Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules - Li, Wanying; Hong, Tao; Fang, Jianqiang; Liu, Wencai; Liu, Yuwen; He, Cunyu; Li, Xinxin; Xu, Chan; Wang, Bing; Chen, Yuanyuan; Sun, Chenyu; Li, Wenle; et al. (Frontiers in Oncology, 2022)
Objective: This study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms. Methods: A retrospective study was performed in 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logostic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR) were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internally validation was performed by using 10-fold cross validation. The performance of the model was measured by the area under the receiver operating characteristic (AUC), accuracy, precision, recall, F1 score, Shapley Additive exPlanations (SHAP) plot, feature importance, and correlation of features. The best cut-off value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC). Results: The malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, the AUC value was 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected, meanwhile approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost. Conclusions: Combined clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on XGBoost model can easily and real-time identify the probability of malignant thyroid nodules, which can assist clinicians to formulate the individualized management strategies for patients.