate a predictive nomogram for lymph node metastasis in papillary thyroid cancer.
- To identify key predictors, including emotional factors, for lymph node metastasis.
Main Methods:
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression for variable selection.
- Constructed a nomogram using nine selected variables from 484 patient cases.
- Assessed model performance using ROC curve analysis, calibration curves, and decision curves.
Main Results:
- The developed nomogram demonstrated strong predictive performance.
- The area under the curve (AUC) was 0.8045 for the training set and 0.8146 for the verification set.
- The model showed excellent discrimination and calibration for predicting lymph node metastasis.
Conclusions:
- The nomogram provides a valuable tool for predicting lymph node metastasis in papillary thyroid cancer.
- This model can assist in clinical decision-making and potentially guide treatment strategies.
