Background: Prophylactic central neck dissection (pCND) in patients with well-differentiated primary papillary thyroid carcinoma (PTC) has become controversial. Several attempts have been made to predict central compartment lymph node metastasis (CLNM) based on clinical and conventional ultrasonic parameters. This study aimed to develop a decision tree (DT) model for predicting the risk of CLNM in patients with PTC based on clinical and preoperative multimodal ultrasound (US) characteristics. Methods: A total of 148 PTC nodules confirmed by surgical pathology at Beijing Tiantan Hospital were retrospectively analyzed. All nodules underwent multimodal US examinations preoperatively from January 2020 to September 2021. Correlation analysis of CLNM with clinical characteristics as well as multimodal US parameters of PTC lesions based on gray-scale US, color Doppler flow imaging (CDFI), superb microvascular imaging (SMI), contrast-enhanced ultrasound (CEUS), and shear wave elastography (SWE) technology was carried out. Finally, the chi-squared automatic interaction detector (CHAID) with a 10-fold cross-validation was used to establish DTs for CLNM prediction. The area under the curve was calculated to compare the predictive performance.Results: Univariate analysis indicated that CLNM was positively correlated with thyroglobulin level, maximum size, taller-than-wide, the number of microcalcifications greater than or equal to 5, contact capsule, abnormal cervical lymph node on conventional US, noncentripetal perfusion, delayed clearance, the average shear wave velocity (SWV mean), and the SWV ratio (P<0.05). The multimodal US DT based on taller than-wide, contact capsule, abnormal cervical lymph node on conventional US, and centripetal enhancement as independent variables showed good discrimination: the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were 80.0%, 76.7%, 78.4%, and 0.837 [95% confidence interval (CI): 0.771-0.902]. There was a significant difference between the multimodal and conventional US DTs (P=0.009).Conclusions: Our results indicated that the DT based on the preoperative multimodal US characteristics of PTCs has a reasonable predictive ability for CLNM and can be conveniently used for clinical decision making of individualized treatment in patients with well-differentiated PTC.

Background: Early preoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) is critical for further surgical treatment. However, insufficient accuracy in predicting LNM status for PTC based on ultrasound images is a problem that needs to be urgently resolved. This study aimed to clarify the role of convolutional neural networks (CNNs) in predicting LNM for PTC based on multimodality ultrasound. Methods: In this study, the data of 308 patients who were clinically diagnosed with PTC and had confirmed LNM status via postoperative pathology at Beijing Tiantan Hospital, Capital Medical University, from August 2018 to April 2022 were incorporated into CNN algorithm development and evaluation. Of these patients, 80% were randomly included into the training set and 20% into the test set. The ultrasound examination of cervical LNM was performed to assess possible metastasis. Residual network 50 (Resnet50) was employed for feature extraction from the B-mode and contrast-enhanced ultrasound (CEUS) images. For each case, all of features were extracted from B-mode ultrasound images and CEUS images separately, and the ultrasound examination data of cervical LNM information were concatenated together to produce a final multimodality LNM prediction. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the predictive model. Heatmaps were further developed for visualizing the attention region of the images of the best-working model. Results: Of the 308 patients with PTC included in the analysis, 158 (51.3%) were diagnosed as LNM and 150 (48.7%) as non-LNM. In the test set, when a triple-modality method (i.e., B-mode image, CEUS image, and ultrasound examination of cervical LNM) was used, accuracy was maximized at 80.65% (AUC =0.831; sensitivity =80.65%; specificity =82.26%), which showed an expected increased performance over B-mode alone (accuracy =69.00%; AUC =0.720; sensitivity =70.00%; specificity =73.00%) and a dual-modality method (B-mode image plus CEUS image: accuracy =75.81%; AUC =0.742; sensitivity =74.19%; specificity =77.42%). The heatmaps of our triple-modality model demonstrated a possible focus area and revealed the model's flaws. Conclusions: The PTC lymph node prediction model based on the triple-modality features significantly outperformed all the other feature configurations. This deep learning model mimics the workflow of a human expert and leverages multimodal data from patients with PTC, thus further supporting clinical decision-making.

Background There is a recognized need for additional approaches to improve the accuracy of extrathyroidal extension (ETE) diagnosis in papillary thyroid carcinoma (PTC) before surgery. Up to now, multimodal ultrasound has been widely applied in disease diagnosis. We investigated the value of radiomic features extracted from multimodal ultrasound in the preoperative prediction of ETE. Methods We retrospectively pathologically confirmed PTC lesions in 235 patients from January 2019 to April 2022 in our hospital, including 45 ETE lesions and 205 non-ETE lesions. MaZda software was employed to obtain radiomics parameters in multimodal sonography. The most valuable radiomics features were selected by the Fisher coefficient, mutual information, probability of classification error and average correlation coefficient methods (F + MI + PA) in combination with the least absolute shrinkage and selection operator (LASSO) method. Finally, the multimodal model was developed by incorporating the clinical records and radiomics features through fivefold cross-validation with a linear support vector machine algorithm. The predictive performance was evaluated by sensitivity, specificity, accuracy, F1 scores and the area under the receiver operating characteristic curve (AUC) in the training and test sets. Results A total of 5972 radiomics features were extracted from multimodal sonography, and the 13 most valuable radiomics features were selected from the training set using the F + MI + PA method combined with LASSO regression. The multimodal prediction model yielded AUCs of 0.911 (95% CI 0.866–0.957) and 0.716 (95% CI 0.522–0.910) in the cross-validation and test sets, respectively. The multimodal model and radiomics model showed good discrimination between ETE and non-ETE lesions. Conclusion Radiomics features based on multimodal ultrasonography could play a promising role in detecting ETE before surgery.

Objective:To explore the clinical application value of deep learning based ultrasonic thyroid nodule segmentation.Methods:We selected 1044 ultrasound images of 166 thyroid patients collected from Beijing Tiantan Hospital Affiliated to Capital Medical University from August 2018 to October 2020. The segmentation effect of Unet with improved self-attention mechanism and control Unet-based method was assessed using test datasets. Whether the segmentation result is close to the manual annotation by a sonographer with many years of clinical experience was used as a reference standard, and the Unet and Unet-based methods with improved self-attention mechanism were compared for the segmentation effect of thyroid nodules, using IoU (intersection and union ratio), Dice (Dice similarity coefficient), and the degree of closeness to the manual outline of thyroid nodules by the sonographer to evaluate the performance and clinical value of the deep learning model for thyroid nodule segmentation.Results:The IoU and Dice coefficients of thyroid nodule segmentation by Unet with improved self-attention mechanism were 0.815 and 0.839, respectively, which were higher than those of Unet (IoU=0.788, Dice=0.817). It can also be seen from the segmented images that the Unet based on the improved self-attention mechanism had a better segmentation effect on the overall and edge details of thyroid nodules than the Unet-based method, and was closer to the manual outline results of the sonographer.Conclusion:Unet based on self-attention mechanism has good performance in thyroid nodule segmentation, which can improve the diagnostic efficiency, the method also has clinical application value.

Objective:To investigate the predictive value of multimodal ultrasound consisting of conventional ultrasound, contrast-enhanced ultrasound, and superb microvascular imaging (SMI) in cervical lymph node metastasis (CLNM) of papillary thyroid microcarcinoma (PTMC).Methods:A retrospective analysis was performed on 99 patients with pathologically confirmed PTMC at Beijing Tiantan Hospital, Capital Medical University from October 2018 to April 2021. Conventional ultrasound, contrast-enhanced ultrasound, and SMI were all performed preoperatively. According to pathologic results, the patients were divided into either a non-metastatic cervical lymph node group (n=60) or a metastatic cervical lymph node group (n=39). Features of multimodal ultrasound and clinical data of PTMC were observed and recorded. Independent-sample t test, Chi-square test, and Fisher exact test were used to compare the differences in all features between the two groups, and the statistically significant factors were included in multivariate Logistic regression analysis to identify the independent risk factors for PTMC with CLNM.Results:Univariate analysis showed that compared with the patients with CLNM, the patients without CLNM were older [(47.50±11.48) years vs (39.67±9.95) years], had smaller PTMC [(0.66±0.02) cm vs (0.77±0.02) cm], were less likely to have the maximum diameter of single carcinoma or the sum of the maximum diameter of multiple carcinoma＞1.0 cm (13/60 vs 17/39), had less microcalcifications (none, ≤5 but not none, and＞5∶23, 23, and 14 vs 10, 10, and 19, respectively), were less likely to have PTMC with equal or high density enhancement (9/60 vs 13/39), and were less likely to have interruption of capsule continuity in early stage of contrast-enhanced ultrasound (14/60 vs 17/39) (t=3.491, P=0.001; t=3.376, P=0.001; χ2=5.379, P=0.020; χ2=6.854, P=0.032; χ2=4.596, P=0.032; and χ2=4.509, P=0.034, respectively). Multivariate analysis showed that younger age (odds ratio [OR]=0.933, P =0.004), larger PTMC (OR=30.567,P=0.046), and interruption of capsule continuity in early stage of contrast-enhanced ultrasound (OR=0.296, P=0.032) independently increased the risk of CLNM in PTMC.Conclusion:Younger age, larger PTMC, and interruption of capsule continuity in early stage of contrast-enhanced ultrasound are independent risk factors for predicting CLNM of PTMC. The features of preoperative multimodal ultrasound have appreciated predictive value for CLNM in patients with PTMC.

Background Thyroid cancer is the most common malignancy of the endocrine system worldwide. Papillary thyroid cancer (PTC) is the most common pathologic type. The preoperative diagnosis of PTC and central lymph node metastasis (CLNM) or metastatic tendency is of great clinical significance to the diagnosis, treatment and prognosis of these patients. This study was conducted to investigate the correlation between ultrasound features and central CLNM of PTC. Methods This study retrospectively analyzed patients who underwent PTC surgery and central lymph node dissection in the Department of Surgery, Beijing Tiantan Hospital, from January 2019 to February 2020. According to the inclusion and exclusion criteria, data from 136 patients were ultimately included, and the clinical and ultrasonic data of the patients were analyzed by multivariate regression to evaluate the correlation among grayscale ultrasound (US), superb microvascular imaging (SMI) and contrast-enhanced ultrasound (CEUS) features of thyroid nodules and CLNM of PTCs. Results The multivariate analysis showed that tumor size, multifocality, microcalcification characteristics, SMI vascularization, and CEUS evaluation of contact with the adjacent capsule were correlated with PTC metastasis (P=0.008, P=0.001, P=0.028, P=0.041, and P< 0.001, respectively). Comparisons of the area under the ROC curves revealed that the area under the ROC curve of the degree of nodular invasion into the thyroid capsule was the largest (AUC: 0.754). The sensitivity and specificity for evaluating CLNM of PTC were 67.7% and 83.1%, respectively. Conclusions Ultrasound characteristics of the following features are associated with a high risk of lymph node metastasis in PTCs: maximum diameter of nodules ≥1 cm, multifocality, ≥5 microcalcifications, abundant blood flow of SMI in nodules and nodule contact with the thyroid capsule ≥25% under CEUS. Ultrasound has clinical value in the preoperative evaluation of CLNM of PTCs.