Objective To develop and validate a predictive model for lymph node metastasis (LNM) in thyroid microcarcinoma (TMC) based on clinical, ultrasonographic, and radiomic features, providing a basis for accurate preoperative risk assessment and individualized surgical planning. Methods A total of 426 TMC patients treated at our institution from June 2022 to December 2024 were retrospectively enrolled and randomly divided into a training set (n = 300) and a validation set (n = 126) at a 7:3 ratio. Demographic characteristics (age, gender), preoperative clinical and ultrasonographic features (tumor size measured by ultrasound, multifocality assessed by ultrasound, capsular invasion on ultrasound, etc.), ultrasound features (lymph node size, sphericity, etc.), laboratory indicators (preoperative TSH level), and radiomic parameters (3D tumor volume, surface area, etc.) were collected. In the training set, univariate analysis was performed to screen LNM-associated factors, followed by LASSO regression for variable selection. Multivariate logistic regression was used to identify independent predictors. Random forest (RF), K-nearest neighbors (KNN), and gradient boosting (GB) models were constructed. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) in the training and internal validation sets. Results No significant differences in baseline characteristics were observed between the training and validation sets (P > 0.05). Univariate analysis revealed that tumor size, lymph node size, TSH level, central lymph node metastasis, 3D tumor volume, and sphericity were associated with LNM (P < 0.05). Multivariate logistic regression identified tumor size, lymph node size, TSH level, central lymph node metastasis, and 3D tumor volume as independent risk factors for LNM (P < 0.05), while sphericity was an independent protective factor (P < 0.05). The RF model exhibited superior performance (training AUC: 0.838, internal validation AUC: 0.815) compared to KNN (0.815, 0.792) and GB (0.787, 0.763), demonstrating good calibration and stable clinical net benefit. Conclusion The RF model constructed using multidimensional features effectively predicts LNM in TMC, with TSH level, tumor size, and sphericity as key predictors, demonstrating high clinical utility.

Radiomics-based prediction model for lymph node metastasis in thyroid microcarcinoma

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2026年5月19日

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Yifeng Yang,
Anlong Yuan,
Cuicui Huang

摘要

Objective

To develop and validate a predictive model for lymph node metastasis (LNM) in thyroid microcarcinoma (TMC) based on clinical, ultrasonographic, and radiomic features, providing a basis for accurate preoperative risk assessment and individualized surgical planning.

Methods

A total of 426 TMC patients treated at our institution from June 2022 to December 2024 were retrospectively enrolled and randomly divided into a training set (n = 300) and a validation set (n = 126) at a 7:3 ratio. Demographic characteristics (age, gender), preoperative clinical and ultrasonographic features (tumor size measured by ultrasound, multifocality assessed by ultrasound, capsular invasion on ultrasound, etc.), ultrasound features (lymph node size, sphericity, etc.), laboratory indicators (preoperative TSH level), and radiomic parameters (3D tumor volume, surface area, etc.) were collected. In the training set, univariate analysis was performed to screen LNM-associated factors, followed by LASSO regression for variable selection. Multivariate logistic regression was used to identify independent predictors. Random forest (RF), K-nearest neighbors (KNN), and gradient boosting (GB) models were constructed. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) in the training and internal validation sets.

Results

No significant differences in baseline characteristics were observed between the training and validation sets (P > 0.05). Univariate analysis revealed that tumor size, lymph node size, TSH level, central lymph node metastasis, 3D tumor volume, and sphericity were associated with LNM (P < 0.05). Multivariate logistic regression identified tumor size, lymph node size, TSH level, central lymph node metastasis, and 3D tumor volume as independent risk factors for LNM (P < 0.05), while sphericity was an independent protective factor (P < 0.05). The RF model exhibited superior performance (training AUC: 0.838, internal validation AUC: 0.815) compared to KNN (0.815, 0.792) and GB (0.787, 0.763), demonstrating good calibration and stable clinical net benefit.

Conclusion

The RF model constructed using multidimensional features effectively predicts LNM in TMC, with TSH level, tumor size, and sphericity as key predictors, demonstrating high clinical utility.