Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study

Abstract

ObjectivesThis study aimed to develop a computed tomography (CT)-based radiomics model to predict central lymph node metastases (CLNM) preoperatively in patients with papillary thyroid carcinoma (PTC).MethodsIn this retrospective study, 678 patients with PTC were enrolled from Yantai Yuhuangding Hot3spital (n=605) and the Affiliated Hospital of Binzhou Medical University (n=73) within August 2010 to December 2020. The patients were randomly divided into a training set (n=423), an internal test set (n=182), and an external test set (n=73). Radiomics features of each patient were extracted from preoperative plain scan and contrast-enhanced CT images (arterial and venous phases). One-way analysis of variance (ANOVA) and least absolute shrinkage and selection operator algorithm were used for feature selection. The K-nearest neighbor, logistics regression, decision tree, linear-support vector machine (linear-SVM), Gaussian-SVM, and polynomial-SVM algorithms were used to establish radiomics models for CLNM prediction. The clinical risk factors were selected by ANOVA and multivariate logistic regression. Incorporated with clinical risk factors, a combined radiomics model was established for the preoperative prediction of CLNM in patients with PTCs. The performance of the combined radiomics model was evaluated using the receiver operating characteristic (ROC) and calibration curves in the training and test sets. The clinical usefulness was evaluated through decision curve analysis (DCA).ResultsA total of 4227 radiomic features were extracted from the CT images of each patient, and 14 non-zero coefficient features associated with CLNM were selected. Four clinical variables (sex, age, tumor diameter, and CT-reported lymph node status) were significantly associated with CLNM. Linear-SVM led to the best prediction model, which incorporated radiomic features and clinical risk factors. Areas under the ROC curves of 0.747 (95% confidence interval [CI] 0.706–0.782), 0.710 (95% CI 0.634–0.786), and 0.764 (95% CI 0.654–0.875) were obtained in the training, internal, and external test sets, respectively. The linear-SVM algorithm also showed better sensitivity (0.702 [95% CI 0.600–0.790] vs. 0.477 [95% CI 0.409–0.545]) and accuracy (0.670 [95% CI 0.600–0.738] vs. 0.642 [95% CI 0.569–0.712]) than an experienced radiologist in the internal test set in the combined radiomics model. The calibration plot reflected a favorable agreement between the actual and estimated probabilities of CLNM. The DCA indicated the clinical usefulness of the combined radiomics model.ConclusionThe combined radiomics model is a non-invasive preoperative tool that incorporates radiomic features and clinical risk factors to predict CLNM in patients with PTC.

A radiomics nomogram for the ultrasound-based evaluation of central cervical lymph node metastasis in papillary thyroid carcinoma

“…metastasis in PTC (29,36,37). These results suggested that radiomics models help to improve the diagnostic accuracy of cervical LN metastasis.…”

“…Radiomics extracts hundreds of quantitative features from images, and self-training and learning are conducted based on the pathological results to assist in clinical diagnosis ( 24 – 28 , 35 ). Available studies have been conducted to develop radiomics models based on CT or US images to predict cervical LN metastasis or lateral cervical LN metastasis in PTC ( 29 , 36 , 37 ). These results suggested that radiomics models help to improve the diagnostic accuracy of cervical LN metastasis.…”

Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer

“…The SVM was used to obtain the best predictive radiomics model. An SVM is an effective, powerful, and robust machine learning classifier used primarily in radiology ( 25 , 26 ). In this study, SVM had faster training and classification speed than RF, KNN, LR, GBDT, and XGBOOST, because it was most suitable for high-dimensional features ( 27 ).…”

Preliminary study on CT contrast-enhanced radiomics for predicting central cervical lymph node status in patients with thyroid nodules

“…Recent studies have found that radiomics can be used to describe tumor phenotypes, distinguish benign and malignant tumors, and predict lymph node metastasis and outcomes (7). Radiomics models have been shown to predict lymph node metastasis in PTC (8,9), and CT-based radiomics models are also valuable in the differentiation of benign and malignant lymph nodes in the head and neck (10). Few studies have examined the differentiation of metastatic and nonmetastatic lymph nodes among patients with PTC and the classification of reactive hyperplastic lymph nodes among patients with benign thyroid lesions.…”