Health Management Center, Foshan Hospital of Traditional Chinese Medicine, Foshan, Guangdong, China.
Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, United States.
Front Endocrinol (Lausanne). 2024 May 7;15:1385836. doi: 10.3389/fendo.2024.1385836. eCollection 2024.
Ultrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes. However, there is a lack of clinical guidelines for the judicious use of thyroid ultrasonography in routine screening. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to leverage the ML approach in assessing the risk of thyroid nodules based on common clinical features.
Data were sourced from a Chinese cohort undergoing routine physical examinations including thyroid ultrasonography between 2013 and 2023. Models were established to predict the 3-year risk of thyroid nodules based on patients' baseline characteristics and laboratory tests. Four ML algorithms, including logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine, were trained and tested using fivefold cross-validation. The importance of each feature was measured by the permutation score. A nomogram was established to facilitate risk assessment in the clinical settings.
The final dataset comprised 4,386 eligible subjects. Thyroid nodules were detected in 54.8% (n=2,404) individuals within the 3-year observation period. All ML models significantly outperformed the baseline regression model, successfully predicting the occurrence of thyroid nodules in approximately two-thirds of individuals. Age, high-density lipoprotein, fasting blood glucose and creatinine levels exhibited the highest impact on the outcome in these models. The nomogram showed consistency and validity, providing greater net benefits for clinical decision-making than other strategies.
This study demonstrates the viability of an ML-based approach in predicting the occurrence of thyroid nodules. The findings highlight the potential of ML models in identifying high-risk individuals for personalized screening, thereby guiding the judicious use of ultrasound in this context.
超声在甲状腺结节的早期检测中起着重要作用，这对于进行适当的管理和获得良好的结果至关重要。然而，目前缺乏关于在常规筛查中合理使用甲状腺超声的临床指南。机器学习（ML）已越来越多地应用于大数据，以预测临床结果。本研究旨在利用 ML 方法评估基于常见临床特征的甲状腺结节风险。
数据来自于 2013 年至 2023 年期间在中国进行常规体检（包括甲状腺超声检查）的队列。基于患者的基线特征和实验室检查，建立模型以预测 3 年内甲状腺结节的风险。使用五折交叉验证对逻辑回归、随机森林、极端梯度提升和轻梯度提升机四种 ML 算法进行了训练和测试。通过置换得分来衡量每个特征的重要性。建立了一个列线图以方便在临床环境中进行风险评估。
最终数据集包括 4386 名合格的受试者。在 3 年的观察期内，54.8%（n=2404）的个体中发现了甲状腺结节。所有 ML 模型均显著优于基线回归模型，成功预测了大约三分之二个体中甲状腺结节的发生。在这些模型中，年龄、高密度脂蛋白、空腹血糖和肌酐水平对结果的影响最大。列线图显示了一致性和有效性，为临床决策提供了比其他策略更大的净收益。
本研究表明基于 ML 的方法在预测甲状腺结节发生方面具有可行性。研究结果强调了 ML 模型在识别高危个体以进行个性化筛查方面的潜力，从而指导在这种情况下合理使用超声。
