Utilizing machine learning for early screening of thyroid nodules: a dual-center cross-sectional study in China

作者：Shuwei Weng, Chen Ding, Die Hu, Jin Chen, Yang Liu, Wenwu Liu, Yang Chen, Xin Guo, Chenghui Cao, Yuting Yi, Yang Yanyi, Daoquan Peng · 发表于：Frontiers in Endocrinology · 年份：2024 · DOI：10.3389/fendo.2024.1385167 · 被引用次数：17 · 研究领域：Thyroid Cancer Diagnosis and Treatment、AI in cancer detection、Artificial Intelligence in Healthcare and Education

Background: Thyroid nodules, increasingly prevalent globally, pose a risk of malignant transformation. Early screening is crucial for management, yet current models focus mainly on ultrasound features. This study explores machine learning for screening using demographic and biochemical indicators. Methods: Analyzing data from 6,102 individuals and 61 variables, we identified 17 key variables to construct models using six machine learning classifiers: Logistic Regression, SVM, Multilayer Perceptron, Random Forest, XGBoost, and LightGBM. Performance was evaluated by accuracy, precision, recall, F1 score, specificity, kappa statistic, and AUC, with internal and external validations assessing generalizability. Shapley values determined feature importance, and Decision Curve Analysis evaluated clinical benefits. Results: Random Forest showed the highest internal validation accuracy (78.3%) and AUC (89.1%). LightGBM demonstrated robust external validation performance. Key factors included age, gender, and urinary iodine levels, with significant clinical benefits at various thresholds. Clinical benefits were observed across various risk thresholds, particularly in ensemble models. Conclusion: Machine learning, particularly ensemble methods, accurately predicts thyroid nodule presence using demographic and biochemical data. This cost-effective strategy offers valuable insights for thyroid health management, aiding in early detection and potentially improving clinical outcomes. These ...