Preoperative diagnosis of cervical metastatic lymph nodes in papillary thyroid carcinoma: comparison of ultrasound, computed tomography, and combined ultrasound with computed tomography

Predicting central lymph node metastasis in papillary thyroid cancer: A nomogram based on clinical, ultrasound and contrast‑enhanced computed tomography characteristics

Central lymph node (CLN) status is considered to be an important risk factor in patients with papillary thyroid carcinoma (PTC). The aim of the present study was to identify risk factors associated with CLN metastasis (CLNM) for patients with PTC based on preoperative clinical, ultrasound (US) and contrast-enhanced computed tomography (CT) characteristics, and establish a prediction model for treatment plans. A total of 786 patients with a confirmed pathological diagnosis of PTC between January 2021 to December 2022 were included in the present retrospective study, with 550 patients included in the training group and 236 patients enrolled in the validation group (ratio of 7:3). Based on the preoperative clinical, US and contrast-enhanced CT features, univariate and multivariate logistic regression analyses were used to determine the independent predictive factors of CLNM, and a personalized nomogram was constructed. Calibration curve, receiver operating characteristic (ROC) curve and decision curve analyses were used to assess discrimination, calibration and clinical application of the prediction model. As a result, 38.9% (306/786) of patients with PTC and CLNM(-) status before surgery had confirmed CLNM using postoperative pathology. In multivariate analysis, a young age (≤45 years), the male sex, no presence of Hashimoto thyroiditis, isthmic location, microcalcification, inhomogeneous enhancement and capsule invasion were independent predictors of CLNM in patients with PTC. The nomogram integrating these 7 factors exhibited strong discrimination in both the training group [Area under the curve (AUC)=0.826] and the validation group (AUC=0.818). Furthermore, the area under the ROC curve for predicting CLNM based on clinical, US and contrast-enhanced CT features was higher than that without contrast-enhanced CT features (AUC=0.818 and AUC=0.712, respectively). In addition, the calibration curve was appropriately fitted and decision curve analysis confirmed the clinical utility of the nomogram. In conclusion, the present study developed a novel nomogram for preoperative prediction of CLNM, which could provide a basis for prophylactic central lymph node dissection in patients with PTC.

Ultrasound-Base Radiomics for Discerning Lymph Node Metastasis in Thyroid Cancer: A Systematic Review and Meta-analysis

PURPOSE

Ultrasound is the imaging modality of choice for preoperative diagnosis of lymph node metastasis (LNM) in thyroid cancer (TC), yet its efficacy remains suboptimal. As radiomics gains traction in tumor diagnosis, its integration with ultrasound for LNM differentiation in TC has emerged, but its diagnostic merit is debated. This study assesses the accuracy of ultrasound-integrated radiomics in preoperatively diagnosing LNM in TC.

METHODS

Literatures were searched in PubMed, Embase, Cochrane, and Web of Science until July 11, 2023. Quality of the studies was assessed by the radiomics quality score (RQS). A meta-analysis was executed using a bivariate mixed effects model, with a subgroup analysis based on modeling variables (clinical features, radiomics features, or their combination).

RESULTS

Among 27 articles (16,410 TC patients, 6356 with LNM), the average RQS was 16.5 (SD:5.47). Sensitivity of the models based on clinical features, radiomics features, and radiomics features plus clinical features were 0.64, 0.76 and 0.69. Specificities were 0.77, 0.78 and 0.82. SROC values were 0.76, 0.84 and 0.81.

CONCLUSION

Ultrasound-based radiomics effectively evaluates LNM in TC preoperatively. Adding clinical features does not notably enhance the model's performance. Some radiomics studies showed high bias, possibly due to the absence of standard application guidelines.

Malignancy risk of indeterminate lymph node at the central compartment in patients with thyroid cancer and concomitant sonographic thyroiditis

BACKGROUND

To evaluate the malignancy risk of sonographic (US) indeterminate lymph node (LN)s at the central compartment in thyroid cancer patients with US-thyroiditis (ST).

METHODS

Among the central compartments of suspicious, indeterminate, and probably benign LN US categories, the malignancy rates were compared between ST and non-US-thyroiditis (non-ST) groups. Those of indeterminate category were compared with suspicious and probably benign categories.

RESULTS

At 531 central compartments from 349 patients, the malignancy rate was lower in ST group (34.4% [44/128]) than non-ST group (43.4% [175/403]), although statistically not significant (p = 0.08). The malignancy rate of indeterminate category in ST group (35.7% [5/14]) was lower than non-ST group (71.9% [23/32]) (p = 0.047). Within ST group, the malignancy rate of indeterminate category (35.7% [5/14]) did not differ from probably benign category (29.1% [30/103]) (p = 0.756), but was lower than suspicious category (81.8% [9/11]) (p = 0.042).

CONCLUSIONS

The malignancy risk of US indeterminate LNs at the central compartment in thyroid cancer patients with US thyroiditis was lower than that in patients without US thyroiditis.

A model based on ultrasound and clinical factors to predict central lymph node metastasis in cN0 papillary thyroid microcarcinoma

Objective

The prevalence of thyroid malignancies has sharply elevated in the past few years, and a large number of newly diagnosed thyroid malignancies was papillary thyroid microcarcinomas (PTMC). The efficacy of prophylactic central lymph node dissection (PCLND) in patients with clinical lymph node-negative (cN0) PTMC is still debatable. In this study, we aimed to create a predictive model to assess the likelihood of central lymph node metastasis (CLNM) in cN0 PTMC.

Methods

Two hundred and fifty three patients diagnosed with cN0 PTMC who received surgery in the First People's Hospital of Kunshan from October 2018 to June 2023 were enrolled. Multivariate logistic regression was employed to evaluate the patient's clinical and ultrasonographic information to determine independent factors. Two prediction models were generated and their ability to evaluate the likelihood of CLNM in cN0 PTMC was determined and compared.

Results

Multivariate analysis based on clinical characteristics revealed that, CLNM was markedly linked to age, tumor size, and extrathyroidal infiltration in cN0 PTMC. Multivariate analysis utilizing clinical and ultrasound features demonstrated that age, tumor size, extrathyroidal infiltration, shape, microcalcification were independent risk factors for CLNM. The analysis of the receiver operating characteristic curve demonstrated that the predictive nomogram utilizing clinical and ultrasound features was more beneficial for predicting CLNM. And decision curve demonstrates the same. The model's calibration curve exhibited strong consistency.

Conclusions

Age, tumor size, extrathyroidal infiltration, shape, microcalcification are significant independent factors of CLNM in cN0 PTMC. A predictive model derived from these independent clinical and ultrasound factors has a good value, but further validation is still required.

Development and validation of a clinical predictive model for high-volume lymph node metastasis of papillary thyroid carcinoma

The central lymph node metastasis (CLNM) status in the cervical region serves as a pivotal determinant for the extent of surgical intervention and prognosis in papillary thyroid carcinoma (PTC). This paper seeks to devise and validate a predictive model based on clinical parameters for the early anticipation of high-volume CLNM (hv-CLNM, > 5 nodes) in high-risk patients. A retrospective analysis of the pathological and clinical data of patients with PTC who underwent surgical treatment at Medical Centers A and B was conducted. The data from Center A was randomly divided into training and validation sets in an 8:2 ratio, with those from Center B serving as the test set. Multifactor logistic regression was harnessed in the training set to select variables and construct a predictive model. The generalization ability of the model was assessed in the validation and test sets. The model was evaluated through the receiver operating characteristic area under the curve (AUC) to predict the efficiency of hv-CLNM. The goodness of fit of the model was examined via the Brier verification technique. The incidence of hv-CLNM in 5897 PTC patients attained 4.8%. The occurrence rates in males and females were 9.4% (128/1365) and 3.4% (156/4532), respectively. Multifactor logistic regression unraveled male gender (OR = 2.17, p < .001), multifocality (OR = 4.06, p < .001), and lesion size (OR = 1.08 per increase of 1 mm, p < .001) as risk factors, while age emerged as a protective factor (OR = 0.95 per an increase of 1 year, p < .001). The model constructed with four predictive variables within the training set exhibited an AUC of 0.847 ([95%CI] 0.815-0.878). In the validation and test sets, the AUCs were 0.831 (0.783-0.879) and 0.845 (0.789-0.901), respectively, with Brier scores of 0.037, 0.041, and 0.056. Subgroup analysis unveiled AUCs for the prediction model in PTC lesion size groups (≤ 10 mm and > 10 mm) as 0.803 (0.757-0.85) and 0.747 (0.709-0.785), age groups (≤ 31 years and > 31 years) as 0.778 (0.720-0.881) and 0.837 (0.806-0.867), multifocal and solitary cases as 0.803 (0.767-0.838) and 0.809 (0.769-0.849), and Hashimoto's thyroiditis (HT) and non-HT cases as 0.845 (0.793-0.897) and 0.845 (0.819-0.871). Male gender, multifocality, and larger lesion size are risk factors for hv-CLNM in PTC patients, whereas age serves as a protective factor. The clinical predictive model developed in this research facilitates the early identification of high-risk patients for hv-CLNM, thereby assisting physicians in more efficacious risk stratification management for PTC patients.

Enhancement on CT for preoperative diagnosis of metastatic lymph nodes in thyroid cancer: a comparison across experience levels

OBJECTIVES

To investigate the diagnostic performance and interobserver agreement of quantitative CT parameters indicating strong lymph node (LN) enhancement in differentiated thyroid cancer (DTC), comparing them with qualitative analysis by radiologists of varying experience.

MATERIALS AND METHODS

This study included 463 LNs from 399 patients with DTC. Three radiologists independently analyzed strong LN enhancement on CT. Qualitative analysis of strong enhancement was defined as LN cortex showing greater enhancement than adjacent muscles on the arterial phase. Quantitative analysis included the mean attenuation value (MAV) of LN on arterial phase (LNA) and venous phase (LNV), LNA normalized to the common carotid artery (NAVCCA), internal jugular vein (NAVIJV), and sternocleidomastoid muscle (NAVSCM), attenuation difference [AD; (LNA - MAVSCM)], and relative washout ratio [((LNA - LNV)/LNA) × 100]. The interobserver agreement and diagnostic performance of the quantitative and qualitative analyses were evaluated.

RESULTS

Interobserver agreement was excellent for all quantitative CT parameters (ICC, 0.83-0.94) and substantial for qualitative assessment (κ = 0.61). All CT parameters except for LNV showed good diagnostic performance for metastatic LNs (AUC, 0.81-0.85). NAVCCA (0.85, 95% CI: 0.8-0.9) and AD (0.85, 95% CI: 0.81-0.89) had the highest AUCs. All quantitative parameters except for NAVIJV had significantly higher AUCs than qualitative assessments by inexperienced radiologists, with no significant difference from assessments by an experienced radiologist.

CONCLUSION

Quantitative assessment of LN enhancement on arterial phase CT showed higher interobserver agreement and AUC values than qualitative analysis by inexperienced radiologists, supporting the need for a standardized quantitative CT parameter-based model for determining strong LN enhancement.

CLINICAL RELEVANCE STATEMENT

When assessing strong LN enhancement in DTC, quantitative CT parameters indicating strong enhancement can improve interobserver agreement, regardless of experience level. Therefore, the development of a standardized diagnostic model based on quantitative CT parameters might be necessary.

KEY POINTS

Accurate preoperative assessment of LN metastasis in thyroid cancer is crucial. Quantitative CT parameters indicating strong LN enhancement demonstrated excellent interobserver agreement and good diagnostic performance. Quantitative assessment of contrast enhancement offers a more objective model for the identification of metastatic LNs.

CT-Based Radiomics Models for Differentiation of Benign and Malignant Thyroid Nodules: A Multicenter Development and Validation Study

BACKGROUND. CT is increasingly detecting thyroid nodules. Prior studies indicated a potential role of CT-based radiomics models in characterizing thyroid nodules, although these studies lacked external validation. OBJECTIVE. The purpose of this study was to develop and validate a CT-based radiomics model for the differentiation of benign and malignant thyroid nodules. METHODS. This retrospective study included 378 patients (mean age, 46.3 ± 13.9 [SD] years; 86 men, 292 women) with 408 resected thyroid nodules (145 benign, 263 malignant) from two centers (center 1: 293 nodules, January 2018 to December 2022; center 2: 115 nodules, January 2020 to December 2022) who underwent preoperative multiphase neck CT (noncontrast, arterial, and venous phases). Nodules from center 1 were divided into training (n = 206) and internal validation (n = 87) sets; all nodules from center 2 formed an external validation set. Radiologists assessed nodules for morphologic CT features. Nodules were manually segmented on all phases, and radiomic features were extracted. Conventional (clinical and morphologic CT), noncontrast CT radiomics, arterial phase CT radiomics, venous phase CT radiomics, multiphase CT radiomics, and combined (clinical, morphologic CT, and multiphase CT radiomics) models were established using feature selection methods and evaluated by ROC curve analysis, calibration-curve analysis, and decision-curve analysis. RESULTS. The combined model included patient age, three morphologic features (cystic change, "edge interruption" sign, abnormal cervical lymph nodes), and 28 radiomic features (from all three phases). In the external validation set, the combined model had an AUC of 0.923, and, at an optimal threshold derived in the training set, sensitivity of 84.0%, specificity of 94.1%, and accuracy of 87.0%. In the external validation set, the AUC was significantly higher for the combined model than for the conventional model (0.827), noncontrast CT radiomics model (0.847), arterial phase CT radiomics model (0.826), venous phase CT radiomics model (0.773), and multiphase CT radiomics model (0.824) (all p < .05). In the external validation set, the calibration curves indicated the lowest (i.e., best) Brier score for the combined model; in the decision-curve analysis, the combined model had the highest net benefit for most of the range of threshold probabilities. CONCLUSION. A combined model incorporating clinical, morphologic CT, and multiphase CT radiomics features exhibited robust performance in differentiating benign and malignant thyroid nodules. CLINICAL IMPACT. The combined radiomics model may help guide further management for thyroid nodules detected on CT.

A CT based radiomics analysis to predict the CN0 status of thyroid papillary carcinoma: a two- center study

OBJECTIVES

To develop and validate radiomics model based on computed tomography (CT) for preoperative prediction of CN0 status in patients with papillary thyroid carcinoma (PTC).

METHODS

A total of 548 pathologically confirmed LNs (243 non-metastatic and 305 metastatic) two distinct hospitals were retrospectively assessed. A total of 396 radiomics features were extracted from arterial-phase CT images, where the strongest features containing the most predictive potential were further selected using the least absolute shrinkage and selection operator (LASSO) regression method. Delong test was used to compare the AUC values of training set, test sets and cN0 group.

RESULTS

The Rad-score showed good discriminating performance with Area Under the ROC Curve (AUC) of 0.917(95% CI, 0.884 to 0.950), 0.892 (95% CI, 0.833 to 0.950) and 0.921 (95% CI, 868 to 0.973) in the training, internal validation cohort and external validation cohort, respectively. The test group of CN0 with a AUC of 0.892 (95% CI, 0.805 to 0.979). The accuracy was 85.4% (sensitivity = 81.3%; specificity = 88.9%) in the training cohort, 82.9% (sensitivity = 79.0%; specificity = 88.7%) in the internal validation cohort, 85.4% (sensitivity = 89.7%; specificity = 83.8%) in the external validation cohort, 86.7% (sensitivity = 83.8%; specificity = 91.3%) in the CN0 test group.The calibration curve demonstrated a significant Rad-score (P-value in H-L test > 0.05). The decision curve analysis indicated that the rad-score was clinically useful.

CONCLUSIONS

Radiomics has shown great diagnostic potential to preoperatively predict the status of cN0 in PTC.

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

RATIONALE AND OBJECTIVES

This study aims to develop and validate a computed tomography (CT)-based radiomics nomogram for pre-operatively predicting central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC) and explore the underlying biological basis by using RNA sequencing data.

METHODS

This study trained 452 PTMC patients across two hospitals from January 2012 to December 2020. The sets were randomly divided into the training (n = 339), internal test (n = 86), external test (n = 27) sets. Radiomics features were extracted from primary lesion's pre-operative CT images for each patient. After screening for features, five algorithms such as K-nearest neighbor, logistics regression, linear-support vector machine (SVM), Gaussian SVM, and polynomial SVM were used to establish the radiomics models. The performance of these five algorithms was evaluated and compared directly to radiologist's interpretation (CT-reported lymph node status). The radiomics signature score (Rad-score) was generated using a linear combination of the selected features. By combining the clinical risk factors and Rad score, a radiomics nomogram was established and compared with Rad-score and clinical model. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve analysis (DCA). The potential biological basis of nomogram was revealed by performing genetic analysis based on the RNA sequencing data.

RESULTS

A total of 25 radiomic features were ultimately selected to train the machine learning models, and the five machine learning models outperformed the radiologists' interpretation by achieving area under the ROC curves (AUCs) ranging from 0.606 to 0.730 in the internal test set. By incorporating the Rad score and clinical risk factors (sex, age, tumor-diameter, and CT-reported lymph node status), this nomogram achieved AUCs of 0.800 and 0.803 in the internal and external test set, which were higher than that of the Rad-score and clinical model, respectively. Calibration curves and DCA also showed that the nomogram had good performance. As for the biological basis exploration, in patients predicted by nomogram to be PTC patients with CLMN, 109 genes were dysregulated, and some of them were associated with pathways and biological processes such as tumor angiogenesis.

CONCLUSION

This radiomics nomogram successfully identified CLNM on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists and had the potential to be integrated into clinical decision making as a non-invasive pre-operative tool.

The frequency of risk pathological characteristics in clinically low-risk papillary thyroid microcarcinoma suitable for active surveillance

PURPOSE

Active surveillance has emerged as an initial management strategy for patients with low-risk papillary thyroid microcarcinoma (PTMC). The main objective of this research was to investigate the frequency of risk pathological characteristics among patients with clinically low-risk PTMC who are suitable for Active Surveillance.

METHODS

A retrospective review was conducted on patients who underwent lobectomy for PTMC between January 2013 and December 2018. Patients with bilateral tumors, macroscopic multifocal tumors, macroscopic extrathyroidal extension (ETE), clinical lymph node metastases, macroscopic extranodal extension (ENE), distant metastases, a history of neck radiation or familial thyroid cancer were excluded. Pathological characteristics were collected from the postoperative pathological results. Aggressive variants, multifocality, ETE, lymphovascular invasion (LVI), perineural invasion (PNI), metastatic lymph nodes (LNs) ≥ 5, and ENE were defined as risk characteristics.

RESULTS

The study included 4923 patients, of whom 1229 (25.0%) were male. The mean age was 43 years. A total of 2250 patients (45.7%) exhibited risk characteristics. Among them, 15 patients presented with aggressive variants, and 1813 patients (36.8%) had ETE. Multifocality, LVI, and PNI were observed in 551 (11.2%), 21 (0.4%), and 40 (0.8%) patients, respectively. A total of 139 patients (2.8%) had five or more metastatic LNs, and ENE was identified in 140 patients. Notably, 172 patients (3.5%) fulfilled the criteria for completion thyroidectomy, as they had aggressive variants, LVI, or five or more metastatic LNs.

CONCLUSIONS

Nearly half of the patients diagnosed with clinically low-risk PTMC exhibited risk pathological characteristics, and a small proportion of patients met the criteria for completion thyroidectomy.

Development of a nomogram for prediction of central lymph node metastasis of papillary thyroid microcarcinoma

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most frequent malignant tumor in thyroid carcinoma. The aim of this study was to explore the risk factors associated with central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) and establish a nomogram model that can assess the probability of central lymph node metastasis (CLNM).

METHODS

The clinicopathological data of 377 patients with cN0 PTMC were collected and analyzed from The Second Affiliated Hospital of Fujian Medical University from July 1st, 2019 to December 30th, 2021. All patients were examined by underwent ultrasound (US), found without metastasis to central lymph nodes, and diagnosed with PTMC through pathologic examination. All patients received thyroid lobectomy or total thyroidectomy with therapeutic or prophylactic central lymph node dissection (CLND). R software (Version 4.1.0) was employed to conduct a series of statistical analyses and establish the nomogram.

RESULTS

A total of 119 patients with PTMC had central lymph node metastases (31.56%). After that, age (P < 0.05), gender (P < 0.05), tumor size (P < 0.05), tumor multifocality (P < 0.05), and ultrasound imaging-suggested tumor boundaries (P < 0.05) were identified as the risk factors associated with CLNM. Subsequently, multivariate logistic regression analysis indicated that the area under the receiver operating characteristic (ROC) curve (AUC) of the training cohort was 0.703 and that of the validation cohort was 0.656, demonstrating that the prediction ability of this model is relatively good compared to existing models. The calibration curves indicated a good fit for the nomogram model. Finally, the decision curve analysis (DCA) showed that a probability threshold of 0.15-0.50 could benefit patients clinically. The probability threshold used in DCA captures the relative value the patient places on receiving treatment for the disease, if present, compared to the value of avoiding treatment if the disease is not present.

CONCLUSION

CLNM is associated with many risk factors, including age, gender, tumor size, tumor multifocality, and ultrasound imaging-suggested tumor boundaries. The nomogram established in our study has moderate predictive ability for CLNM and can be applied to the clinical management of patients with PTMC. Our findings will provide a better preoperative assessment and treatment strategies for patients with PTMC whether to undergo central lymph node dissection.

Socioeconomic disparities and regional environment are associated with cervical lymph node metastases in children and adolescents with differentiated thyroid cancer: developing a web-based predictive model

Purpose

To establish an online predictive model for the prediction of cervical lymph node metastasis (CLNM) in children and adolescents with differentiated thyroid cancer (caDTC). And analyze the impact between socioeconomic disparities, regional environment and CLNM.

Methods

We retrospectively analyzed clinicopathological and sociodemographic data of caDTC from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2019. Risk factors for CLNM in caDTC were analyzed using univariate and multivariate logistic regression (LR). And use the extreme gradient boosting (XGBoost) algorithm and other commonly used ML algorithms to build CLNM prediction models. Model performance assessment and visualization were performed using the area under the receiver operating characteristic (AUROC) curve and SHapley Additive exPlanations (SHAP).

Results

In addition to common risk factors, our study found that median household income and living regional were strongly associated with CLNM. Whether in the training set or the validation set, among the ML models constructed based on these variables, the XGBoost model has the best predictive performance. After 10-fold cross-validation, the prediction performance of the model can reach the best, and its best AUROC value is 0.766 (95%CI: 0.745-0.786) in the training set, 0.736 (95%CI: 0.670-0.802) in the validation set, and 0.733 (95%CI: 0.683-0.783) in the test set. Based on this XGBoost model combined with SHAP method, we constructed a web-base predictive system.

Conclusion

The online prediction model based on the XGBoost algorithm can dynamically estimate the risk probability of CLNM in caDTC, so as to provide patients with personalized treatment advice.

Nomogram model based on preoperative clinical characteristics of unilateral papillary thyroid carcinoma to predict contralateral medium-volume central lymph node metastasis

Objectives

To explore the preoperative high-risk clinical factors for contralateral medium-volume central lymph node metastasis (conMVCLNM) in unilateral papillary thyroid carcinoma (uPTC) and the indications for dissection of contralateral central lymph nodes (conCLN).

Methods

Clinical and pathological data of 204 uPTC patients who underwent thyroid surgery at the Hangzhou First People's Hospital from September 2010 to October 2022 were collected. Univariate and multivariate logistic regression analyses were conducted to determine the independent risk factors for contralateral central lymph node metastasis (conCLNM) and conMVCLNM in uPTC patients based on the preoperative clinical data. Predictive models for conCLNM and conMVCLNM were constructed using logistic regression analyses and validated using receiver operating characteristic (ROC) curves, concordance index (C-index), calibration curves, and decision curve analysis (DCA).

Results

Univariate and multivariate logistic regression analyses showed that gender (P < 0.001), age (P < 0.001), tumor diameter (P < 0.001), and multifocality (P = 0.008) were independent risk factors for conCLNM in uPTC patients. Gender(P= 0.026), age (P = 0.010), platelet-to-lymphocyte ratio (PLR) (P =0.003), and tumor diameter (P = 0.036) were independent risk factors for conMVCLNM in uPTC patients. A predictive model was established to assess the risk of conCLNM and conMVCLNM, with ROC curve areas of 0.836 and 0.845, respectively. The C-index, the calibration curve, and DCA demonstrated that the model had good diagnostic value.

Conclusion

Gender, age, tumor diameter, and multifocality are high-risk factors for conCLNM in uPTC patients. Gender, age, tumor diameter, and PLR are high-risk factors for conMVCLNM in uPTC patients, and preventive conCLN dissection should be performed.

Deep learning-assisted ultrasonic diagnosis of cervical lymph node metastasis of thyroid cancer: a retrospective study of 3059 patients

Objective

This study aimed to develop a deep learning system to identify and differentiate the metastatic cervical lymph nodes (CLNs) of thyroid cancer.

Methods

From January 2014 to December 2020, 3059 consecutive patients with suspected with metastatic CLNs of thyroid cancer were retrospectively enrolled in this study. All CLNs were confirmed by fine needle aspiration. The patients were randomly divided into the training (1228 benign and 1284 metastatic CLNs) and test (307 benign and 240 metastatic CLNs) groups. Grayscale ultrasonic images were used to develop and test the performance of the Y-Net deep learning model. We used the Y-Net network model to segment and differentiate the lymph nodes. The Dice coefficient was used to evaluate the segmentation efficiency. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the classification efficiency.

Results

In the test set, the median Dice coefficient was 0.832. The sensitivity, specificity, accuracy, PPV, and NPV were 57.25%, 87.08%, 72.03%, 81.87%, and 66.67%, respectively. We also used the Y-Net classified branch to evaluate the classification efficiency of the LNs ultrasonic images. The classification branch model had sensitivity, specificity, accuracy, PPV, and NPV of 84.78%, 80.23%, 82.45%, 79.35%, and 85.61%, respectively. For the original ultrasonic reports, the sensitivity, specificity, accuracy, PPV, and NPV were 95.14%, 34.3%, 64.66%, 59.02%, 87.71%, respectively. The Y-Net model yielded better accuracy than the original ultrasonic reports.

Conclusion

The Y-Net model can be useful in assisting sonographers to improve the accuracy of the classification of ultrasound images of metastatic CLNs.

Ultrasound-Based Nomogram for Predicting the Aggressiveness of Papillary Thyroid Carcinoma in Adolescents and Young Adults

RATIONALE AND OBJECTIVES

Assessing the aggressiveness of papillary thyroid carcinoma (PTC) preoperatively might play an important role in guiding therapeutic strategy. This study aimed to develop and validate a nomogram that integrated ultrasound (US) features with clinical characteristics to preoperatively predict aggressiveness in adolescents and young adults with PTC.

MATERIALS AND METHODS

In this retrospective study, a total of 2373 patients were enrolled and randomly divided into two groups with 1000 bootstrap sampling. The multivariable logistic regression (LR) analysis or least absolute shrinkage and selection operator LASSO regression was applied to select predictive US and clinical characteristics in the training cohort. By incorporating most powerful predictors, two predictive models presented as nomograms were developed, and their performance was assessed with respect to discrimination, calibration, and clinical usefulness.

RESULTS

The LR_model that incorporated gender, tumor size, multifocality, US-reported cervical lymph nodes (CLN) status, and calcification demonstrated good discrimination and calibration with an area under curve (AUC), sensitivity and specificity of 0.802 (0.781-0.821), 65.58% (62.61%-68.55%), and 82.31% (79.33%-85.46%), respectively, in the training cohort; and 0.768 (0.736-0.797), 60.04% (55.62%-64.46%), and 83.62% (78.84%-87.71%), respectively, in the validation cohort. Gender, tumor size, orientation, calcification, and US-reported CLN status were combined to build LASSO_model. Compared with LR_model, the LASSO_model yielded a comparable diagnostic performance in both cohorts, the AUC, sensitivity, and specificity were 0.800 (0.780-0.820), 65.29% (62.26%-68.21%), and 81.93% (78.77%-84.91%), respectively, in the training cohort; and 0.763 (0.731-0.792), 59.43% (55.12%-63.93%), and 84.98% (80.89%-89.08%), respectively, in the validation cohort. The decision curve analysis indicated that using the two nomograms to predict the aggressiveness of PTC provided a greater benefit than either the treat-all or treat-none strategy.

CONCLUSION

Through these two easy-to-use nomograms, the possibility of the aggressiveness of PTC in adolescents and young adults can be objectively quantified preoperatively. The two nomograms may serve as a useful clinical tool to provide valuable information for clinical decision-making.

Impact of prophylactic central lymph node dissection on the complications and recurrence rates in papillary thyroid carcinoma-An AFCE (French-speaking Association of Endocrine Surgery) multicentre study based on the EUROCRINE® national data

BACKGROUND

Prophylactic central neck dissection (pCND) remains controversial during the initial surgery for preoperative and intraoperative node-negative (cN0) papillary thyroid carcinoma (PTC).

METHODS

Patients undergoing thyroidectomy with or without pCND (Nx) for PTC in nine French surgical departments, registered in the EUROCRINE® national data in France between January 2015 and June 2021, were included in a cohort study. Demographic and clinicopathological characteristics, complications, and recurrence rates were compared using multivariate regression analysis.

RESULTS

A total of 1905 patients with cN0 PTC were enrolled, including 1534 who had undergone pCND and 371 who hadn't (Nx). Of these, 1546 (81.2%) were female, and the median age was 49 years (range: 15-89 years). Patients who had undergone pCND were more likely to have multifocal tumors (n = 524 [34.2%] vs. n = 68 [18.3%], p < .001) and larger tumors (15.3 vs. 10.2 mm, p = .01) than patients with Nx. Of the patients with pCND, 553 (36%) had positive central LN (N1a), with a median of 1 N1 (IQR 0-5). pCND was associated with a higher temporary hypocalcemia rate (n = 25 [8%] vs. n = 15 [4%], p < .001). The rates of permanent hypocalcemia and temporary and permanent recurrent laryngeal nerve (RLN) palsy were not significantly different between the two groups (p > .2). After adjusting for covariates (age, sex, multifocality, and pathological T stage) in a multivariable Cox PH model, the performance of lymph node dissection (pCND vs. no-pCND) was not associated with PTC recurrence (p = .2).

CONCLUSION

pCND in PTC does not reduce recurrence and is associated with a two-fold increase in the incidence of transient hypoparathyroidism. These data should be considered while issuing further guidelines regarding the treatment of patients with cN0 PTC.

Prediction of cervical lymph node metastasis in solitary papillary thyroid carcinoma based on ultrasound radiomics analysis

Objective

To assess the utility of predictive models using ultrasound radiomic features to predict cervical lymph node metastasis (CLNM) in solitary papillary thyroid carcinoma (PTC) patients.

Methods

A total of 570 PTC patients were included (456 patients in the training set and 114 in the testing set). Pyradiomics was employed to extract radiomic features from preoperative ultrasound images. After dimensionality reduction and meticulous selection, we developed radiomics models using various machine learning algorithms. Univariate and multivariate logistic regressions were conducted to identify independent risk factors for CLNM. We established clinical models using these risk factors. Finally, we integrated radiomic and clinical models to create a combined nomogram. We plotted ROC curves to assess diagnostic performance and used calibration curves to evaluate alignment between predicted and observed probabilities.

Results

A total of 1561 radiomics features were extracted from preoperative ultrasound images. After dimensionality reduction and feature selection, 16 radiomics features were identified. Among radiomics models, the logistic regression (LR) model exhibited higher predictive efficiency. Univariate and multivariate logistic regression results revealed that patient age, tumor size, gender, suspicious cervical lymph node metastasis, and capsule contact were independent predictors of CLNM (all P < 0.05). By constructing a clinical model, the LR model demonstrated favorable diagnostic performance. The combined model showed superior diagnostic efficacy, with an AUC of 0.758 (95% CI: 0.712-0.803) in the training set and 0.759 (95% CI: 0.669-0.849) in the testing set. In the training dataset, the AUC value of the nomogram was higher than that of the clinical and radiomics models (P = 0.027 and 0.002, respectively). In the testing dataset, the AUC value of the nomogram model was also greater than that of the radiomics models (P = 0.012). However, there was no significant statistical difference between the nomogram and the clinical model (P = 0.928). The calibration curve indicated a good fit of the combined model.

Conclusion

Ultrasound radiomics technology offers a quantitative and objective method for predicting CLNM in PTC patients. Nonetheless, the clinical indicators persists as irreplaceable.

Effect of Hashimoto's thyroiditis on the dual-energy CT quantitative parameters and performance in diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer

BACKGROUND

To evaluate the effect of Hashimoto's thyroiditis (HT) on dual-energy computed tomography (DECT) quantitative parameters of cervical lymph nodes (LNs) in patients with papillary thyroid cancer (PTC), and its effect on the diagnostic performance and threshold of DECT in preoperatively identifying metastatic cervical LNs.

METHODS

A total of 479 LNs from 233 PTC patients were classified into four groups: HT+/LN+, HT+/LN-, HT-/LN + and HT-/LN - group. DECT quantitative parameters including iodine concentration (IC), normalized IC (NIC), effective atomic number (Zeff), and slope of the spectral Hounsfield unit curve (λHU) in the arterial phase (AP) and venous phase were compared. Receiver operating characteristic curve analyses were performed to evaluate DECT parameters' diagnostic performance in differentiating metastatic from nonmetastatic LNs in the HT - and HT + groups.

RESULTS

The HT+/LN + group exhibited lower values of DECT parameters than the HT-/LN + group (all p < 0.05). Conversely, the HT+/LN - group exhibited higher values of DECT parameters than the HT-/LN - group (all p < 0.05). In the HT + group, if an AP-IC of 1.850 mg/mL was used as the threshold value, then the optimal diagnostic performance (area under the curve, 0.757; sensitivity, 69.4%; specificity, 71.0%) could be obtained. The optimal threshold value of AP-IC in the HT - group was 2.050 mg/mL. In contrast, in the HT - group, AP-NIC demonstrated the highest area under the curve of 0.988, when an optimal threshold of 0.243 was used. The optimal threshold value of AP-NIC was 0.188 in the HT + group.

CONCLUSIONS

HT affected DECT quantitative parameters of LNs and subsequent the diagnostic thresholds. When using DECT to diagnose metastatic LNs in patients with PTC, whether HT is coexistent should be clarified considering the different diagnostic thresholds.

A prediction model for identifying high-risk lymph node metastasis in clinical low-risk papillary thyroid microcarcinoma

BACKGROUND

The presence of high-volume lymph node metastasis (LNM) and extranodal extension (ENE) greatly increases the risk of recurrence in patients with low-risk papillary thyroid microcarcinoma (PTMC). The goal of this research was to analyze the factors that contribute to high-risk lymph node metastasis in patients with low-risk PTMC.

METHODS

We analyzed the records of 7344 patients who were diagnosed with low-risk PTMC and treated at our center from January 2013 to June 2018.LNM with a high volume or ENE was classified as high-risk lymph node metastasis (hr-LNM). A logistic regression analysis was conducted to identify the risk factors associated with hr-LNM. A nomogram was created and verified using risk factors obtained from LASSO regression analysis, to predict the likelihood of hr-LNM.

RESULTS

The rate of hr-LNM was 6.5%. LASSO regression revealed six variables that independently contribute to hr-LNM: sex, age, tumor size, tumor location, Hashimoto's thyroiditis (HT), and microscopic capsular invasion. A predictive nomogram was developed by integrating these risk factors, demonstrating its excellent performance. Upon analyzing the receiver operating characteristic (ROC) curve for predicting hr-LNM, it was observed that the area under the curve (AUC) had a value of 0.745 and 0.730 in the training and testing groups showed strong agreement, affirming great reliability.

CONCLUSION

Sex, age, tumor size, tumor location, HT, and microscopic capsular invasion were determined to be key factors associated with hr-LNM in low-risk PTMC. Utilizing these factors, a nomogram was developed to evaluate the risk of hr-LNM in patients with low-risk PTMC.

Lymph Node Metastases in Papillary Thyroid Carcinoma can be Predicted by a Convolutional Neural Network: a Multi-Institution Study

OBJECTIVES

The presence of nodal metastases in patients with papillary thyroid carcinoma (PTC) has both staging and treatment implications. However, lymph nodes are often not removed during thyroidectomy. Prior work has demonstrated the capability of artificial intelligence (AI) to predict the presence of nodal metastases in PTC based on the primary tumor histopathology alone. This study aimed to replicate these results with multi-institutional data.

METHODS

Cases of conventional PTC were identified from the records of 2 large academic institutions. Only patients with complete pathology data, including at least 3 sampled lymph nodes, were included in the study. Tumors were designated "positive" if they had at least 5 positive lymph node metastases. First, algorithms were trained separately on each institution's data and tested independently on the other institution's data. Then, the data sets were combined and new algorithms were developed and tested. The primary tumors were randomized into 2 groups, one to train the algorithm and another to test it. A low level of supervision was used to train the algorithm. Board-certified pathologists annotated the slides. HALO-AI convolutional neural network and image software was used to perform training and testing. Receiver operator characteristic curves and the Youden J statistic were used for primary analysis.

RESULTS

There were 420 cases used in analyses, 45% of which were negative. The best performing single institution algorithm had an area under the curve (AUC) of 0.64 with a sensitivity and specificity of 65% and 61% respectively, when tested on the other institution's data. The best performing combined institution algorithm had an AUC of 0.84 with a sensitivity and specificity of 68% and 91% respectively.

CONCLUSION

A convolutional neural network can produce an accurate and robust algorithm that is capable of predicting nodal metastases from primary PTC histopathology alone even in the setting of multi-institutional data.

Role of computed tomography in the evaluation of regional metastasis in well-differentiated thyroid cancer

Background

Accurate neck staging is essential for performing appropriate surgery and avoiding undue morbidity in thyroid cancer. The modality of choice for evaluation is ultrasonography (US), which has limitations, particularly in the central compartment, that can be overcome by adding a computed tomography (CT).

Methods

A total of 314 nodal levels were analyzed in 43 patients with CT, and US; evaluations were done between January 2013 and November 2015. The images were reviewed by two radiologists independently who were blinded to histopathological outcomes. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy of US, CT, and US + CT were calculated using histology as the gold standard.

Results

The overall sensitivity, specificity, PPV, and NPV for US, CT, and US + CT were 53.9%, 88.8%, 74.1%, and 76.4%; 81.2%, 68.0%, 60.1%, and 85.9%; and 84.6%, 66.0%, 59.6%, and 87.8%, respectively. The overall accuracy of the US was 75.80%, the CT scan was 72.93%, and the US + CT scan was 72.93%. For the lateral compartment, the sensitivity, specificity, PPV, and NPV for the US, CT, and US + CT were 56.6%, 91.4%, 77.1%, and 80.5%; 80.7%, 70.6%, 58.3%, and 87.8%; and 84.3%, 68.7%, 57.9%, and 89.6%, respectively. The accuracy of the US was 79.67%, the CT scan was 73.98%, and the US + CT scan was 73.98% for the lateral compartment. For the central compartment, the sensitivity, specificity, PPV, and NPV for the US, CT, and US + CT were 47.1%, 76.5%, 66.7%, and 59.1%; 82.4%, 55.9%, 65.1%, and 76.0%; and 85.3%, 52.9%, 64.4%, and 78.3%, respectively. The accuracy of the US was 61.76%, the CT scan was 69.12%, and the US + CT scan was 69.12% for the central compartment.

Conclusions

This study demonstrated that CT has higher sensitivity in detecting nodal metastasis; however, its role is complementary to US due to low specificity.

Validation of CT-Based Risk Stratification System for Lymph Node Metastasis in Patients With Thyroid Cancer

OBJECTIVE

To evaluate the computed tomography (CT) features for diagnosing metastatic cervical lymph nodes (LNs) in patients with differentiated thyroid cancer (DTC) and validate the CT-based risk stratification system suggested by the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) guidelines.

MATERIALS AND METHODS

A total of 463 LNs from 399 patients with DTC who underwent preoperative CT staging and ultrasound-guided fine-needle aspiration were included. The following CT features for each LN were evaluated: absence of hilum, cystic changes, calcification, strong enhancement, and heterogeneous enhancement. Multivariable logistic regression analysis was performed to identify independent CT features associated with metastatic LNs, and their diagnostic performances were evaluated. LNs were classified into probably benign, indeterminate, and suspicious categories according to the K-TIRADS and the modified LN classification proposed in our study. The diagnostic performance of both classification systems was compared using the exact McNemar and Kosinski tests.

RESULTS

The absence of hilum (odds ratio [OR], 4.859; 95% confidence interval [CI], 1.593-14.823; P = 0.005), strong enhancement (OR, 28.755; 95% CI, 12.719-65.007; P < 0.001), and cystic changes (OR, 46.157; 95% CI, 5.07-420.234; P = 0.001) were independently associated with metastatic LNs. All LNs showing calcification were diagnosed as metastases. Heterogeneous enhancement did not show a significant independent association with metastatic LNs. Strong enhancement, calcification, and cystic changes showed moderate to high specificity (70.1%-100%) and positive predictive value (PPV) (91.8%-100%). The absence of the hilum showed high sensitivity (97.8%) but low specificity (34.0%). The modified LN classification, which excluded heterogeneous enhancement from the K-TIRADS, demonstrated higher specificity (70.1% vs. 62.9%, P = 0.016) and PPV (92.5% vs. 90.9%, P = 0.011) than the K-TIRADS.

CONCLUSION

Excluding heterogeneous enhancement as a suspicious feature resulted in a higher specificity and PPV for diagnosing metastatic LNs than the K-TIRADS. Our research results may provide a basis for revising the LN classification in future guidelines.

Artificial intelligence-based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT

OBJECTIVES

To develop an artificial intelligence (AI) system for predicting cervical lymph node metastasis (CLNM) preoperatively in patients with papillary thyroid cancer (PTC) based on CT images.

METHODS

This multicenter retrospective study included the preoperative CT of PTC patients who were divided into the development, internal, and external test sets. The region of interest of the primary tumor was outlined manually on the CT images by a radiologist who has eight years of experience. With the use of the CT images and lesions masks, the deep learning (DL) signature was developed by the DenseNet combined with convolutional block attention module. One-way analysis of variance and least absolute shrinkage and selection operator were used to select features, and a support vector machine was used to construct the radiomics signature. Random forest was used to combine the DL, radiomics, and clinical signature to perform the final prediction. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used by two radiologists (R1 and R2) to evaluate and compare the AI system.

RESULTS

For the internal and external test set, the AI system achieved excellent performance with AUCs of 0.84 and 0.81, higher than the DL (p = .03, .82), radiomics (p < .001, .04), and clinical model (p < .001, .006). With the aid of the AI system, the specificities of radiologists were improved by 9% and 15% for R1 and 13% and 9% for R2, respectively.

CONCLUSIONS

The AI system can help predict CLNM in patients with PTC, and the radiologists' performance improved with AI assistance.

CLINICAL RELEVANCE STATEMENT

This study developed an AI system for preoperative prediction of CLNM in PTC patients based on CT images, and the radiologists' performance improved with AI assistance, which could improve the effectiveness of individual clinical decision-making.

KEY POINTS

• This multicenter retrospective study showed that the preoperative CT image-based AI system has the potential for predicting the CLNM of PTC. • The AI system was superior to the radiomics and clinical model in predicting the CLNM of PTC. • The radiologists' diagnostic performance improved when they received the AI system assistance.

Quantitative analysis of dual-phase enhanced CT in cervical lymph node metastasis of papillary thyroid carcinoma: a comparative study along with pathological manifestations

PURPOSE

This study aimed to investigate the diagnostic value of dual-phase enhanced computed tomography (CT) in the cervical lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) by analyzing the dual-phase enhanced Hounsfield units (HUs) of lymph node and sternocleidomastoid muscle, and the ratio and difference.

METHODS

The CT arterial-phase and venous-phase imaging data of 143 metastasis-positive lymph nodes (MPLNs) in 88 cases and 172 metastasis-negative lymph nodes (MNLNs) in 128 cases with PTC were retrospectively analyzed. All lymph nodes were confirmed by surgical pathology. The arterial-phase HU of lymph nodes (ANHU), venous-phase HU of lymph nodes (VNHU), arterial-phase HU of the sternocleidomastoid muscle (AMHU) and venous-phase HU of the sternocleidomastoid muscle (VMHU) were measured, and their difference and ratio (ANHU-AMHU, ANHU/AMHU, VNHU-VMHU, VNHU/VMHU) were calculated. The cutoff values and corresponding diagnostic efficacy for diagnosing LNM in PTC were sought by performing the receiver operating characteristic curves. The maximum pathological diameter (MPD) measured on pathological sections of lymph nodes was compared with the maximum transverse diameter (MTD) and maximum sagittal diameter (MSD) and their average values on CT images.

RESULTS

The ANHU, and VNHU of MPLNs and MNLNs were 111.89 ± 33.26 and 66.12 (56.81-76.86) (P < 0.001), and 99.07 ± 23.27 and 75.47 ± 13.95 (P < 0.001), respectively. The area under the curve, sensitivity, and specificity of the arterial-phase three parameters (ANHU, ANHU-AMHU, ANHU/AMHU) for diagnosing LNM were (0.877-0.880), (0.755-0.769), and (0.901-0.913), respectively, and the venous-phase three parameters (VNHU, VNHU-VMHU, VNHU/VMHU) were (0.801-0.817), (0.650-0.678), and (0.826-0.901), respectively. Compared with MPD, MTD (Z = -2.686, P = 0.007) and MSD (Z = -3.539, P < 0.001) were significantly different, while (MTD + MSD)/2 was not statistically different (Z = -0.038b, P = 0.969).

CONCLUSION

In the differential diagnosis of cervical LNM of PTC by dual-phase enhanced CT angiography, the arterial phase had higher diagnostic efficacy.

Comprehensive analysis of clinicopathologic and sonographic features in thyroid cancer with skip lymph node metastasis: establish and assessment of a prediction nomogram

Lateral Lymph Node Metastasis (LLNM) is common in Papillary Thyroid Carcinoma (PTC) and is associated with a poor prognosis. LLNM without central lymph node metastasis as skip metastasis is not common. We aimed to investigate clinicopathologic and sonographic risk factors for skip metastasis in PTC patients, and to establish a nomogram for predicting the possibility of skip metastasis in order to determine the therapeutic strategy. We retrospectively reviewed the data of 1037 PTC patients who underwent surgery from 2016 to 2020 at a single institution. Univariate and multivariate analyses were used to identify the clinicopathologic and preoperative sonographic risk factors of skip metastasis. A nomogram including the risk factors for predicting skip metastasis was further developed and validated. The incidence of skip metastasis was 10.7%. The univariate and multivariate analyses suggested that gender (p= 0.001), tumor location (p= 0.000), extrathyroidal extension (p= 0.000), and calcification (p= 0.000) were independent risk factors. For papillary thyroid microcarcinoma, tumor location (p= 0.000) and calcification (p= 0.001) were independent risk factors. A nomogram according to the clinicopathologic and sonographic predictors was developed. The receiver operating characteristic curve indicated that AUC was 0.824 and had an excellent consistency. The calibration plot analysis showed a good performance and clinical utility of the model. Decision curve analysis revealed it was clinically useful. A nomogram for predicting the probability of skip metastasis was developed, which exhibited a favorable predictive value and consistency. For the female PTC patient, tumor located at the upper pole is more likely to have skip metastasis. Surgeons and sonographers should pay close attention to the patients who have the risk factors. Evidence level: This article's evidence level is 3. Level 3 evidence is derived from non-randomized, controlled clinical trials. In this study, patients who receive an intervention are compared to a control group. Authors may detect a statistically significant and clinically relevant outcome.

A combination of computed tomography scan and ultrasound provides optimal detection of cervical lymph node metastasis in papillary thyroid carcinomas: A systematic review and meta-analysis

BACKGROUND

Lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) is common. This meta-analysis assesses the diagnostic accuracy of computed tomography (CT), ultrasound (US), and CT + US in detecting central and lateral LNM.

METHODS

A systematic review and meta-analysis was performed by searching PubMed, Embase, and Cochrane for studies published up to April 2022. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated. The area under the curve (AUC) for summary receiver operating curves (sROC) were compared.

RESULTS

The study population included 7902 patients with a total of 15 014 lymph nodes. Twenty-four studies analyzed the sensitivity of the overall neck region in which dual CT + US imaging (55.9%) had greater sensitivities (p < 0.001) than either US (48.4%) or CT (50.4%) alone. The specificity of US alone (89.0%) was greater (p < 0.001) than CT alone (88.5%) or dual imaging (86.8%). The DOR for dual CT + US imaging was greatest (p < 0.001) at 11.134, while the AUCs of the three imaging modalities were similar (p > 0.05). Twenty-one studies analyzed the sensitivity of the central neck region in which both CT (45.8%) and CT + US imaging (43.4%) had greater sensitivities (p < 0.001) than US alone (35.3%). The specificity of all three modalities was higher than 85%. The DOR for CT (7.985) was greater than US alone (4.723, p < 0.001) or dual CT + US imaging (4.907, p = 0.015). The AUC of both CT + US (0.785) and CT alone (0.785) were significantly greater (p < 0.001) than US alone (0.685). Of the 19 studies that reported lateral LNM, CT + US imaging sensitivity (84.5%) was higher than CT alone (69.2%, p < 0.001) and US alone (79.7%, p = 0.038). The specificity of all imaging techniques was all greater than 80.0%. CT + US imaging DOR (35.573) was greater than CT (20.959, p = 0.024) and US (15.181, p < 0.001) individually. The AUC of independent imaging was high (CT: 0.863, US: 0.858) and improved significantly when combined (CT + US: 0.919, p = 0.024 and p < 0.001, respectively).

CONCLUSIONS

We report an up-to-date analysis elucidating the diagnostic accuracy of LNM detection by either CT, US, or in combination. Our work suggests dual CT + US to be the best for overall detection of LNM and CT to be preferable in detecting central LNM. The use of either CT or US alone may detect lateral LNM with acceptable accuracy, yet dual imaging (CT + US) significantly improved detection rates.

Validation of Ultrasound and Computed Tomography-Based Risk Stratification System and Biopsy Criteria for Cervical Lymph Nodes in Preoperative Patients With Thyroid Cancer

OBJECTIVE

This study aimed to validate the risk stratification system (RSS) and biopsy criteria for cervical lymph nodes (LNs) proposed by the Korean Society of Thyroid Radiology (KSThR).

MATERIALS AND METHODS

This retrospective study included a consecutive series of preoperative patients with thyroid cancer who underwent LN biopsy, ultrasound (US), and computed tomography (CT) between December 2006 and June 2015. LNs were categorized as probably benign, indeterminate, or suspicious according to the current US- and CT-based RSS and the size thresholds for cervical LN biopsy as suggested by the KSThR. The diagnostic performance and unnecessary biopsy rates were calculated.

RESULTS

A total of 277 LNs (53.1% metastatic) in 228 patients (mean age ± standard deviation, 47.4 years ± 14) were analyzed. In US, the malignancy risks were significantly different among the three categories (all P < 0.001); however, CT-detected probably benign and indeterminate LNs showed similarly low malignancy risks (P = 0.468). The combined US + CT criteria stratified the malignancy risks among the three categories (all P < 0.001) and reduced the proportion of indeterminate LNs (from 20.6% to 14.4%) and the malignancy risk in the indeterminate LNs (from 31.6% to 12.5%) compared with US alone. In all image-based classifications, nodal size did not affect the malignancy risks (short diameter [SD] ≤ 5 mm LNs vs. SD > 5 mm LNs, P ≥ 0.177). The criteria covering only suspicious LNs showed higher specificity and lower unnecessary biopsy rates than the current criteria, while maintaining sensitivity in all imaging modalities.

CONCLUSION

Integrative evaluation of US and CT helps in reducing the proportion of indeterminate LNs and the malignancy risk among them. Nodal size did not affect the malignancy risk of LNs, and the addition of indeterminate LNs to biopsy candidates did not have an advantage in detecting LN metastases in all imaging modalities.

Development of a Clinical-Radiomics Nomogram That Used Contrast-Enhanced Ultrasound Images to Anticipate the Occurrence of Preoperative Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Patients

Background and Objectives

Papillary thyroid carcinoma (PTC) is a prevalent histological type of thyroid cancer; however, noninvasive assessment of cervical lymph node metastasis (LNM) poses a challenge. This study aims to develop a novel clinical-radiomics nomogram that utilizes ultrasound (US) images to predict the presence of cervical LNM metastasis in patients with PTC.

Methods

A total of 423 patients with PTC were recruited to participate in this study between January 2020 and December 2022, of which 282 were classified into the training group and 141 patients were classified into the validation set. Contrast-enhanced ultrasound (CEUS) and B-mode ultrasound (BMUS) images were subjected to radiomic analysis, leading to the extraction of 912 radiomic features. Thereafter, a radiomics score (Radscore) was developed to effectively integrate the information derived from BMUS and CEUS modalities. Univariate and multivariate backward stepwise logistic regression analysis techniques were used to construct the clinical and clinical-radiomics models, respectively.

Results

The findings revealed that the clinical-radiomics nomogram incorporated age, sex, CEUS Radscore, and US-reported LNM as risk factors. The nomogram demonstrated good performance using data from the training (AUC = 0.891) and validation (AUC = 0.870) sets. The decision curve analysis implied that this nomogram exhibited good clinical utility, which was further supported by the results of the calibration curves and Hosmer-Lemeshow test.

Conclusion

The CEUS Radscore-based clinical radiomics nomogram could serve as a valuable tool for predicting cervical LNM metastasis in patients with PTC, thereby tailoring individualized treatment strategies for them.

Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis

OBJECTIVES

To build a combined model based on the ultrasound radiomic and morphological features, and evaluate its diagnostic performance for preoperative prediction of central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC).

METHOD

A total of 295 eligible patients, who underwent preoperative ultrasound scan and were pathologically diagnosed with unifocal PTC were included at our hospital from October 2019 to July 2022. According to ultrasound scanners, patients were divided into the training set (115 with CLNM; 97 without CLNM) and validation set (45 with CLNM; 38 without CLNM). Ultrasound radiomic, morphological, and combined models were constructed using multivariate logistic regression. The diagnostic performance was assessed by the area under the curve (AUC) of the receiver operating characteristic curve, accuracy, sensitivity, and specificity.

RESULTS

A combined model was built based on the morphology, boundary, length diameter, and radiomic score. The AUC was 0.960 (95% CI, 0.924-0.982) and 0.966 (95% CI, 0.901-0.993) in the training and validation set, respectively. Calibration curves showed good consistency between prediction and observation, and DCA demonstrated the clinical benefit of the combined model.

CONCLUSION

Based on ultrasound radiomic and morphological features, the combined model showed a good performance in predicting CLNM of patients with PTC preoperatively.

A nomogram for predicting lateral lymph node metastasis in cN0 unifocal papillary thyroid microcarcinoma

BACKGROUND

Identifying risk factors for occult lateral lymph node metastasis (LLNM) in papillary thyroid microcarcinoma (PTMC) can provide valuable insights into the necessity of lateral neck dissection (LND). The objective of this study was to develop a nomogram for predicting the probability of LLNM in patients with cN0 unifocal PTMC.

METHODS

We conducted a retrospective analyzed a total of 4872 patients with cN0 unifocal PTMC who were treated at our center from January 2013 to June 2018. Logistic regression analysis was used to determine the risk factors for LLNM, and a nomogram was constructed based on these risk factors.

RESULTS

The rate of LLNM was 3.2%. Tumors located in the upper lobe(odds ratio [OR] = 2.56, 95% confidence interval [CI] 1.80-3.62; p < 0.001) and size greater than 7 mm (OR = 2.59, 95% CI 1.85-3.62; p < 0.001) had a significantly higher risk of LLNM compared to tumors in the lower or middle lobe and size less than or equal to 7 mm. Tumors with extrathyroidal extension (ETE) had a significantly higher risk of LLNM (OR = 1.41, 95% CI 1.01-1.99; p = 0.044). The presence of three or more central lymph node metastases (CLNMs) (OR = 5.84, 95% CI 3.83-8.93; p < 0.001) or one or two CLNMs (OR = 2.91, 95% CI 1.93-4.42; p < 0.001) also increased the risk of LLNM compared to having no CLNMs. A nomogram incorporating these risk factors was developed, and the receiver operating characteristic (ROC) curve demonstrated an area under the curve (AUC) of 0.777, indicating a high degree of predictive accuracy.

CONCLUSION

Tumor location in the upper lobe, greater than 7 mm in size, ETE, and CLNMs, especially three or more, were independent risk factors for LLNM in cN0 unifocal PTMC. The nomogram based on these factors exhibited favorable predictive value and consistency.

Predictive nomogram for central lymph node metastasis in papillary thyroid microcarcinoma based on pathological and ultrasound features

Background

Central lymph node metastases (CLNM) in papillary thyroid microcarcinoma (PTMC) are common, but management through prophylactic central lymph node dissection (pCLND) remains controversial. In this study, the independent predictors of CLNM in PTMC were retrospectively studied based on ultrasound and pathological data, and we aim to establish the prediction model to predict CLNM in PTMC.

Methods

This study included a total of 1,506 patients who underwent thyroid surgery for PTMC at the First Affiliated Hospital of Chongqing Medical University from 2015 to 2018. Ultrasound and clinicopathological features were summarized and analyzed. Univariate and multivariate analyses were performed to determine the risk factors associated with CLNM. The prediction model is established and verified according to the multivariate analysis results. The Kaplan-Meier curve was used to evaluate the effect of CLNM on survival.

Results

The CLNM rate was 44.5% (670/1,506). Multivariate analysis showed that men, younger age, smaller diameter, ETE, microcalcification, without Hashimoto's thyroiditis, and multifocal were independent risk predictors of CLNM. Nomogram has a good discriminative ability (C-index: 0.755 in the validation group), and the calibration effect is good. In the DCA curve, the CLNM prediction model performed better net benefit given any high-risk thresholds. The median follow-up time was 30 months (12-59 months), 116 cases were lost, and the follow-up rate was 92.8% (1,506/1,622). Of the 1,506 patients included, 12 (0.8%) experienced recurrence.

Conclusion

The likelihood of CLNM can be objectively quantified before surgery by using this reliable and accurate nomogram that combines preoperative ultrasound with clinicopathological features. Clinicians can use this nomogram to assess central lymph node status in patients with PTMC and consider prophylactic CND in patients with high scores.

Accuracy of papillary thyroid cancer prognostic nomograms: a systematic review

Objective

Current staging and risk-stratification systems for predicting survival or recurrence of patients with differentiated thyroid carcinoma may be ineffective at predicting outcomes in individual patients. In recent years, nomograms have been proposed as an alternative to conventional systems for predicting personalized clinical outcomes. We conducted a systematic review to evaluate the predictive performance of available nomograms for thyroid cancer patients.

Design and methods

PROSPERO registration (CRD42022327028). A systematic search was conducted without time and language restrictions. PICOT questions: population, patients with papillary thyroid cancer; comparator prognostic factor, single-arm studies; outcomes, overall survival, disease-free survival, cancer-specific survival, recurrence, central lymph node metastases, or lateral lymph node metastases; timing, all periods; setting, hospital setting. Risk of bias was assessed through PROBAST tool.

Results

Eighteen studies with a total of 20 prognostic models were included in the systematic review (90,969 papillary thyroid carcinoma patients). Fourteen models were at high risk of bias and four were at unclear risk of bias. The greatest concerns arose in the analysis domain. The accuracy of nomograms for overall survival was assessed in only one study and appeared limited (0.77, 95% CI: 0.75-0.79). The accuracy of nomograms for disease-free survival ranged from 0.65 (95% CI: 0.55-0.75) to 0.92 (95% CI: 0.91-0.95). The C-index for predicting lateral lymph node metastasis ranged from 0.72 to 0.92 (95% CI: 0.86-0.97). For central lymph node metastasis, the C-index of externally validated studies ranged from 0.706 (95% CI: 0.685-0.727) to 0.923 (95% CI: 0.893-0.946).

Conclusions

Our work highlights the extremely high heterogeneity among nomograms and the critical lack of external validation studies that limit the applicability of nomograms in clinical practice. Further studies ideally using commonly adopted risk factors as the backbone to develop nomograms are required.

Significance statement

Nomograms may be appropriate tools to plan treatments and predict personalized clinical outcomes in patients with papillary thyroid cancer. However, the nomograms developed to date are very heterogeneous, and their results seem to be closely related to the specific samples studied to generate the same nomograms. The lack of rigorous external validation procedures and the use of risk factors that sometimes appear to be far from those commonly used in clinical practice, as well as the great heterogeneity of the risk factors considered, limit the ability of nomograms to predict patient outcomes and thus their current introduction in clinical practice.

Nomograms for Prediction of High-Volume Lymph Node Metastasis in Papillary Thyroid Carcinoma Patients

OBJECTIVE

The coexistence rate between chronic lymphocytic thyroiditis (CLT) and papillary thyroid carcinoma (PTC) is quite high. Whether CLT influences metastatic lymph nodes remains uncertain. High-volume lymph node metastasis is recommended as an unfavorable pathological feature. We aimed to investigate risk factors for high-volume central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM) in PTC patients.

STUDY DESIGN

Retrospective cohort study.

SETTING

Changzhou First People's Hospital.

METHODS

Clinicopathological characteristics of 1094 PTC patients who underwent surgery in our center from January 2019 to November 2021 were analyzed.

RESULTS

The number of metastatic lymph nodes in the central compartment and lateral compartment were lower in the CLT group. We demonstrated that age, BRAF V600E, shape, and the number of foci were risk factors for high-volume CLNM in patients with CLT. For patients without CLT, sex, age, tumor size, number of foci, and margin were risk factors for high-volume CLNM. Tumor size, number of foci, location, and CLNM were all risk factors for high-volume LLNM in patients with or without CLT. Body mass index was only associated with high-volume LLNM in CLT patients. All the above factors were incorporated into nomograms, which showed perfect discriminative ability.

CONCLUSION

Separate predictive systems should be used for CLT and non-CLT patients for a more accurate clinical assessment of lymph node status. Our nomograms of predicting high-volume CLNM and LLNM could facilitate risk-stratified management of PTC recurrence and treatment decisions.

Benign Intranodal Thyroid Tissue Similar to Nodal Metastasis of Thyroid Papillary Carcinoma: A Rare Case Report

In patients with thyroid nodules, if the cervical lymph nodes gradually enlarge, a histological confirmation is required to rule out malignancy. Here is a case of benign intranodal thyroid tissue with cystic changes resembling lymph node metastasis of a papillary thyroid carcinoma. A 47-year-old man received ethanol sclerotherapy because of repeated enlargement of the thyroid gland 2 years prior to presentation. Subsequently, the patient underwent abscess removal from the deep neck and partial lobectomy of the attached left thyroid gland. Two months before the visit, extensive cervical lymphadenopathy was detected on ultrasonography (US) and computed tomography (CT). Total thyroidectomy and cervical lymph node dissection were performed to differentiate between metastatic papillary carcinoma of the thyroid gland and benign thyroid inclusions. Microscopic examination revealed multiple variable-sized nodules of benign thyroid follicles with cystic changes in both thyroid glands and bilateral cervical lymph nodes. An occult papillary microcarcinoma strongly positive for HBME-1 was also observed in the left thyroid lobe. However, the benign intranodal thyroid tissue was negative in both the real-time PCR-based BRAF V600E mutation test and HBME-1 immunohistochemical stain. Similarly, benign intranodal thyroid tissue can be enlarged by multiple cystic changes in a large number of lymph nodes along the neck node chain. For the differentiation of metastatic thyroid papillary carcinoma, real-time PCR-based BRAF V600E mutation test and HBME-1 immunohistochemical staining in addition to histological examination are helpful.

Dual-modal radiomics for predicting cervical lymph node metastasis in papillary thyroid carcinoma

BACKGROUND

Preoperative prediction of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) is significant for surgical decision-making.

OBJECTIVE

This study aims to develop a dual-modal radiomics (DMR) model based on grayscale ultrasound (GSUS) and dual-energy computed tomography (DECT) for non-invasive CLNM in PTC.

METHODS

In this study, 348 patients with pathologically confirmed PTC at Jiangsu University Affiliated People's Hospital who completed preoperative ultrasound (US) and DECT examinations were enrolled and randomly assigned to training (n = 261) and test (n = 87) cohorts. The enrolled patients were divided into two groups based on pathology findings namely, CLNM (n = 179) and CLNM-Free (n = 169). Radiomics features were extracted from GSUS images (464 features) and DECT images (960 features), respectively. Pearson correlation coefficient (PCC) and the least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation were then used to select CLNM-related features. Based on the selected features, GSUS, DECT, and GSUS combined DECT radiomics models were constructed by using a Support Vector Machine (SVM) classifier.

RESULTS

Three predictive models based on GSUS, DECT, and a combination of GSUS and DECT, yielded performance of areas under the curve (AUC) = 0.700 [95% confidence interval (CI), 0.662-0.706], 0.721 [95% CI, 0.683-0.727], and 0.760 [95% CI, 0.728-0.762] in the training dataset, and AUC = 0.643 [95% CI, 0.582-0.734], 0.680 [95% CI, 0.623-0.772], and 0.744 [95% CI, 0.686-0.784] in the test dataset, respectively. It shows that the predictive model combined GSUS and DECT outperforms both models using GSUS and DECT only.

CONCLUSIONS

The newly developed combined radiomics model could more accurately predict CLNM in PTC patients and aid in better surgical planning.

Nomogram based on preoperative conventional ultrasound and shear wave velocity for predicting central lymph node metastasis in papillary thyroid carcinoma

OBJECTIVE

To establish a nomogram for predicting cervical lymph node metastasis (CLNM) based on the preoperative conventional ultrasound (US) and shear wave velocity (SWV) features of papillary thyroid carcinoma (PTC).

METHODS

A total of 101 patients with pathologically confirmed thyroid nodules were enrolled. These patients were divided into the CLNM-positive (n = 40) and CLNM-negative groups (n = 61). All patients underwent the preoperative conventional US and shear wave elastography (SWE) evaluation, and the US parameters and SWV data were collected. The association between SWV ratio and CLNM was compared to assess the diagnostic efficacy of SWV ratio alone as opposed to SWV ratio in combination with the conventional US for predicting CLNM.

RESULTS

There were significant differences in shape, microcalcification, capsule contact, SWV mean, and SWV ratio between the CLNM-positive and CLNM-negative groups (P < 0.05). Logistic regression analysis showed that taller-than-wide shape, microcalcification, capsule contact, and SWV ratio > 1.3 were risk factors for CLNM; Logistic(P)=-6.93 + 1.647 * (microcalcification)+1.138 * (taller-than-wide-shape)+1.612 * (capsule contact)+2.933 * (SWV ratio > 1.3). The area under the curve (AUC) of the receiver operating characteristic (ROC) of the model for CLNM prediction was 0.87, with 81.19% accuracy, 77.5% sensitivity, and 85.25% specificity.

CONCLUSION

The nomogram based on conventional US imaging in combination with SWV ratio has the potential for preoperative CLNM risk assessment. This nomogram serves as a useful clinical tool for active surveillance and treatment decisions.

An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study

OBJECTIVE

Central lymph node metastasis (CLNM) is a predictor of poor prognosis for papillary thyroid carcinoma (PTC) patients. The options for surgeon operation or follow-up depend on the state of CLNM while accurate prediction is a challenge for radiologists. The present study aimed to develop and validate an effective preoperative nomogram combining deep learning, clinical characteristics and ultrasound features for predicting CLNM.

MATERIALS AND METHODS

In this study, 3359 PTC patients who had undergone total thyroidectomy or thyroid lobectomy from two medical centers were enrolled. The patients were divided into three datasets for training, internal validation and external validation. We constructed an integrated nomogram combining deep learning, clinical characteristics and ultrasound features using multivariable logistic regression to predict CLNM in PTC patients.

RESULTS

Multivariate analysis indicated that the AI model-predicted value, multiple, position, microcalcification, abutment/perimeter ratio and US-reported LN status were independent risk factors predicting CLNM. The area under the curve (AUC) for the nomogram to predict CLNM was 0.812 (95% CI, 0.794-0.830) in the training cohort, 0.809 (95% CI, 0.780-0.837) in the internal validation cohort and 0.829(95%CI, 0.785-0.872) in the external validation cohort. Based on the analysis of the decision curve, our integrated nomogram was superior to other models in terms of clinical predictive ability.

CONCLUSION

Our proposed thyroid cancer lymph node metastasis nomogram shows favorable predictive value to assist surgeons in making appropriate surgical decisions in PTC treatment.

Diagnostic value of CT enhancement degree in lymph node metastasis of papillary thyroid cancer: A comparison of enhancement, ratio, and difference

OBJECTIVES

To evaluate the value of computed tomography (CT) enhancement degree in diagnosing lymph node (LN) metastasis in papillary thyroid carcinoma (PTC) by determining the ratio and difference between the Hounsfield units (HU) of CT enhancement and plain scan of the LNs, as well as between the HU of CT-enhanced LNs and the sternocleidomastoid muscle.

METHODS

The plain and enhanced CT findings of 114 metastasis-positive LNs in 89 cases and 143 metastasis-negative LNs in 114 cases of PTC were analyzed retrospectively. Plain HU of LNs (PN_HU), enhanced HU of LNs (EN_HU), and enhanced HU of the sternocleidomastoid muscle (EM_HU) were measured. The EN_HU, difference between EN_HU and PN_HU (EN-PN_HU), ratio of EN_HU to PN_HU (EN/PN_HU), difference between EN_HU and EM_HU (EN-EM_HU), and ratio of EN_HU to EM_HU (EN/EM_HU) in metastasis-positive and metastasis-negative LN groups were calculated, the corresponding diagnostic efficacy for differentiating metastasis-positive from metastasis-negative LNs in PTC were sought using the receiver-operating curve. The interobserver agreement between readers was assessed using the interobserver correlation coefficient (ICC).

RESULTS

The EN_HU of 114 metastasis-positive LNs and 143 metastasis-negative LNs was 113.39 ± 24.13 and 77.65 ± 15.93, EN-PN_HU was 65.84 ± 21.72 HU and 34.07 ± 13.63 HU, EN/PN_HU was 2.36 (1.98, 2.75) and 1.76 (1.54, 2.02), EN-EM_HU was 49.42 ± 24.59 HU and 13.27 ± 15.41 HU, and EN/EM_HU was 1.79 ± 0.40 and 1.21 ± 0.24, respectively (all P < 0.001). The area under the curve, cutoff value, sensitivity, specificity, and accuracy of EN_HU for identifying metastasis-positive and metastasis-negative LNs were 0.895, 97.3 HU, 0.746, 0.895, and 0.829, EN-PN_HU was 0.894, 47.8 HU, 0.807, 0.874, and 0.844, EN/PN_HU was 0.831, 1.9, 0.877, 0.650, and 0.751, EN-EM_HU was 0.890, 26.4 HU, 0.807, 0.839, and 0.825, and EN/EM_HU was 0.888, 1.5, 0.728, 0.902, and 0.825, respectively. The readers had an excellent interobserver agreement on these five parameters (ICC = 0.874-0.994).

CONCLUSION

In the preoperative evaluation of LN metastasis in PTC, EN_HU, EN-PN_HU, EN-EM_HU, and EN/EM_HU had similarly high diagnostic efficacy, with EN_HU, EN-PN_HU, and EN/EM_HU having higher specificity and EN-PN_HU and EN-EM_HU having higher sensitivity.

Risk factors and prediction model of level II lymph node metastasis in papillary thyroid carcinoma

Introduction

Surgical management of lateral lymph nodes in papillary thyroid carcinoma, especially at level II, remains controversial. This study aimed to investigate the risk factors for level II lymph node metastasis in patients with papillary thyroid carcinoma and establish a prediction model to estimate the metastatic risk.

Materials and methods

A total of 768 patients with papillary thyroid carcinoma underwent thyroidectomy and central plus lateral lymph node dissection, including levels VI, II, III, and IV, at the First Affiliated Hospital of Chongqing Medical University from January 2016 to December 2018. Data on the clinicopathological characteristics were collected and analyzed. Univariate and multivariate analyses were performed to identify risk factors for level II lymph node metastasis. Subsequently, a predictive model was established based on the results of the multivariate analyses.

Results

The level II lymph node metastatic rate was 34.11% with the following features: largest tumor diameter >20 mm (Odds ratio=1.629, P=0.026), located in the upper pole (Odds ratio=4.970, P<0.001), clinical lymph node-positive (clinical central lymph node-positive: Odds ratio=1.797; clinical lateral lymph node-positive: Odds ratio=1.805, P=0.008), vascular invasion (Odds ratio=6.759, P=0.012), and rate of central lymph node metastasis (Odds ratio=2.498, P<0.001). Level III lymph node metastasis (Odds ratio=2.749, P<0.001) and level IV lymph node metastasis (Odds ratio=1.732, P=0.007) were independent of level II lymph node metastasis predictors. The prediction model's areas under the receiver operating characteristic curve were 0.815 and 0.804, based on bootstrapping validation. Level II lymph node metastasis was associated with the tumor-free survival rate of patients with papillary thyroid carcinoma (P<0.001).

Conclusions

Largest tumor diameter >20 mm, located in the upper pole, clinical lymph node-positive, vascular invasion, rate of central lymph node metastasis, and levels III and IV lymph node metastases were independent level II lymph node metastasis predictors. We developed a prediction model for level II lymph node metastasis. Overall, level II lymph node metastasis dissection should be individualized according to clinicopathological data both preoperatively and intraoperatively.

Surgeon-performed ultrasound changes surgical management in patients with thyroid cancer

BACKGROUND

A comprehensive neck ultrasound (US) is essential in the operative planning of patients with thyroid disease. Recent literature has shown surgeon-performed US (SUS) can be more accurate than radiology-performed US for the purpose of surgical planning. Missed findings on radiology-performed ultrasound may lead to inadequate surgical management.

METHODS

A retrospective cohort study of patients undergoing total thyroidectomy with lateral neck dissection for thyroid cancer, with both radiology-performed US and SUS performed by a Head and Neck surgeon. Ultrasound findings and adherence to American Thyroid Association (ATA) guidelines were compared, and changes in management based on SUS findings were identified.

RESULTS

A total of 26 patients who underwent total thyroidectomy with lateral neck dissection met the inclusion criteria. Preconsultation US investigations fulfilled criteria as recommended by the American Thyroid Association (ATA) guidelines in 57.7%% of cases. The central and lateral neck compartments were assessed in 57.7% and 84.6% of preconsultation US investigations respectively. Central and lateral metastatic neck metastases were incorrectly reported or not reported in 78.6% and 42.3% of cases. The SUS findings prompted a change in surgical management in 65.4% of cases.

CONCLUSION

SUS changed surgical management in two thirds (65.4%) of patients. Reliance on radiology-performed ultrasound alone may result in incorrect staging. Awareness of the additional benefits of SUS is important for surgeons treating patients with thyroid disease to prevent inadequate surgery being performed.

Radiologist versus Non-Radiologist Detection of Lymph Node Metastasis in Papillary Thyroid Carcinoma by Ultrasound: A Meta-Analysis

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer worldwide and is known to spread to adjacent neck lymphatics. Lymph node metastasis (LNM) is a known predictor of disease recurrence and is an indicator for aggressive resection. Our study aims to determine if ultrasound sonographers’ degree of training influences overall LNM detection. PubMed, Embase, and Scopus articles were searched and screened for relevant articles. Two investigators independently screened and extracted the data. Diagnostic test parameters were determined for all studies, studies reported by radiologists, and studies reported by non-radiologists. The total sample size amounted to 5768 patients and 10,030 lymph nodes. Radiologists performed ultrasounds in 18 studies, while non-radiologists performed ultrasounds in seven studies, corresponding to 4442 and 1326 patients, respectively. The overall sensitivity of LNM detection by US was 59% (95%CI = 58–60%), and the overall specificity was 85% (95%CI = 84–86%). The sensitivity and specificity of US performed by radiologists were 58% and 86%, respectively. The sensitivity and specificity of US performed by non-radiologists were 62% and 78%, respectively. Summary receiver operating curve (sROC) found radiologists and non-radiologists to detect LNM on US with similar accuracy (p = 0.517). Our work suggests that both radiologists and non-radiologists alike detect overall LNM with high accuracy on US.

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

RATIONALE AND OBJECTIVES

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.

MATERIALS AND METHODS

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 .

RESULTS

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.

CONCLUSION

This radiomic signature could be helpful to predict CLNM status in cN0 PTC patients.

A Combination of Contrast-Enhanced Ultrasound and Thyroglobulin Level in Fine-Needle Aspirates Improves Diagnostic Accuracy for Metastatic Lymph Nodes of Papillary Thyroid Carcinoma

OBJECTIVE

To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with thyroglobulin (Tg) levels in fine-needle aspirates (FNA) washout fluid (FNA-Tg) in diagnosing cervical lymph node (LN) metastasis in papillary thyroid cancer (PTC) patients.

METHODS

Data from 190 LNs in 167 patients suspected of metastasis from the US between November 2018 and September 2020 were included. All subjects underwent FNA, CEUS, and FNA-Tg examinations. The final outcomes were confirmed by histopathological or cytological examination or follow-up imaging. Data were analyzed using the Wilcoxon rank-sum or chi-squared test. The diagnostic efficacy of FNA, CEUS, and FNA-Tg in diagnosing LNs was compared.

RESULTS

A cutoff value of 6.15 ng/ml (AUC 0.925, 95% confidence interval (CI) 0.885-0.966) successfully identified metastatic LNs. FNA missed 58 LN metastases, of these, 94.8% (55/58) were correctly diagnosed using the combination of CEUS and FNA-Tg. FNA-Tg showed higher sensitivity (90.2%), NPV (86.1%) and accuracy (88.9%) than either FNA (48.2, 57.4 and 69.5%, respectively) or CEUS (82.1, 67.7 and 70.5%, respectively) alone. The combination of CEUS, FNA and FNA-Tg resulted in maximal sensitivity (100%) and NPV (100%) but reduced specificity (51.3%) and overall diagnostic accuracy (80.0%). After adding FNA-Tg to discordant samples between CEUS and FNA, 81.9% of LNs (77/94) were correctly diagnosed.

CONCLUSIONS

The combination of FNA, FNA-Tg and CEUS was found to be a promising imaging tool in detecting metastatic LNs in PTC patients.

Risk factors for level V metastasis in patients with N1b papillary thyroid cancer

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer, and its incidence has increased. Lateral lymph node metastasis (LLNM) implies a worse prognosis than central lymph node metastasis, with a higher recurrence rate and decreased disease-free survival. The 2015 American Thyroid Association guidelines recommend compartmental node dissection in patients with LLNM to reduce the risk of recurrence and mortality. The purpose of this study was to identify the risk factors for level V lymph node (LN) metastasis in patients with N1b papillary thyroid cancer (PTC).

METHODS

A total of 110 consecutive patients who underwent total thyroidectomy with lateral neck dissection for PTC between April 2016 and April 2022 were retrospectively enrolled. Based on level V metastasis, 94 patients were divided into two groups, and their clinicopathological characteristics were compared. Univariable analysis were used to assess the factors associated with level V metastasis. Spearman correlation analysis were used to assess the correlation between tumors and LN. The receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value for the number of metastatic LNs at each level for level V metastasis.

RESULTS

The number of metastatic LNs and lymph node ratio (LNR) in level II were significantly associated with level V metastasis (P = 0.011 and 0.001, respectively). The number of metastatic LNs in level II and those in the total number of levels correlated with the number of metastatic LNs in level V (rho = 0.331, 0.325, and P = 0.001, 0.001, respectively). The cutoff value for the number of metastatic LNs in level II was defined as 2.5 (area under the curve = 0.757, sensitivity = 50%, specificity = 82.5%, 95% confidence interval [CI] 0.626-0.889, P = 0.002). Simultaneous 3-level metastasis (level II, III, and IV) and 3-level with ≥ 2.5 metastatic LNs in level II were significantly associated with level V metastasis (P = 0.003 and 0.002).

CONCLUSIONS

The number of metastatic LNs and LNR in level II, simultaneous 3-level metastasis (level II, III, and IV), and 3-level with ≥ 2.5 metastatic LNs in level II were significantly associated with level V metastasis. (P = 0.011, 0.001, 0.003, and 0.002, respectively). In the future, larger-scale multi-institutional studies were needed to find out predictors for level V metastasis.

Diagnostic accuracy of ultrasound, CT and their combination in detecting cervical lymph node metastasis in patients with papillary thyroid cancer: a systematic review and meta-analysis

OBJECTIVES

To determine the diagnostic accuracy of ultrasound (US), CT and their combination in detecting cervical lymph node metastasis (CLNM) in patients with papillary thyroid cancer (PTC).

METHODS

Medline (via PubMed), Web of Science, Embase were searched to identify studies published till 5 December 2021 that used US and CT to detect CLNM in patients with PTC. The primary outcomes were sensitivity, specificity and diagnostic ORs in neck-level-based (lymph nodes are analysed by neck level) or patient-based (lymph nodes are analysed by patient) analysis. Secondary outcomes were sensitivity, specificity and DORs in the central and lateral compartments.

RESULTS

Fourteen studies (6167 patients with 11 601 neck lymph nodes) met the inclusion criteria. Based on the neck-level-based analysis, the pooled sensitivity, specificity and DORs were 0.35 (95% CI 0.34 to 0.37), 0.95 (95% CI 0.94 to 0.95) and 13.94 (95% CI 9.34 to 20.82) for US, were 0.46 (95% CI 0.44 to 0.47), 0.88 (95% CI 0.87 to 0.89) and 7.24 (95% CI 5.46 to 9.62) for CT, were 0.51 (95% CI 0.49 to 0.52), 0.85 (95% CI 0.84 to 0.86), 6.01 (95% CI 3.84 to 9.40) for the combination of US and CT. In the patient-based analysis, the pooled estimates of sensitivity, specificity and DOR were 0.41 (95% CI 0.36 to 0.46), 0.92 (95% CI 0.89 to 0.94) and 7.56 (95% CI 4.08 to 14.01) for US, were 0.49 (0.44 to 0.54), 0.91 (0.89 to 0.94), 9.40 (5.79 to 15.27) for CT, and were 0.64 (95% CI 0.57 to 0.71), 0.83 (95% CI 0.77 to 0.88), 8.59 (95% CI 5.37 to 13.76) for the combination of US and CT.

DISCUSSION

These findings suggest US, with a DOR almost twice that of CT in the neck-level-based analysis, was superior to CT in detecting CLNM in patients with PTC, especially in the lateral compartment. The combination of US and CT increased the sensitivity from 41%-49% for the individual modalities to 64% for combined modalities in the patient-based analysis.

Artificial Neural Network-Based Ultrasound Radiomics Can Predict Large-Volume Lymph Node Metastasis in Clinical N0 Papillary Thyroid Carcinoma Patients

Objective

To evaluate the ability of artificial neural network- (ANN-) based ultrasound radiomics to predict large-volume lymph node metastasis (LNM) preoperatively in clinical N0 disease (cN0) papillary thyroid carcinoma (PTC) patients.

Methods

From January 2020 to April 2021, 306 cN0 PTC patients admitted to our hospital were retrospectively reviewed and divided into a training (n = 183) cohort and a validation cohort (n = 123) in a 6 : 4 ratio. Radiomic features quantitatively extracted from ultrasound images were pruned to train one ANN-based radiomic model and three conventional machine learning-based classifiers in the training cohort. Furthermore, an integrated model using ANN was constructed for better prediction. Meanwhile, the prediction of the two models was evaluated in the papillary thyroid microcarcinoma (PTMC) and conventional papillary thyroid cancer (CPTC) subgroups.

Results

The radiomic model showed better discrimination than other classifiers for large-volume LNM in the validation cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.856 and an area under the precision-recall curve (AUPR) of 0.381. The performance of the integrated model was better, with an AUROC of 0.910 and an AUPR of 0.463. According to the calibration curve and decision curve analysis, the radiomic and integrated models had good calibration and clinical usefulness. Moreover, the models had good predictive performance in the PTMC and CPTC subgroups.

Conclusion

ANN-based ultrasound radiomics could be a potential tool to predict large-volume LNM preoperatively in cN0 PTC patients.

A novel tool for predicting the risk of central lymph node metastasis in patients with papillary thyroid microcarcinoma: a retrospective cohort study

Introduction

Central lymph node status in papillary thyroid microcarcinoma (PTMC) plays an important role in treatment decision-making clinically, however, it is not easy to predict central lymph node metastasis (CLNM). The present work focused on finding the more rational alternative for evaluating central lymph node status while identifying influencing factors to construct a model to predict CLNM incidence.

Methods

In this study, we retrospectively analyzed the typical sonographic and clinicopathologic features of 546 PTMC patients who underwent surgery, among which, the data of 382 patients were recruited in the training cohort and that of 164 patients in the validation cohort. Based on the outcome of the training cohort, significant influencing factors were further identified through univariate analysis and were considered as independent variables in multivariable logistic regression analysis and incorporated in and presented with a nomogram.

Results

In total, six independent predictors, including the age, sex, tumor size, multifocality, capsular invasion, Hashimotos thyroiditis were entered into the nomogram. Both internal validation and external validation revealed the favorable discrimination of our as-constructed nomogram. Calibration curves exhibited high consistency. As suggested by decision-curve analyses, the as-constructed nomogram might be applied in clinic. Besides, the model also distinguished patients according to risk stratification.

Conclusions

The novel nomogram containing remarkable influencing factors for CLNM cases was established in the present work. The nomogram can assist clinicians in clinical decision-making.

Asporin Interacts With HER2 to Promote Thyroid Cancer Metastasis via the MAPK/EMT Signaling Pathway

Approximately 85% of histological subtypes of thyroid cancer are papillary thyroid cancer (PTC), and the morbidity and mortality of PTC patients rapidly increased due to lymph node metastases or distant metastasis. Therefore, it needs to distill an enhanced understanding of the pathogenesis of PTC patients with lymph node metastases or distant metastasis. We employed the TMT-based quantitative proteomics approach to identify and analyze differentially expressed proteins in PTC with different degrees of lymph node metastases. Compared with paired normal tissues, asporin is overexpressed in PTC-N0, PTC-N1a, and PTC-N1b tumorous tissues via proteomics, western blotting, and immunohistochemistry assays. Functionally, asporin is mainly expressed in the extracellular matrix, cell membrane, and cytoplasm of PTC tumorous tissues, and promotes thyroid cancer cell proliferation, migration, and invasion. Mechanistically, asporin, interacting with HER2, co-localizes HER2 on the cell membrane and cytoplasm, and the asporin/HER2/SRC/EGFR axis upregulate the expression of EMT-activating transcription factors through the MAPK signaling pathway. Clinically, asporin can be regarded as a serological biomarker to identify PTC patients with or without lymph node metastasis, and high expression of asporin in PTC tumorous tissues is a risk factor for poor prognosis.

Radiomics Profiling Identifies the Value of CT Features for the Preoperative Evaluation of Lymph Node Metastasis in Papillary Thyroid Carcinoma

Background: The aim of this study was to identify the increased value of integrating computed tomography (CT) radiomics analysis with the radiologists’ diagnosis and clinical factors to preoperatively diagnose cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients. Methods: A total of 178 PTC patients were randomly divided into a training (n = 125) and a test cohort (n = 53) with a 7:3 ratio. A total of 2553 radiomic features were extracted from noncontrast, arterial contrast-enhanced and venous contrast-enhanced CT images of each patient. Principal component analysis (PCA) and Pearson’s correlation coefficient (PCC) were used for feature selection. Logistic regression was employed to build clinical–radiological, radiomics and combined models. A nomogram was developed by combining the radiomics features, CT-reported lymph node status and clinical factors. Results: The radiomics model showed a predictive performance similar to that of the clinical–radiological model, with similar areas under the curve (AUC) and accuracy (ACC). The combined model showed an optimal predictive performance in both the training (AUC, 0.868; ACC, 86.83%) and test cohorts (AUC, 0.878; ACC, 83.02%). Decision curve analysis demonstrated that the combined model has good clinical application value. Conclusions: Embedding CT radiomics into the clinical diagnostic process improved the diagnostic accuracy. The developed nomogram provides a potential noninvasive tool for LNM evaluation in PTC patients.

Relationship between subgroups of central and lateral lymph node metastasis in clinically node-negative papillary thyroid carcinoma

BACKGROUND

Lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) has a certain regularity and occurs first to the central lymph node and then to the lateral lymph node. The pathway of PTC LNM can guide surgical prophylactic lymph node dissection (LND) for clinical surgeons.

AIM

To investigate the relationship between subgroups of central LNM and lateral LNM in unilateral clinically node-negative PTC (cN0-PTC).

METHODS

Data were collected for 1089 PTC patients who underwent surgical treatment at the Department of Endocrine and Breast Surgery of the First Hospital of Chongqing Medical University from January 2016 to December 2017. A total of 388 unilateral cN0-PTC patients met the inclusion criteria and were enrolled in this study. The clinical and pathological data for these 388 patients who underwent total thyroidectomy + central LND + lateral LND were retrospectively analyzed. The relationship between the central LNM and lateral LNM subgroups was investigated.

RESULTS

The coincidence rate of cN0-PTC was only 30.0%.Optimal scaling regression analysis showed that sex (57.1% vs 42.9%, P = 0.026), primary tumor size (68.8% vs 31.2%, P = 0.008), tumor location (59.7% vs 40.3%, P = 0.007), extrathyroid extension (ETE) (50.6% vs 49.9%, P = 0.046), and prelaryngeal LNM (57.1% vs 42.9%, P = 0.004) were significantly associated with ipsilateral level-II LNM. Their importance levels were 0.122, 0.213, 0.172, 0.110, and 0.227, respectively. Primary tumor size (74.6% vs 30.2%, P = 0.016), pretracheal LNM (67.5% vs 32.5%, P < 0.001), and paratracheal LNM (71.4% vs 28.6%, P < 0.001) were significantly associated with ipsilateral level-III LNM. Their importance levels were 0.120, 0.408, and 0.351, respectively. Primary tumor size (72.1% vs 27.9%, P = 0.003), ETE (70.4% vs 29.6%, P = 0.016), pretracheal LNM (68.3% vs 31.7%, P=0.001), and paratracheal LNM (80.8% vs 19.2%, P < 0.001) were significantly associated with ipsilateral level-IV LNM. Their importance levels were 0.164, 0.146, 0.216, and 0.472, respectively.

CONCLUSION

The LNM pathway of thyroid cancer has a certain regularity. For unilateral cN0-PTC patients with a tumor diameter > 2 cm and pretracheal or ipsilateral paratracheal LNM, LND at ipsilateral level III and level IV must be considered. When there is a tumor in the upper third of the thyroid with prelaryngeal LNM, LND at level II, level III and level IV must be considered.