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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Machine Learning: A Multicenter Study on Predicting Lateral Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma
Jing Zhou1,2, Daxue Li1,2, Jiahui Ren2
1Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 40016, China.
Background:
The necessity of prophylactic lateral neck dissection for cN0 papillary thyroid carcinoma (PTC) remains debated. This study aimed to compare traditional nomograms with machine learning (ML) models for predicting ipsilateral lateral and level II, III, and IV lymph node metastasis (LNM).
Methods:
Data from 1616 PTC patients diagnosed via fine-needle aspiration biopsy from hospital A were split into training and testing sets (7:3). Two hundred forty-three patients from hospital B served as a validation set. Four dependent variables-ipsilateral lateral and level II, III, and IV LNM-were analyzed. Eight ML models [logistic regression, decision tree, random forest (RF), gradient boosting, support vector machine, K-nearest neighbor, Gaussian naive Bayes, neural networks] were developed and validated using 10-fold cross-validation and grid search hyperparameter tuning. Models were assessed using 11 metrics including accuracy, area under the curve (AUC), specificity, and sensitivity. The best was compared with nomograms using the probability-based ranking model approach (PMRA).
Results:
RF outperformed other approaches achieving accuracy, AUC, specificity, and sensitivity of 0.773/0.728, 0.858/0.799, 0.984/0.935, 0.757/0.807 in the testing/validation sets, respectively, for ipsilateral LLNM. A streamlined model based on the top 10 contributing features that includes ipsilateral central lymph node metastasis rate, extrathyroidal extension, and ipsilateral central lymph node metastasis number retained strong performance and clearly surpassed a traditional nomogram approach based on multiple metrics and PMRA analysis. Similar results were obtained for the other dependent variables, with the RF models relying on distinct but overlapping sets of features. Clinical tool implementation is facilitated via a web-based calculator for each of the 4 dependent variables.
Conclusion:
ML, especially RF, reliably predicts lateral LNM in cN0 PTC patients, outperforming traditional nomograms.
