Central Cervical Lymph Node Metastasis Research Articles

Application of ultrasound elastography and radiomic for predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma.

This study aims to combine ultrasound (US) elastography (USE) and radiomic to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). A total of 204 patients with 204 thyroid nodules who were confirmed with PTMC and treated in our hospital were enrolled and randomly assigned to the training set (n = 142) and the validation set (n = 62). US features, USE (gender, shape, echogenic foci, thyroid imaging reporting and data system (TIRADS) category, and elasticity score), and radiomic signature were employed to build three models. A nomogram was plotted for the combined model, and decision curve analysis was applied for clinical use. The combined model (USE and radiomic) showed optimal diagnostic performance in both training (AUC = 0.868) and validation sets (AUC = 0.857), outperforming other models. The combined model based on USE and radiomic showed a superior performance in the prediction of CLNM of patients with PTMC, covering the shortage of low specificity of conventional US in detecting CLNM.

Transoral robotic thyroidectomy via vestibular approach: a retrospective study of 107 cases in a single center

Objective: To investigate the short-term outcome of transoral robotic thyroidectomy. Methods: This is a retrospective case series study. The clinicopathologic characteristics and postoperative results of 107 patients who underwent transoral robotic thyroidectomies in the Department of Thyroid and Breast Surgery of the 960th Hospital of People's Liberation Army from May 2020 to August 2023 were retrospectively analyzed. There were 12 males and 95 females, with an age of (31.8±9.4) years (range: 11 to 55 years), including 20 benign tumors and 87 thyroid papillary carcinoma. Postoperative follow-up was carried out through returning visit and telephone, mainly to observe the recovery of postoperative complications, cosmetic effects and recurrence results. Results: All transoral robotic thyroidectomy was successfully completed without conversion to open surgery. The tumor size of thyroid papillary carcinoma patients was (5.6±2.7) mm (range: 2 to 15 mm). Furthermore, central cervical lymph node metastasis was found in 45 cases. The number of central cervical lymph nodes retrieved and metastasized (M(IQR)) were 11 (8) (range: 3 to 26) and 1 (3) (range: 0 to 13), respectively. There was no recurrent laryngeal nerve injury and permanent hypoparathyroidism. The transient hypoparathyroidism after surgery was 8 cases. Other complications occurred as follows: postoperative infection (n=1), left submandibular perforation (n=1), skin scald (n=1), and perioral numbness (n=1), oral tear (n=2). The postoperative stay was 6 (2) days (range: 3 to 11 days). No local lymph node recurrence or metastasis occurred after a follow-up of (22.6±10.0) months (range: 1.0 to 37.4 months). All patients were satisfied with the postoperative cosmetic results, the aesthetic effect score was 9.3 (0.2) (range: 8.4 to 9.6) one month after surgery. Conclusion: For highly screened patients with early thyroid cancer, experienced surgeons can perform a transoral robotic thyroidectomy that has excellent cosmetic results.

Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning.

The aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). This research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F1 score. The multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, p < 0.001), tumor diameter (OR = 2.551, p = 0.010), macrocalcifications (OR = 1.832, p = 0.002), and capsular invasion (OR = 1.977, p = 0.005). The suggested DL model utilized US images achieved an average area under the curve (AUC) of 0.65, slightly outperforming the model that employed traditional clinical factors (AUC = 0.64). Nevertheless, the model that incorporated both of them did not enhance prediction accuracy (AUC = 0.63). The suggested approach offers a reference for the treatment and supervision of PTMC. Among three models used in this study, the deep model relied generally more on image modalities than the data modality of clinic records when making the predictions.

Predicting central cervical lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis: a practical nomogram based on retrospective study.

In papillary thyroid carcinoma (PTC) patients with Hashimoto's thyroiditis (HT), preoperative ultrasonography frequently reveals the presence of enlarged lymph nodes in the central neck region. These nodes pose a diagnostic challenge due to their potential resemblance to metastatic lymph nodes, thereby impacting the surgical decision-making process for clinicians in terms of determining the appropriate surgical extent. Logistic regression analysis was conducted to identify independent risk factors associated with central lymph node metastasis (CLNM) in PTC patients with HT. Then a prediction model was developed and visualized using a nomogram. The stability of the model was assessed using ten-fold cross-validation. The performance of the model was further evaluated through the use of ROC curve, calibration curve, and decision curve analysis. A total of 376 HT PTC patients were included in this study, comprising 162 patients with CLNM and 214 patients without CLNM. The results of the multivariate logistic regression analysis revealed that age, Tg-Ab level, tumor size, punctate echogenic foci, and blood flow grade were identified as independent risk factors associated with the development of CLNM in HT PTC. The area under the curve (AUC) of this model was 0.76 (95% CI [0.71-0.80]). The sensitivity, specificity, accuracy, and positive predictive value of the model were determined to be 88%, 51%, 67%, and 57%, respectively. The proposed clinic-ultrasound-based nomogram in this study demonstrated a favorable performance in predicting CLNM in HT PTCs. This predictive tool has the potential to assist clinicians in making well-informed decisions regarding the appropriate extent of surgical intervention for patients.

Application of Ultrasound Shear Wave Elastography Combined With BRAFV600E Mutation for the Diagnosis of Papillary Thyroid Carcinoma.

This prospective study aimed to investigate the combination of shear wave elastography (SWE) and BRAFV600E mutation testing for the diagnosis of papillary thyroid carcinoma (PTC). One hundred thyroid nodules with an American College of Radiology thyroid imaging reporting and data system classification of 4 or 5 were subjected to SWE measurement, BRAFV600E genotyping, fine-needle aspiration, and surgery. Nonparametric statistical tests were used to compare the differences in the elastic parameters and BRAF genotypes between benign and malignant thyroid nodules of PTC, and receiver operating characteristic curve analysis was conducted to compare the diagnostic efficacy. In addition, the correlations between elastic parameters and BRAFV600E mutation in PTC were analyzed using binary logistic regression. The SWE, BRAFV600E, and their combination exhibited sensitivities of 72.9%, 81.3%, and 85.4%, respectively, and specificities of 66.7%, 90.3%, and 86.5%, respectively, in the diagnosis of PTC ( P < 0.05). The SWE, BRAFV600E, and their combination exhibited sensitivities of 50.0%, 24.1% and 56.3%, respectively, and specificities of 89.7%, 87.5% and 82.8%, respectively, in the diagnosis of central cervical lymph node metastasis ( P < 0.05). The combined use of SWE and BRAFV600E detection had the largest area under the curve, indicating that this combination is more effective in diagnosing PTC and lymph node metastasis in the central region than either method alone. Furthermore, Emax was positively associated with the BRAFV600E genotype. In conclusion, the combination of SWE and BRAFV600E genotype detection can improve the diagnostic efficacy for PTC. Emax can predict the BRAFV600E mutation status.

Preoperative strain ultrasound elastography can predict occult central cervical lymph node metastasis in papillary thyroid cancer: a single-center retrospective study.

To determine whether preoperative ultrasound elastography can predict occult central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid cancer. This retrospective study included 541 papillary thyroid cancer patients with clinically negative lymph nodes prior to surgery between July 2019 and December 2021. Based on whether CCLNM was present on postoperative pathology, patients were categorized as CCLNM (+) or CCLNM (-). Preoperative clinical data, conventional ultrasound features, and ultrasound elastography indices were compared between the groups. Univariate and multivariate logistic regression analysis were performed to identify the independent predictors of occult CCLNM. A total of 36.60% (198/541) patients had confirmed CCLNM, while 63.40% (343/541) did not. Tumor location, bilaterality, multifocality, echogenicity, margin, shape, vascularity, capsule contact, extrathyroidal extension, aspect ratio, and shear wave elasticity parameters were comparable between the groups (all P > 0.05). Univariate analysis showed statistically significant differences between the two groups in age, sex, tumor size, calcification, capsule invasion, and strain rates ratio in strain ultrasound elastography (all P < 0.05). In multivariate logistic regression analysis, the independent predictors of occult CCLNM were age (OR = 0.975, 95% CI = 0.959-0.991, P = 0.002), sex (OR = 1.886, 95% CI = 1.220-2.915, P = 0.004), tumor size (OR = 1.054, 95% CI = 1.014-1.097, P = 0.008), and strain rates ratio (OR = 1.178, 95% CI = 1.065-1.304, P = 0.002). Preoperative strain ultrasound elastography can predict presence of occult CCLNM in papillary thyroid cancer patients and help clinicians select the appropriate treatment strategy.

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

To develop and validate a radiomics nomogram based on ultrasound (US) to predict central cervical lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC). PTC patients with pathologically confirmed presence or absence of central cervical LN metastasis in our hospital between March 2021 and November 2021 were enrolled as the training cohort. Radiomics features were extracted from the preoperative US images, and a radiomics signature was constructed. Univariate and multivariate logistic regression analyses were used to screen out the independent risk factors, and a radiomics nomogram was established. The performance of the model was verified in the independent test cohort of PTC patients who underwent thyroidectomy and cervical LN dissection in our hospital from December 2021 to March 2022. In the independent test cohort, the radiomics model based on long-axis cross-section and short-axis cross-section images outperformed the radiomics models based on either one of these sections (the area under the curve (AUC), 0.69 vs. 0.62 and 0.66). The radiomics signature consisted of 4 selected features. The US radiomics nomogram included the radiomics signature, age, gender, BRAF V600E mutation status, and extrathyroidal extension (ETE) status. In the independent test cohort, the AUC of the receiver operating curve(ROC) of this nomogram was 0.76, outperformingthe clinical model and the radiomics model (0.63 and 0.69, respectively), and also much better than preoperative US examination (AUC, 0.60). Decision curve analysis indicated that the radiomics nomogram was clinically useful. This study presents an efficient and useful US radiomics nomogram that can provide comprehensive information to assist clinicians in the individualized preoperative prediction of central cervical LN metastasis in PTC patients.

Contrast-enhanced ultrasound characteristics of preoperative central cervical lymph node metastasis in papillary thyroid carcinoma

This study evaluated the preoperative diagnostic value of lymph node ultrasonography in distinguishing between benign and malignant central cervical lymph nodes (CCLNs) in patients with papillary thyroid carcinoma (PTC). A total of 176 patients who had PTC with 216 CCLNs (49 benign and 155 malignant) were enrolled in this study and preoperatively imaged by ultrasonography, including conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS). We evaluated the ultrasonography parameters for each lymph node. Binary logistic regression analysis indicated that multifocality of PTC and the absence of Hashimoto’s thyroiditis are independent clinical features related to patients with PTC who also have malignant CCLNs. For preoperative ultrasonography features, heterogeneous enhancement and centripetal perfusion are independent ultrasonographic features to identify malignant and benign CCLNs. This study demonstrated that preoperative CEUS characteristics help to distinguish malignant CCLNs from benign CCLNs.

Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP.

ObjectivesA radiomics-based explainable eXtreme Gradient Boosting (XGBoost) model was developed to predict central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid carcinoma (PTC), including positive and negative effects.MethodsA total of 587 PTC patients admitted at Binzhou Medical University Hospital from 2017 to 2021 were analyzed retrospectively. The patients were randomized into the training and test cohorts with an 8:2 ratio. Radiomics features were extracted from ultrasound images of the primary PTC lesions. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used to select CCLNM positively-related features and radiomics scores were constructed. Clinical features, ultrasound features, and radiomics score were screened out by the Boruta algorithm, and the XGBoost model was constructed from these characteristics. SHapley Additive exPlanations (SHAP) was used for individualized and visualized interpretation. SHAP addressed the cognitive opacity of machine learning models.ResultsEleven radiomics features were used to calculate the radiomics score. Five critical elements were used to build the XGBoost model: capsular invasion, radiomics score, diameter, age, and calcification. The area under the curve was 91.53% and 90.88% in the training and test cohorts, respectively. SHAP plots showed the influence of each parameter on the XGBoost model, including positive (i.e., capsular invasion, radiomics score, diameter, and calcification) and negative (i.e., age) impacts. The XGBoost model outperformed the radiologist, increasing the AUC by 44%.ConclusionsThe radiomics-based XGBoost model predicted CCLNM in PTC patients. Visual interpretation using SHAP made the model an effective tool for preoperative guidance of clinical procedures, including positive and negative impacts.

Related factor analysis for predicting large-volume central cervical lymph node metastasis in papillary thyroid carcinoma.

The aim of this study was to investigate the factors related to large-volume central cervical lymph node metastasis (LNM) in papillary thyroid carcinoma. A retrospective study of 340 patients with 642 papillary thyroid carcinoma nodules who underwent thyroidectomy in Peking Union Medical College Hospital between 2011 and 2015 was conducted. These nodules were divided into two groups by the number of central cervical lymph node metastases: large‐volume central cervical LNM (>5 metastatic lymph nodes, n = 129) and no central cervical LNM (n = 211). We evaluated the correlations between gender, age, chronic lymphocytic thyroiditis, thyroid ultrasonographic features, and large‐volume central cervical LNM. We found that younger age (≤40 years) (OR = 3.796, 95% CI = 2.842, 5.070), male gender (OR = 4.005, 95% CI = 2.858, 5.61), and ultrasonographic features such as tumor macroaxis size (OR = 2.985, 95% CI = 1.581, 5.633), tumor located in the isthmus (OR = 7.578, 95% CI = 4.863, 11.810), ill-defined margin (OR = 3.008, 95% CI = 1.986, 4.556), microcalcification (OR = 2.155, 95% CI = 1.585, 2.929), and abnormal cervical lymph nodes (OR = 13.753, 95% CI = 9.278, 20.385) were independent risk factors for large-volume central cervical LNM in papillary thyroid carcinoma, while chronic lymphocytic thyroiditis (OR = 0.248, 95% CI = 0.172, 0.358) was a protective factor. Younger age (≤40 years), male sex, and ultrasonographic features such as tumor macroaxis size, tumor located in the isthmus, ill-defined margin, microcalcification, and abnormal cervical lymph nodes were independent risk factors for large-volume central cervical LNM in papillary thyroid carcinoma, while chronic lymphocytic thyroiditis can be considered a protective factor. Our results provide a reference for adjusting clinical treatment approaches.

Extreme gradient boosting model to assess risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual prediction using SHapley Additive exPlanations

Background and ObjectivesCentral cervical lymph node metastasis (CLNM) is considered a risk factor for recurrence in patients with papillary thyroid carcinoma (PTC). Traditional machine learning models suffered from “black-box” problems, which could not exactly explain the interactive effects of the risk factors. We aimed to develop an eXtreme Gradient Boosting (XGBoost) model to assess CLNM, including positive and negative effects. Methods1,122 patients with PTC admitted at Tianjin First Central Hospital from 2016 to 2020 were retrospectively selected. They were randomly divided into the training and test datasets with an 8:2 ratio. 108 patients with PTC admitted at Binzhou Medical University Hospital in 2020 served as the validation dataset. The XGBoost model was used to assess CLNM. The 10-fold cross-validation was utilized for model selection, and the metric used to evaluate classification performance was the average area under the curve (AUC) of 10-fold cross-validation. Interpretation and transparency of the “black-box” problem were performed. SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) were used to ensure the stability and reliability of the model. ResultsThe XGBoost model based on ultrasound and dual-energy computed tomography images of the solitary primary lesion had an excellent performance for assessing CLNM, with average AUCs of 0.918, 0.903, and 0.881 in the training, test, and validation datasets, respectively. SHAP plots showed the influence of each parameter on the XGBoost model, including positive (i.e., capsular invasion, diameter, iodine concentration in the venous phase, and calcification) and negative (i.e., sex and age) impacts. For all cases, the capsular invasion prediction weight was the highest; for individual cases, different predictors were assigned different weights. Moreover, the performance of the XGBoost model was better than classical machine-learning models. ConclusionsThis study developed and validated an XGBoost model for assessing CLNM in patients with PTC. The ability to visually interpret the positive and negative effects made the XGBoost model an effective tool for guiding clinical treatment.

Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study

BackgroundAn accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC.MethodsA total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness.ResultsThe radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis.ConclusionThe nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice.

Preoperative Prediction of Central Cervical Lymph Node Metastasis in Fine-Needle Aspiration Reporting Suspicious Papillary Thyroid Cancer or Papillary Thyroid Cancer Without Lateral Neck Metastasis

PurposeNo non-invasive method can accurately determine the presence of central cervical lymph node (CCLN) metastasis in papillary thyroid cancer (PTC) until now. This study aimed to investigate factors significantly associated with CCLN metastasis and then develop a model to preoperatively predict CCLN metastasis in fine-needle aspiration (FNA) reporting suspicious papillary thyroid cancer (PTC) or PTC without lateral neck metastasis.Patients and MethodsConsecutive inpatients who were diagnosed as suspicious PTC or PTC in FNA and underwent partial or total thyroidectomy and CCLN dissection between May 1st, 2016 and June 30th, 2018 were included. The total eligible patients were randomly divided into a training set and an internal validation set with the ratio of 7:3. Univariate analysis and multivariate analysis were conducted in the training set to investigate factors associated with CCLN metastasis. The predicting model was built with factors significantly correlated with CCLN metastasis and validated in the validation set.ResultsA total of 770 patients were eligible in this study. Among them, 268 patients had histologically confirmed CCLN metastasis, while the remaining patients did not. Factors including age, BRAF mutation, multifocality, size, and capsule involvement were found to be significantly correlated with the CCLN metastasis in univariate and multivariate analysis. A model used to predict the presence CCLN metastasis based on these factors and US CCLN status yielded AUC, sensitivity, specificity and accuracy of 0.933 (95%CI: 0.905-0.960, p < 0.001), 0.816, 0.966 and 0.914 in the training set and 0.967 (95%CI: 0.943-0.991, p < 0.001), 0.897, 0.959 and 0.936 in the internal validation set.ConclusionAge, BRAF mutation, multifocality, size, and capsule involvement were independent predictors of CCLN metastasis in FNA reporting suspicious PTC or PTC without lateral neck metastasis. A simple model was successfully built and showed excellent discrimination to distinguish patients with or without CCLN metastasis.

Predictors and a Prediction Model for Central Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma (cN0).

ObjectivesTo screen out the predictors of central cervical lymph node metastasis (CLNM) for papillary thyroid carcinoma (PTC) and establish a prediction model to guide the operation of PTC patients (cN0).MethodsData from 296 PTC patients (cN0) who underwent thyroid operation at the Second Affiliated Hospital of Chongqing Medical University were collected and retrospectively analyzed. They were divided into two groups in accordance with central CLNM or not. Their information, including ultrasound (US) features, BRAFV600E status, and other characteristics of the two groups, was analyzed and compared using univariate and multivariate logistic regression analyses, and the independent predictors were selected to construct a nomogram. The calibration plot, C-index, and decision curve analysis were used to assess the prediction model’s calibration, discrimination, and clinical usefulness.ResultsA total of 37.8% (112/296) of PTC patients had central CLNM, and 62.2% (184/296) did not. The two groups were compared using a univariate logistic regression analysis, and there were no significant differences between the two groups in sex, aspect ratio, boundary, morphology, hypoechoic nodule, thyroid peroxidase antibody, or tumor location (P>0.05), and there were significant differences between age, tumor size, capsule contact, microcalcifications, blood flow signal, thyroglobulin antibodies (TgAb), and BRAF gene status (P<0.05). A multivariate logistic regression analysis was performed to further clarify the correlation of these indices. However, only tumor size (OR=2.814, 95% Cl=1.634~4.848, P<0.001), microcalcifications (OR=2.839, 95% Cl=1,684~4.787, P<0.001) and TgAb (OR=1.964, 95% Cl=1.039~3,711, P=0.038) were independent predictors of central CLNM and were incorporated and used to construct the prediction nomogram. The model had good discrimination with a C-index of 0.715. An ROC curve analysis was performed to evaluate the accuracy of this model. The decision curve analysis showed that the model was clinically useful when intervention was decided in the threshold range of 16% to 80%.ConclusionIn conclusion, three independent predictors of central CLNM, including tumor size (> 1.0 cm), US features (microcalcifications), and TgAb (positive), were screened out. A visualized nomogram model was established based on the three predictors in this study, which could be used as a basis of central cervical lymph node dissection (CLND) for PTC patients (cN0).

Correlation analysis of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio and central cervical lymph node metastasis of papillary thyroid microcarcinoma

Objective: To investigate the correlation between neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and central lymph node metastasis (CLNM) in patients with cN0 papillary thyroid microcarcinoma (PTMC). Methods: The clinicopathological data of PTMC patients confirmed by surgery and pathology in the 81st Military Hospital of People's Liberation Army from 2016 to 2019 were collected, and the relationship between preoperative NLR, PLR levels and postoperative PTMC CLNM were analyzed. Logistic regression analysis was used for multivariate analysis. Receiver operating characteristic (ROC) curve was used to determine the cutoff value of NLR and PLR. The interaction relative excess risk was used to analyze the relationship between NLR, PLR and CLNM. Results: Among 220 patients with cN0 stage PTMC, 92 were CLNM. The ROC curve showed that when the cutoff value of NLR was 2.5 and the cutoff value of PLR was 175, the highest Youden index was 0.318 and 0.264, respectively. NLR and PLR were both related to CLNM (P<0.05). The tumor long diameter, multifocality, NLR≥2.5 and PLR≥175 were independent impact factors of CLNM (P<0.05). The results of the interaction showed that the relative excess risk of the interaction was 5.531 (95%CI: 0.160, 10.901, P=0.016), the attribution ratio was 0.512 (95%CI: 0.230, 0.794, P=0.009), and the synergy index was 2.294 (95%CI: 1.492, 4.579, P=0.022), suggested that NLR and PLR had an interactive effect, and these two synergistically promoted CLNM. Conclusions: NLR and PLR are independent risk factors for cN0 stage PTMC CLNM. When NLR≥2.5 and PLR≥175, preventive central lymph node dissection should be routinely performed.

Establishment of a predictive model for central cervical lymph node metastasis of papillary thyroid carcinoma based on ACR TI-RADS score and evaluation of its diagnostic efficacy

Objective:To establish a predictive model for central lymph node metastasis（CLNM） of papillary thyroid carcinoma（PTC） based on ACR TI-RADS grades（ATR model） and evaluate its diagnostic efficacy. Methods:A total of 319 patients with PTC diagnosed from January 2019 to May 2020 were included, including 366 nodules were used as the modeling cohort to construct the risk prediction model. A total of 105 PTC patients with 121 nodules from June to August 2020 were included as the external validation cohort. The C-index of the model was calculated and the Hosmer-Lemeshow goodness-of-fit test was performed to compare the diagnostic efficiency of ACR model and those conventional imaging models. Results:The ATR model, Y=-3.719+0.765×gender+1.094×multifocality+0.08×maximum diameter+0.266×ACR TI-RADS score. In the training set, validation set and external validation cohort, the model C-index was 0.758（95%CI: 0.699-0.817）, 0.717（95%CI: 0.619-0.815） and 0.756（95%CI: 0.671-0.840）, respectively. The Hosmer-Lemeshow goodness of fit test showed that the prediction rate of the model was consistent with the actual incidence rate（P=0.918; P=0.581; P=0.366）. With ≥0.434 as the diagnostic threshold, the model had the highest diagnostic efficacy （sensitivity: 86.0%, specificity: 56.3%, Youden index: 0.423）. In the external validation cohort, there was no significant difference between C-US and CT（P>0.05）. Compared with C-US and CT, the sensitivity（66.1% vs 16.1%, P<0.001; 66.1% vs 9.7%, P<0.001） and accuracy（68.6% vs 55.4%, P=0.041; 68.6% vs 52.9%, P=0.012） of ATR model were higher, and the negative predictive value was higher than that of CT（66.7% vs 50.9%, P=0.042）, but there was no difference between ATR model and C-US（66.7% vs 52.3%, P=0.066）; There was no significant difference among the three positive predictive values（70.7% vs 83.3%, P=0.211; 70.7% vs 85.7%, P=0.319）, but the specificity of the model was low （71.2% vs 96.6%, P=0.001; 71.2% vs 98.3%, P<0.001）. Conclusion:The predictive model based on ACR TI-RADS grades can predict CLNM of PTC more accurately and sensitively than traditional imaging examination.

Long-Term Efficacy of Ultrasound-Guided Percutaneous Laser Ablation for Low-Risk Papillary Thyroid Microcarcinoma: A 5-Year Follow-Up Study.

Objective To evaluate the long-term efficacy and safety of ultrasound-guided percutaneous laser ablation (PLA) for the treatment of low-risk papillary thyroid microcarcinoma (PTMC). Methods From June 2012 to May 2015, 105 patients with solitary, pathologically confirmed PTMC lesions were treated with ultrasound-guided PLA. Nodule location, nodule volume, thyroid function, and clinical symptoms were evaluated before ablation. Contrast-enhanced ultrasound (CEUS) was performed 1 h after treatment to evaluate whether the ablation was complete. Ultrasound examination was performed at 1, 3, 6, and 12 months after ablation and every 6 months thereafter to determine the size of the ablation area and search for recurrence in the thyroid parenchyma and lymph node metastasis. Thyroid function was examined before and 1 month after ablation. Fine needle aspiration biopsy was performed for any suspicious metastatic lymph nodes and recurrent lesions in the thyroid. Results All 105 lesions were completely inactivated after one ablation, making the success rate for single ablation 100%. The average ablation time was 2.78 ± 1.05 min, and the average ablation energy was 505 ± 185 J. All patients could tolerate and complete the ablation. No serious complications occurred during the treatment; only minor side effects such as pain and local discomfort were reported. The volume reduction rates were −781.14 ± 653.29% at 1 h posttreatment and −268.65 ± 179.57%, −98.39 ± 76.58%, 36.78 ± 30.32%, 75.55 ± 21.81%, 96.79 ± 10.57%, and 100% at 1, 3, 6, 12, 18, and 24 months after ablation, respectively. This rate remained 100% at the later follow-up times. Overall, 28 (26.67%), 74 (70.48%), 96 (91.43%), and 103 (100%) were completely absorbed by 6, 12, 18, and 24 months after PLA. One patient developed another lesion 12 months after ablation, and two patients had central cervical lymph node metastasis 24 months after ablation. Conclusion PLA is a safe and effective alternative clinical treatment for low-risk PTMC.

A Comparative Analysis of Six Machine Learning Models Based on Ultrasound to Distinguish the Possibility of Central Cervical Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma.

Current approaches to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) have failed to identify patients who would benefit from preventive treatment. Machine learning has offered the opportunity to improve accuracy by comparing the different algorithms. We assessed which machine learning algorithm can best improve CLNM prediction. This retrospective study used routine ultrasound data of 1,364 PTC patients. Six machine learning algorithms were compared to predict the possibility of CLNM. Predictive accuracy was assessed by sensitivity, specificity, positive predictive value, negative predictive value, and the area under the curve (AUC). The patients were randomly split into the training (70%), validation (15%), and test (15%) data sets. Random forest (RF) led to the best diagnostic model in the test cohort (AUC 0.731 ± 0.036, 95% confidence interval: 0.664–0.791). The diagnostic performance of the RF algorithm was most dependent on the following five top-rank features: extrathyroidal extension (27.597), age (17.275), T stage (15.058), shape (13.474), and multifocality (12.929). In conclusion, this study demonstrated promise for integrating machine learning methods into clinical decision-making processes, though these would need to be tested prospectively.

Nomogram to Assess the Risk of Central Cervical Lymph Node Metastasis in Patients With Clinical N0 Papillary Thyroid Carcinoma

ObjectiveTo develop and validate an individualized risk prediction model for the need for central cervical lymph node dissection in patients with clinical N0 papillary thyroid carcinoma (PTC) diagnosed using ultrasound. MethodsUpon retrospective review, derivation and internal validation cohorts comprised 1585 consecutive patients with PTC treated from January 2017 to December 2019 at hospital A. The external validation cohort consisted of 406 consecutive patients treated at hospital B from January 2016 to June 2020. Independent risk factors for central cervical lymph node metastasis (CLNM) were determined through univariable and multivariable logistic regression analysis. An individualized risk prediction model was constructed and illustrated as a nomogram, which was internally and externally validated. ResultsThe following risk factors of CLNM were established: a solitary primary thyroid nodule’s diameter, shape, calcification, and capsular abutment-to-lesion perimeter ratio. The areas under the receiver operating characteristic curves of the risk prediction model for the internal and external validation cohorts were 0.921 and 0.923, respectively. The calibration curve showed good agreement between the nomogram-estimated probability of CLNM and the actual CLNM rates in the 3 cohorts. The decision curve analysis confirmed the clinical usefulness of the nomogram. ConclusionThis study developed and validated a model for predicting the risk of CLNM in individual patients with clinical N0 PTC, which should be an efficient tool for guiding clinical treatment.

Extreme Gradient Boosting Model to Assess Risk of Central Cervical Lymph Node Metastasis in Patients with Papillary Thyroid Carcinoma: Individual prediction using SHapley Additive exPlanations

Extreme Gradient Boosting Model to Assess Risk of Central Cervical Lymph Node Metastasis in Patients with Papillary Thyroid Carcinoma: Individual prediction using SHapley Additive exPlanations