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

This 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). In 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). A 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. The combined radiomics model is a non-invasive preoperative tool that incorporates radiomic features and clinical risk factors to predict CLNM in patients with PTC.

Li J ，Wu X ，Mao N ，Zheng G ，Zhang H ，Mou Y ，Jia C ，Mi J ，Song X ... -  《Frontiers in Endocrinology》

Development and Validation of a Computed Tomography-Based Radiomics Nomogram for the Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma.

Mou Y ，Han X ，Li J ，Yu P ，Wang C ，Song Z ，Wang X ，Zhang M ，Zhang H ，Mao N ，Song X ... -  《-》

Prediction of Central Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma by CT Radiomics.

To explore the feasibility of the preoperative prediction of pathological central lymph node metastasis (CLNM) status in patients with negative clinical lymph node (cN0) papillary thyroid carcinoma (PTC) using a computed tomography (CT) radiomics signature. A total of 97 PTC cN0 nodules with CLNM pathology data (pN0, with CLNM, n = 59; pN1, without CLNM, n = 38) in 85 patients were divided into a training set (n = 69) and a validation set (n = 28). For each lesion, 321 radiomic features were extracted from nonenhanced, arterial and venous phase CT images. Minimum redundancy and maximum relevance and the least absolute shrinkage and selection operator were used to find the most important features with which to develop a radiomics signature in the training set. The performance of the radiomics signature was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis . Three nonzero the least absolute shrinkage and selection operator coefficient features were selected for radiomics signature construction. The radiomics signature for distinguishing the pN0 and pN1 groups achieved areas under the curve of 0.79 (95% CI 0.67, 0.91) in the training set and 0.77 (95% CI 0.55, 0.99) in the validation set. The calibration curves demonstrated good agreement between the radiomics score-predicted probability and the pathological results in the two sets (p= 0.399, p = 0.191). The decision curve analysis curves showed that the model was clinically useful. This radiomic signature could be helpful to predict CLNM status in cN0 PTC patients.

Peng Y ，Zhang ZT ，Wang TT ，Wang Y ，Li CH ，Zuo MJ ，Lin HS ，Gong LG ... -  《-》

Radiomics Analysis of Computed Tomography for Prediction of Thyroid Capsule Invasion in Papillary Thyroid Carcinoma: A Multi-Classifier and Two-Center Study.

To investigate the application of computed tomography (CT)-based radiomics model for prediction of thyroid capsule invasion (TCI) in papillary thyroid carcinoma (PTC). This retrospective study recruited 412 consecutive PTC patients from two independent institutions and randomly assigned to training (n=265), internal test (n=114) and external test (n=33) cohorts. Radiomics features were extracted from non-contrast (NC) and artery phase (AP) CT scans. We also calculated delta radiomics features, which are defined as the absolute differences between the extracted radiomics features. One-way analysis of variance and least absolute shrinkage and selection operator were used to select optimal radiomics features. Then, six supervised machine learning radiomics models (k-nearest neighbor, logistic regression, decision tree, linear support vector machine [L-SVM], Gaussian-SVM, and polynomial-SVM) were constructed. Univariate was used to select clinicoradiological risk factors. Combined models including optimal radiomics features and clinicoradiological risk factors were constructed by these six classifiers. The prediction performance was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). In the internal test cohort, the best combined model (L-SVM, AUC=0.820 [95% CI 0.758-0.888]) performed better than the best radiomics model (L-SVM, AUC = 0.733 [95% CI 0.654-0.812]) and the clinical model (AUC = 0.709 [95% CI 0.649-0.783]). Combined-L-SVM model combines 23 radiomics features and 1 clinicoradiological risk factor (CT-reported TCI). In the external test cohort, the AUC was 0.776 (0.625-0.904) in the combined-L-SVM model, showing that the model is stable. DCA demonstrated that the combined model was clinically useful. Our combined model based on machine learning incorporated with CT radiomics features and the clinicoradiological risk factor shows good predictive ability for TCI in PTC.

Wu X ，Yu P ，Jia C ，Mao N ，Che K ，Li G ，Zhang H ，Mou Y ，Song X ... -  《Frontiers in Endocrinology》

Extrathyroidal Extension Prediction of Papillary Thyroid Cancer With Computed Tomography Based Radiomics Nomogram: A Multicenter Study.

To develop and validate a Computed Tomography (CT) based radiomics nomogram for preoperative predicting of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC) patients. A total of 153 patients were randomly assigned to training and internal test sets (7:3). 46 patients were recruited to serve as an external test set. A radiologist with 8 years of experience segmented the images. Radiomics features were extracted from each image and Delta-radiomics features were calculated. Features were selected by using one way analysis of variance and the least absolute shrinkage and selection operator in the training set. K-nearest neighbor, logistic regression, decision tree, linear-support vector machine (linear -SVM), gaussian-SVM, and polynomial-SVM were used to build 6 radiomics models. Next, a radiomics signature score (Rad-score) was constructed by using the linear combination of selected features weighted by their corresponding coefficients. Finally, a nomogram was constructed combining the clinical risk factors with Rad-scores. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve were performed on the three sets to evaluate the nomogram's performance. 4 radiomics features were selected. The six models showed the certain value of radiomics, with area under the curves (AUCs) from 0.642 to 0.701. The nomogram combining the Rad-score and clinical risk factors (radiologists' interpretation) showed good performance (internal test set: AUC 0.750; external test set: AUC 0.797). Calibration curve and DCA demonstrated good performance of the nomogram. Our radiomics nomogram incorporating the radiomics and radiologists' interpretation has utility in the identification of ETE in PTC patients.

Yu P ，Wu X ，Li J ，Mao N ，Zhang H ，Zheng G ，Han X ，Dong L ，Che K ，Wang Q ，Li G ，Mou Y ，Song X ... -  《Frontiers in Endocrinology》