Prediction of cervical lymph node metastasis in patients with papillary thyroid cancer using combined conventional ultrasound, strain elastography, and acoustic radiation force impulse (ARFI) elastography

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

Objective

This study aims to combine ultrasound (US) elastography (USE) and radiomic to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC).

Methods

A total of 204 patients with 204 thyroid nodules who were confirmed with PTMC and treated in our hospital were enrolled and randomly assigned to the training set (n = 142) and the validation set (n = 62). US features, USE (gender, shape, echogenic foci, thyroid imaging reporting and data system (TIRADS) category, and elasticity score), and radiomic signature were employed to build three models. A nomogram was plotted for the combined model, and decision curve analysis was applied for clinical use.

Results

The combined model (USE and radiomic) showed optimal diagnostic performance in both training (AUC = 0.868) and validation sets (AUC = 0.857), outperforming other models.

Conclusion

The combined model based on USE and radiomic showed a superior performance in the prediction of CLNM of patients with PTMC, covering the shortage of low specificity of conventional US in detecting CLNM.

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.

A nomogram based on multimodal ultrasound and clinical features for the prediction of central lymph node metastasis in unifocal papillary thyroid carcinoma

OBJECTIVES

To build a predictive model for central lymph node metastasis (CLNM) in unifocal papillary thyroid carcinoma (UPTC) using a combination of clinical features and multimodal ultrasound (MUS).

METHODS

This retrospective study, included 390 UPTC patients who underwent MUS between January 2017 and October 2022 and were divided into a training cohort (n = 300) and a validation cohort (n = 90) based on a cut-off date of June 2022. Independent indicators for constructing the predictive nomogram models were identified using multivariate regression analysis. The diagnostic yield of the 3 predictive models was also assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

Both clinical factors (age, diameter) and MUS findings (microcalcification, virtual touch imaging score, maximal value of virtual touch tissue imaging and quantification) were significantly associated with the presence of CLNM in the training cohort (all P < .05). A predictive model (MUS + Clin), incorporating both clinical and MUS characteristics, demonstrated favourable diagnostic accuracy in both the training cohort (AUC = 0.80) and the validation cohort (AUC = 0.77). The MUS + Clin model exhibited superior predictive performance in terms of AUCs over the other models (training cohort 0.80 vs 0.72, validation cohort 0.77 vs 0.65, P < .01). In the validation cohort, the MUS + Clin model exhibited higher sensitivity compared to the CLNM model for ultrasound diagnosis (81.2% vs 21.6%, P < .001), while maintaining comparable specificity to the Clin model alone (62.3% vs 47.2%, P = .06). The MUS + Clin model demonstrated good calibration and clinical utility across both cohorts.

CONCLUSION

Our nomogram combining non-invasive features, including MUS and clinical characteristics, could be a reliable preoperative tool to predict CLNM treatment of UPTC.

ADVANCES IN KNOWLEDGE

Our study established a nomogram based on MUS and clinical features for predicting CLNM in UPTC, facilitating informed preoperative clinical management and diagnosis.

Deep learning predicts cervical lymph node metastasis in clinically node-negative papillary thyroid carcinoma

OBJECTIVES

Precise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes.

METHODS

Preoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set. An ensemble deep learning model based on ResNet-50 was built integrating clinical variables, BMUS, and CDFI images using a bagging classifier to predict metastasis of CLN. The final ensemble model performance was compared with expert interpretation.

RESULTS

The ensemble deep convolutional neural network (DCNN) achieved high performance in predicting CLNM in the test sets examined, with area under the curve values of 0.86 (95% CI 0.78-0.94) for the internal test set and 0.77 (95% CI 0.68-0.87) for the external test set. Compared to all radiologists averaged, the ensemble DCNN model also exhibited improved performance in making predictions. For the external validation set, accuracy was 0.72 versus 0.59 (p = 0.074), sensitivity was 0.75 versus 0.58 (p = 0.039), and specificity was 0.69 versus 0.60 (p = 0.078).

CONCLUSIONS

Deep learning can non-invasive predict CLNM for clinically node-negative PTC using conventional US imaging of thyroid cancer nodules and clinical variables in a multi-institutional dataset with superior accuracy, sensitivity, and specificity comparable to experts.

CRITICAL RELEVANCE STATEMENT

Deep learning efficiently predicts CLNM for clinically node-negative PTC based on US images and clinical variables in an advantageous manner.

KEY POINTS

• A deep learning-based ensemble algorithm for predicting CLNM in PTC was developed. • Ultrasound AI analysis combined with clinical data has advantages in predicting CLNM. • Compared to all experts averaged, the DCNN model achieved higher test performance.

Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis

Objective

This retrospective study aimed to establish ultrasound radiomics models to predict central lymph node metastasis (CLNM) based on preoperative multimodal ultrasound imaging features fusion of primary papillary thyroid carcinoma (PTC).

Methods

In total, 498 cases of unifocal PTC were randomly divided into two sets which comprised 348 cases (training set) and 150 cases (validition set). In addition, the testing set contained 120 cases of PTC at different times. Post-operative histopathology was the gold standard for CLNM. The following steps were used to build models: the regions of interest were segmented in PTC ultrasound images, multimodal ultrasound image features were then extracted by the deep learning residual neural network with 50-layer network, followed by feature selection and fusion; subsequently, classification was performed using three classical classifiers-adaptive boosting (AB), linear discriminant analysis (LDA), and support vector machine (SVM). The performances of the unimodal models (Unimodal-AB, Unimodal-LDA, and Unimodal-SVM) and the multimodal models (Multimodal-AB, Multimodal-LDA, and Multimodal-SVM) were evaluated and compared.

Results

The Multimodal-SVM model achieved the best predictive performance than the other models (P < 0.05). For the Multimodal-SVM model validation and testing sets, the areas under the receiver operating characteristic curves (AUCs) were 0.910 (95% CI, 0.894-0.926) and 0.851 (95% CI, 0.833-0.869), respectively. The AUCs of the Multimodal-SVM model were 0.920 (95% CI, 0.881-0.959) in the cN0 subgroup-1 cases and 0.828 (95% CI, 0.769-0.887) in the cN0 subgroup-2 cases.

Conclusion

The ultrasound radiomics model only based on the PTC multimodal ultrasound image have high clinical value in predicting CLNM and can provide a reference for treatment decisions.

Ultrasound-based Nomogram for Predicting the Pathological Nodal Negativity of Unilateral Clinical N1a Papillary Thyroid Carcinoma in Adolescents and Young Adults

RATIONALE AND OBJECTIVES

To develop and validate a nomogram incorporating clinical and ultrasound (US) characteristics for predicting the pathological nodal negativity of unilateral clinically N1a (cN1a) papillary thyroid carcinoma (PTC) among adolescents and young adults.

MATERIALS AND METHODS

From December 2016 to August 2021, 278 patients aged ≤ 30 years from two medical centers were enrolled and randomly assigned to the training and validation cohorts at a ratio of 2:1. After performing univariate and multivariate analyses, a nomogram combining all independent predictive factors was constructed and applied to the validation cohort. The performance of the nomogram was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis .

RESULTS

Multivariate logistic regression analysis showed that unilateral cN1a PTC in young patients with Hashimoto's thyroiditis, T1 stage, no intra-tumoral microcalcification, and tumors located in the upper third of the thyroid gland was more likely to be free of central lymph node metastases. The nomogram revealed good calibration and discrimination in both cohorts, with areas under the receiver operating characteristic curve of 0.764 (95% confidence interval [CI]: 0.684-0.843) and 0.728 (95% CI: 0.602-0.853) in the training and validation cohorts, respectively. The clinical application of the nomogram was further confirmed using decision curve analysis.

CONCLUSION

This US-based nomogram may assist the assessment of central cervical lymph nodes in young patients with unilateral cN1a PTC, enabling improved risk stratification and optimal treatment management in clinical practice.

Can pre-operative ultrasound elastography predict aggressive features of solitary papillary thyroid carcinoma?

OBJECTIVE

To investigate whether pre-operative ultrasound elastography (USE) can be used to predict aggressive features of solitary papillary thyroid carcinomas (PTCs).

METHODS

Clinical and USE indices were retrospectively analyzed in 487 patients with surgically confirmed solitary PTCs. The patients were grouped per aggressive features on pathologic testing. Univariate and binary logistic regression analyses were performed to explore independent risk factors of aggressive features.

RESULTS

Univariate analysis revealed standard deviation (SD) values of the tumor shear-wave velocity (SWV) were associated with capsular invasion (p < 0.05). Further, shear-wave elasticity and SWV ratios correlated with extrathyroidal extension (all p < 0.05). The tumor shear-wave elasticity and SWV SD values were associated with cervical lymph node metastasis (CLNM) (all p < 0.05). Binary logistic regression analysis identified location and capsule contact as independent predictive risk factors for capsular invasion (all p < 0.05); size for extrathyroidal extension (all p < 0.05); and sex, age, margin, and suspected CLNM for CLNM (all p < 0.05). However, pre-operational USE indexes were not independent predictors of aggressive features (all p > 0.05).

CONCLUSION

Pre-operative USE indices were not independent risk factors of aggressive features of solitary PTCs. Thus, USE may have a limited value for predicting the aggressive features of PTC.

ADVANCES IN KNOWLEDGE

Pre-operative USE indices may have a limited value for predicting the aggressive features of PTC.

Multimodal predictive factors of metastasis in lymph nodes posterior to the right recurrent laryngeal nerve in papillary thyroid carcinoma

Objective

The lymph node posterior to the right recurrent laryngeal nerve (LN-prRLN) is a crucial component of the central lymph nodes (LNs). We aimed to evaluate multimodal predictive factors of LN-prRLN metastasis in patients with papillary thyroid carcinomas (PTCs), including the clinical data, pathologic data, and preoperative sonographic characteristics of PTCs.

Methods

A total of 403 diagnosed PTC patients who underwent unilateral, sub-total, or total thyroidectomy with central neck dissection were enrolled in this retrospective study. The clinical data, pathologic data, conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) characteristics of PTCs were collected and evaluated for predicting LN-prRLN metastasis.

Results

In this study, 96 PTC patients with LN-prRLN metastasis and 307 PTC patients without LN-prRLN metastasis were included. Univariate analysis demonstrated that PTC patients with LN-prRLN metastasis more often had younger age, larger size, multifocal cancers, A/T < 1, well-margins, microcalcification, petal-like calcification, internal vascularity, centripetal perfusion pattern and surrounding ring enhancement. Multivariate logistic regression analysis revealed that the CEUS centripetal perfusion pattern, central LN detected by ultrasound and LN-arRLN metastasis were independent characteristics for predicting LN-prRLN metastasis in PTC patients.

Conclusion

According to our research, it is essential for clinicians to thoroughly dissect central LNs, particularly LN-prRLNs.

An Ultrasound-based Prediction Model for Occult Contralateral Papillary Thyroid Carcinoma in Adolescents and Young Adults

RATIONALE AND OBJECTIVES

To investigate the occult contralateral papillary thyroid carcinoma (PTC)-associated ultrasound (US) and clinical characteristics and establish a US-based model for the prediction of occult contralateral carcinoma in adolescents and young adults (AYAs) who were diagnosed with unilateral thyroid carcinoma preoperatively.

MATERIALS AND METHODS

From January 2015 to December 2020, patients who were diagnosed with unilateral thyroid carcinoma by preoperative US examination and underwent total thyroidectomy or thyroid lobectomy with more than 60 months of US follow-up at our hospital were retrospectively collected. Univariate and multivariate analyses were applied to identify the independent risk factors associated with occult contralateral PTC in AYAs, on which a prediction model was developed. The performance of the model was evaluated with accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve.

RESULTS

Occult contralateral PTC was found in 91 of 365 (24.9%) PTC patients with a median age at diagnosis of 26 years (interquartile range, 24-29 years). The multivariate analysis indicated that the presence of contralateral benign nodule, intra-tumoral calcification, and intraglandular dissemination were significantly associated with occult contralateral PTC in AYAs. The prediction model, which incorporated all independent predictors, yielded an area under the receiver operating characteristic curve of .661 (95% CI: .602-.719). The accuracy, sensitivity and specificity were 67.9%, 54.9%, and 72.3%, respectively.

CONCLUSION

The US-based prediction model proposed here exhibited a favorable performance for predicting occult contralateral PTC, which might be used to determine the appropriate extent of surgery for AYAs who had a preoperative diagnosis of unilateral thyroid carcinoma.

Combined Conventional Ultrasound and Contrast-Enhanced Computed Tomography for Cervical Lymph Node Metastasis Prediction in Papillary Thyroid Carcinoma

OBJECTIVES

This study aimed to evaluate conventional ultrasound (US) combined with contrast-enhanced computed tomography (CT) of the neck to predict central lymph node metastasis (CLNM) in clinical lymph-negative patients with papillary thyroid carcinoma (PTC), establish a simple preoperative risk-scoring model, and validate its effectiveness in a two-center dataset.

METHODS

A total of 423 patients with PTC preoperatively evaluated by US and contrast-enhanced CT were included in the modeling group, and 102 patients from two hospitals were enrolled in the validation group. Independent predictive factors were determined using multivariate logistic regression analysis. Diagnostic performance was evaluated using receiver operating characteristic curve analysis.

RESULTS

The independent predictive factors for CLNM were age ≤45 years (odds ratio [OR] = 3.950), nodule presence in the non-upper pole (OR = 2.385), nodule size >12.5 mm (OR = 2.130), Thyroid Imaging Reporting and Data System score ≥9 (OR = 2.857), normalized enhancement CT value ≥0.75 (OR = 3.132), central enhancement (OR = 0.222), and capsular invasion (OR = 3.478). The area under the curve (AUC) of the model was 0.790 (95% confidence interval [CI]: 0.747-0.834), and the sensitivity and specificity were 70.4% and 73.9%, respectively. The AUC in the validation group was 0.827 (95% CI: 0.747-0.907), and the sensitivity and specificity were 88.9% and 63.2%, respectively.

CONCLUSIONS

We found conventional US combined with contrast-enhanced CT of the neck to be useful in predicting CLNM preoperatively and established a simple risk-scoring model that might help surgeons with appropriate surgical plans and prognostic evaluation.

Association between diagnostic efficacy of acoustic radiation force impulse for benign and malignant thyroid nodules and the presence or absence of non-papillary thyroid cancer: A meta-analysis

Purpose

The aim of this study was to investigate the diagnostic efficacy of Acoustic Radiation Force Impulse (ARFI) for benign and malignant thyroid nodules in the presence and absence of non-papillary thyroid cancer (NPTC) and to determine the cut-off values of Shear Wave Velocity (SWV) for the highest diagnostic efficacy of Virtual Touch Quantification (VTQ) and Virtual Touch Tissue Imaging and Quantification (VTIQ).

Methods

The diagnostic accuracy of ARFI for benign and malignant thyroid nodules was assessed by pooling sensitivity, specificity and area under the curve (AUC) in each group in the presence and absence of both non-papillary thyroid glands, using histology and cytology as the gold standard. All included studies were divided into two groups according to VTQ and VTIQ, and each group was ranked according to the magnitude of the SWV cutoff value to determine the SWV cutoff interval with the highest diagnostic efficacy for VTQ and VTIQ.

Results

A total of 57 studies were collected on the evaluation of ARFI for the diagnosis of benign and malignant thyroid nodules. The results showed that the presence of non-papillary thyroid carcinoma led to differences in the specificity of VTIQ for the identification of benign and malignant thyroid nodules, and the differences were statistically significant. In addition, the diagnostic efficacy of VTQ was best when the cutoff value of SWV was in the interval of 2.48-2.55 m/s, and the diagnostic efficacy of VTIQ was best when the cutoff value of SWV was in the interval of 3.01-3.15 m/s.

Conclusion

VTQ and VTIQ have a high diagnostic value for benign and malignant thyroid nodules; however, when the malignant nodules in the study contain non-papillary thyroid carcinoma occupying the thyroid gland, the findings should be viewed in a comprehensive manner.

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.

Preoperative Cervical Lymph Node Metastasis Prediction in Papillary Thyroid Carcinoma: A Noninvasive Clinical Multimodal Radiomics (CMR) Nomogram Analysis

This study aimed to evaluate the feasibility of applying a clinical multimodal radiomics nomogram based on ultrasonography (US) and multiparametric magnetic resonance imaging (MRI) for the prediction of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) preoperatively. We performed retrospective evaluations of 133 patients with pathologically confirmed PTC, who were assigned to the training cohort and validation cohort (7 : 3), and extracted radiomics features from the preoperative US, T2-weighted (T2WI),diffusion-weighted (DWI), and contrast-enhanced T1-weighted (CE-T1WI) images. Optimal subsets were selected using minimum redundancy, maximum relevance, and recursive feature elimination in the support vector machine (SVM). For LNM prediction, the radiomics model was constructed by SVM, and Multi-Omics Graph cOnvolutional NETworks (MOGONET) was used for the effective classification of multiradiomics data. Multivariable logistic regression incorporating multiradiomics signatures and clinical risk factors was used to generate a nomogram, whose performance and clinical utility were assessed. Results showed that the nine most predictive features were separately selected from US, T2WI, DWI, and CE-T1WI images, and 18 features were selected in the combined model. The combined radiomics model showed better performance than models based on US, T2WI, DWI, and CE-T1WI. In a comparison of the combined radiomics and MOGONET model, receiver operating curve analysis showed that the area under the curve (AUC) value (95% CI) was 0.84 (0.76-0.93) and 0.84 (0.71-0.96) for the MOGONET model in the training and validation cohorts, respectively. The corresponding values (95% CI) for the combined radiomics model were 0.82 (0.74-0.90) and 0.77 (0.61-0.94), respectively. The MOGONET model had better performance and better prediction specificity compared with the combined radiomics model. The nomogram including the MOGONET signature showed a better predictive value (AUC: 0.81 vs. 0.88) in the training and validation (AUC: 0.74vs. 0.87) cohorts, as compared with the clinical model. Calibration curves showed good agreement in both cohorts. The applicability of the clinical multimodal radiomics (CMR) nomogram in clinical settings was validated by decision curve analysis. In patients with PTC, the CMR nomogram could improve the prediction of cervical LNM preoperatively and may be helpful in clinical decision-making.

Risk factor score for the prediction of central compartment lymph node metastasis in papillary thyroid carcinoma and its clinical significance

Objective

To establish the criteria for a risk factor score (RFS) for predicting the probability of central compartment lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) and to explore the clinical significance of the RFS.

Methods

The data of 412 patients with PTC who underwent surgical resection between May 2013 and July 2016 were retrospectively analysed and divided into two groups: a central LNM group and a non-central LNM group. In each group, the frequency of six risk factors was documented: sex, age, tumour size, extracapsular spread (ECS), tumour multifocality, and tumour location. The maximum likelihood method of discriminant analysis was adopted to calculate patient scores for the six risk indicators. In addition, the data of 104 patients with PTC admitted between July 2016 and December 2016 were prospectively analysed using this method and these six risk factors. A higher score represented one certain possibility that was the more appropriate for one patient.

Results

In the retrospective group, the result was as follows: 129 patients with positive (+) lymph nodes in the central compartment and 168 patients with negative (-) lymph nodes in the central compartment, which was in line with the actual results. In the prospective group, there were 28 patients with positive lymph nodes in the central compartment and 48 patients with negative lymph nodes in the central compartment. The coincidence rates using the RFS were 71.9% for the retrospective group and 73.1% for the prospective group.

Conclusion

By simple and quantitative analyses of the presence of central LNM, the RFS is of great significance when choosing surgical approaches and postoperative individual-based treatment plans, as well as when determining the prognosis of central compartment LNM in patients with PTC.

Nomograms based on preoperative multimodal ultrasound of papillary thyroid carcinoma for predicting central lymph node metastasis

OBJECTIVES

To establish a nomogram for predicting central lymph node metastasis (CLNM) based on the preoperative clinical and multimodal ultrasound (US) features of papillary thyroid carcinoma (PTC) and cervical LNs.

METHODS

Overall, 822 patients with PTC were included in this retrospective study. A thyroid tumor ultrasound model (TTUM) and thyroid tumor and cervical LN ultrasound model (TTCLNUM) were constructed as nomograms to predict the CLNM risk. Areas under the curve (AUCs) evaluated model performance. Calibration and decision curves were applied to assess the accuracy and clinical utility.

RESULTS

For the TTUM training and test sets, the AUCs were 0.786 and 0.789 and bias-corrected AUCs were 0.786 and 0.831, respectively. For the TTCLNUM training and test sets, the AUCs were 0.806 and 0.804 and bias-corrected AUCs were 0.807 and 0.827, respectively. Calibration and decision curves for the TTCLNUM nomogram exhibited higher accuracy and clinical practicability. The AUCs were 0.746 and 0.719 and specificities were 0.942 and 0.905 for the training and test sets, respectively, when the US tumor size was ≤ 8.45 mm, while the AUCs were 0.737 and 0.824 and sensitivity were 0.905 and 0.880, respectively, when the US tumor size was > 8.45 mm.

CONCLUSION

The TTCLNUM nomogram exhibited better predictive performance, especially for the CLNM risk of different PTC tumor sizes. Thus, it serves as a useful clinical tool to supply valuable information for active surveillance and treatment decisions.

KEY POINTS

• Our preoperative noninvasive and intuitive prediction method can improve the accuracy of central lymph node metastasis (CLNM) risk assessment and guide clinical treatment in line with current trends toward personalized treatments. • Preoperative clinical and multimodal ultrasound features of primary papillary thyroid carcinoma (PTC) tumors and cervical LNs were directly used to build an accurate and easy-to-use nomogram for predicting CLNM. • The thyroid tumor and cervical lymph node ultrasound model exhibited better performance for predicting the CLNM of different PTC tumor sizes. It may serve as a useful clinical tool to provide valuable information for active surveillance and treatment decisions.

Real-World Evidence on the Sensitivity of Preoperative Ultrasound in Evaluating Central Lymph Node Metastasis of Papillary Thyroid Carcinoma

INTRODUCTION

Guidelines for prophylactic dissection in clinical central negative node (cN0) of papillary thyroid carcinoma vary among different countries due to the uncertainty on the benefit of dissection. The Chinese guidelines recommend prophylactic central compartment lymph node dissection (pCLND) under professional technology. Preoperative ultrasound (US) evaluation of central lymph node determines the surgical strategy used. Sensitivity differs significantly when US is conducted by different physicians even in diverse hospitals. In this study, the aim was to explore why the Chinese guidelines were different from the America Thyroid Association (ATA) guidelines through the real-world evidence on the preoperative diagnosis of cN0.

METHODS

Preoperative US and surgical pathology data for 1,015 patients with PTC attending 13 Grade-A tertiary hospitals in 2017 were collected. A retrospective analysis using US assessment of CLNM was the conducted to explore the benefits of this approach in China. US physicians in our hospital were trained on scanning the thyroid gland and its regional lymph nodes in normalization. Data of 1,776 patients were collected under the same condition from 2012 to 2017, whose ultrasonography was performed by diverse physicians and doctors. Further, data of 339 patients evaluated by the same sonographer and operated by the same surgical team was collected between 2015 and 2017. In this set of data, US combined CT versus US alone was compared. Patients were grouped into metastasis group and non-metastasis group based on postoperative pathological diagnosis of CLNM. Diagnostic efficacy of US was evaluated.

RESULTS

A total of 925 patients who underwent preoperative ultrasonography in central lymph node, including 825 cases who underwent thyroidectomy and central lymph node dissection were included in this study. The sensitivity of ultrasonography in detecting CLNM was 23.18%, with occult metastasis rate of 40.8%. Data for 1,776 patients comprising paired ultrasonic report and pathological report were collected in our hospital, whose physicians underwent standardized training. The sensitivity was 37.58%. Furthermore, specialized evaluation showed high sensitivity in US/CT (84.58%) than US (58.21%) alone.

CONCLUSION

Although the sensitivity of US could be enhanced by standardized training and combination with CT, the prevalence of low sensitivity of US in real-world multicenter data and the high occult metastasis rate indicated that the Chinese guidelines were based on the current conditions.

Radiomic Model for Determining the Value of Elasticity and Grayscale Ultrasound Diagnoses for Predicting BRAFV600E Mutations in Papillary Thyroid Carcinoma

BRAF^V600E is the most common mutated gene in thyroid cancer and is most closely related to papillary thyroid carcinoma(PTC). We investigated the value of elasticity and grayscale ultrasonography for predicting BRAF^V600E mutations in PTC.

METHODS

138 patients with PTC who underwent preoperative ultrasound between January 2014 and 2021 were retrospectively examined. Patients were divided into BRAF^V600E mutation-free group (n=75) and BRAF^V600E mutation group (n=63). Patients were randomly divided into training (n=96) and test (n=42) groups. A total of 479 radiomic features were extracted from the grayscale and elasticity ultra-sonograms. Regression analysis was done to select the features that provided the most information. Then, 10-fold cross-validation was used to compare the performance of different classification algorithms. Logistic regression was used to predict BRAF^V600E mutations.

RESULTS

Eight radiomics features were extracted from the grayscale ultrasonogram, and five radiomics features were extracted from the elasticity ultrasonogram. Three models were developed using these radiomic features. The models were derived from elasticity ultrasound, grayscale ultrasound, and a combination of grayscale and elasticity ultrasound, with areas under the curve (AUC) 0.952 [95% confidence interval (CI), 0.914-0.990], AUC 0.792 [95% CI, 0.703-0.882], and AUC 0.985 [95% CI, 0.965-1.000] in the training dataset, AUC 0.931 [95% CI, 0.841-1.000], AUC 0. 725 [95% CI, 0.569-0.880], and AUC 0.938 [95% CI, 0.851-1.000] in the test dataset, respectively.

CONCLUSION

The radiomic model based on grayscale and elasticity ultrasound had a good predictive value for BRAF^V600E gene mutations in patients with PTC.

Role of color-coded virtual touch tissue imaging in suspected thyroid nodules

BACKGROUND

Conventional ultrasound (US) is the most widely used imaging test for thyroid nodule surveillance.

OBJECTIVE

We used the color-coded virtual touch tissue imaging (VTI) in the Acoustic Radiation Force Impulse (ARFI) technique to assess the hardness of the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) TR3-5 nodules. The ability of color-coded VTI (CV) to discriminate between benign and malignant nodules was investigated.

METHODS

In this retrospective study, US and CV were performed on 211 TR3-5 thyroid lesions in 181 consecutive patients. All nodules were operated on to obtain pathological results. A multivariate logistic regression model was chosen to integrate the data obtained from the US and CV.

RESULTS

The area under the receiver operating characteristic (ROC) curve for the model was 0.945 (95% CI, 0.914 to 0.976). The cutoff value of predictive probability for diagnosing malignant thyroid nodules was 10.64%, the sensitivity was 94.43%, and the specificity was 83.12%. Through comparing with US and CV, respectively, it had been observed that the regression model had the best performance (all P< 0.001). However, when the US was compared with CV, the difference was not significant (P= 0.3304).

CONCLUSIONS

A combination of US and CV should be recommended for suspected malignant thyroid nodules in clinical practice.

Ultrasound-Based Radiomic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

RATIONALE AND OBJECTIVES

Accurate preoperative identification of lateral cervical lymph node metastasis (LNM) is important for decision-making and clinical management of patients with papillary thyroid carcinoma (PTC). The aim of this study was to develop an ultrasound (US)-based radiomic nomogram to preoperatively predict the lateral LNM in PTC patients.

METHODS

In this retrospective study, a total of 886 patients were enrolled and randomly divided into 2 groups. Radiomic features were extracted from the preoperative US images. A radiomic signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set. Multivariate logistic regression was performed to develop the radiomic nomogram, which incorporating the radiomic signature and the selected clinical characteristics. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness in both the training and validation sets.

RESULTS

The radiomic signature was significantly associated with the lateral LNM in both cohorts (p< 0.001). The nomogram that consisted of radiomic signature, US-reported cervical lymph node (CLN) status, and CT-reported CLN status demonstrated good discrimination and calibration in the training and validation sets with an AUC of 0.946 and 0.914, respectively. The decision curve analysis indicated that the radiomic nomogram was worthy of clinical application.

CONCLUSION

The radiomic nomogram proposed here has good performance for noninvasively predicting the lateral LNM and might be used to facilitate clinical decision-making and potentially improve the survival outcome in selected patients.

Radiogenomic Analysis of Papillary Thyroid Carcinoma for Prediction of Cervical Lymph Node Metastasis: A Preliminary Study

Background

Papillary thyroid carcinoma (PTC) is characterized by frequent metastases to cervical lymph nodes (CLNs), and the presence of lymph node metastasis at diagnosis has a significant impact on the surgical approach. Therefore, we established a radiomic signature to predict the CLN status of PTC patients using preoperative thyroid ultrasound, and investigated the association between the radiomic features and underlying molecular characteristics of PTC tumors.

Methods

In total, 270 patients were enrolled in this prospective study, and radiomic features were extracted according to multiple guidelines. A radiomic signature was built with selected features in the training cohort and validated in the validation cohort. The total protein extracted from tumor samples was analyzed with LC/MS and iTRAQ technology. Gene modules acquired by clustering were chosen for their diagnostic significance. A radiogenomic map linking radiomic features to gene modules was constructed with the Spearman correlation matrix. Genes in modules related to metastasis were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network was built to identify the hub genes in the modules. Finally, the screened hub genes were validated by immunohistochemistry analysis.

Results

The radiomic signature showed good performance for predicting CLN status in training and validation cohorts, with area under curve of 0.873 and 0.831 respectively. A radiogenomic map was created with nine significant correlations between radiomic features and gene modules, and two of them had higher correlation coefficient. Among these, MEmeganta representing the upregulation of telomere maintenance via telomerase and cell-cell adhesion was correlated with ‘Rectlike’ and ‘deviation ratio of tumor tissue and normal thyroid gland’ which reflect the margin and the internal echogenicity of the tumor, respectively. MEblue capturing cell-cell adhesion and glycolysis was associated with feature ‘minimum calcification area’ which measures the punctate calcification. The hub genes of the two modules were identified by protein-protein interaction network. Immunohistochemistry validated that LAMC1 and THBS1 were differently expressed in metastatic and non-metastatic tissues (p=0.003; p=0.002). And LAMC1 was associated with feature ‘Rectlike’ and ‘deviation ratio of tumor and normal thyroid gland’ (p<0.001; p<0.001); THBS1 was correlated with ‘minimum calcification area’ (p<0.001).

Conclusions

The radiomic signature proposed here has the potential to noninvasively predict the CLN status in PTC patients. Merging imaging phenotypes with genomic data could allow noninvasive identification of the molecular properties of PTC tumors, which might support clinical decision making and personalized management.

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

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

Nomogram to assess risk of central cervical lymph node metastasis in patients with cN0 papillary thyroid carcinoma

OBJECTIVE

This study developed and validated an individualized prediction risk model for the need for central cervical lymph node dissection in patients with cN0 papillary thyroid carcinoma (PTC) diagnosed by ultrasound.

METHODS

Upon retrospective review, derivation and internal validation cohorts comprised 1585 consecutive patients with PTC treated from January 2017 to December 2019 at Hospital A. The external validation cohort consisted of 406 consecutive patients treated at Hospital B from January 2016 to June 2020. Independent risk factors for central cervical lymph node metastasis (CLNM) were determined through univariable and multivariable logistic regression analysis. An individualized risk prediction model was constructed and illustrated as a nomogram, which was internally and externally validated.

RESULTS

The following risk factors of CLNM were established: the solitary primary thyroid nodule's diameter, shape, calcification, and capsular abutment-to-lesion perimeter ratio. The areas under the receiver operating characteristic curves of the risk prediction model for the internal and external validation cohorts were 0.921 and 0.923, respectively. The calibration curve showed good agreement between the nomogram-estimated probability of CLNM and the actual CLNM rate in the three cohorts. The decision curve analysis confirmed the clinical usefulness of the nomogram.

CONCLUSION

This study developed and validated a model for predicting risk of CLNM in the individual patient with cN0 PTC, which should be an efficient tool for guiding clinical treatment.

A nomogram for predicting the risk of neck node metastasis in oral cavity carcinoma using acoustic radiation force impulse imaging (ARFI)

BACKGROUND

The study was conducted to assess the accuracy power of Acoustic radiation force impulse imaging (ARFI) and generate a nomogram using ultrasound and ARFI to predict malignant cervical lymph nodes in head and neck squamous cell carcinoma.

MATERIAL AND METHODS

374 cervical lymph nodes from 67 patients were evaluated. The B-mode ultrasonography and the elastography findings were compared with the final histopathological diagnosis. Radiological variables were used to construct nomogram and clinical utility of the nomogram was cross-validated.

RESULTS

In univariate analysis, status of the hilum, Long Axis Diameter, Short axis diameter, colour virtual touch imaging grade (VTI) and shear wave velocity were significant in predicting metastasis in the cervical lymph nodes. In multivariable analysis, it was found that predominance of red over yellow area on colour VTI was significantly associated with lymph node metastasis. A multiple logistic regression performed to ascertain the effects of on the likelihood that patients had lymph node metastasis on histopathology was statistically significant, χ2(10) = 44.96, p < 0.001. The model was able to correctly classify 93.28% of cases and the concordance index (c-index) was estimated to be 0.8773. A nomogram was thus established to predict metastasis in cervical lymph nodes.

CONCLUSIONS

ARFI increases the diagnostic accuracy of conventional USG in predicting metastatic lymph nodes in HNSCC. Adding the constructed nomogram to the conventional diagnostic pathway can provide an alternative option to frozen section and FNAC.

Prediction of ipsilateral lateral cervical lymph node metastasis in papillary thyroid carcinoma: a combined dual-energy CT and thyroid function indicators study

BACKGROUND

Predicting the possibility of ipsilateral lateral cervical lymph node metastasis (ipsi-LLNM) was crucial to the operation plan for patients with papillary thyroid carcinoma (PTC). This study aimed to investigate the independent risk factors for ipsi-LLNM in PTC patients by combining dual-energy computed tomography (DECT) with thyroid function indicators.

METHODS

We retrospectively enrolled 406 patients with a pathological diagnosis of PTC from Jan 2016 to Dec 2019. Ensure the DECT images were clear and the thyroid function indicators were complete. Univariate and multivariate logistic analyses explored the independent risk factors for ipsi-LLNM. To evaluate the cutoff value of each risk factor by using receiver operating characteristic (ROC) curves.

RESULTS

A total of 406 patients with PTC were analyzed, including 128 with ipsi-LLNM and 278 without ipsi-LLNM. There were statistical differences of parameters between the two groups (P < .0001), including serum Tg, Anti-Tg, Anti-TPO, the volume of the primary lesion, calcification, extrathyroidal extension (ETE), and iodine concentration (IC) in the arterial and the venous phases. Independent risk factors for ipsi-LLNM included serum Tg, Anti-Tg, ETE, and IC in the arterial and the venous phases (P < .05). The combined application of the above independent risk factors can predict the possibility of ipsi-LLNM, with an AUC of 0.834. Ipsi-LLNM was more likely to occur when the following conditions were met: with ETE, Tg >  100.01 ng/mL, Anti-Tg >  89.43 IU/mL, IC in arterial phase > 3.4 mg/mL and IC in venous phase > 3.1 mg/mL.

CONCLUSIONS

The combined application of DECT quantitative parameters and thyroid function indicators can help clinicians accurately predict ipsi-LLNM before surgery, thereby assisting the individualized formulation of surgical procedures.

Sonographic Characteristics of Papillary Thyroid Carcinoma With Coexistent Hashimoto's Thyroiditis in the Preoperative Prediction of Central Lymph Node Metastasis

The purpose of this study was to evaluate the usefulness of the sonographic characteristics of papillary thyroid carcinoma (PTC) with Hashimoto's thyroiditis (HT) for predicting central lymph node metastasis (CLNM). One hundred thirty-three patients who underwent thyroidectomy and central cervical lymph node dissection for PTC with coexistent HT were retrospectively analyzed. All PTCs with HT were preoperatively evaluated by ultrasound (US) regarding their nodular number, size, component, shape, margin, echogenicity, calcification, capsule contact with protrusion, vascularity and contrast enhanced ultrasound (CEUS) parameters. Univariate analysis demonstrated that patients with PTCs with HT and CLNM more frequently had age ≤ 45 years, size > 10 mm, a wider than tall shape, microcalcification, hypo-enhancement and peak intensity index < 1 than those without CLNM (all p<0.05). Binary logistic regression analysis demonstrated that size > 10 mm and CEUS hypo-enhancement were independent characteristics for the presence of CLNM. Our study indicated that preoperative US characteristics could offer help in predicting CLNM in PTCs with coexistent HT.

Nomogram Including Elastography for Prediction of Contralateral Central Lymph Node Metastasis in Solitary Papillary Thyroid Carcinoma Preoperatively

Background

It is controversial whether contralateral prophylactic central neck dissection (PCND) should be performed for patients with solitary and clinical lymph node negative (cN₀) papillary thyroid carcinoma (PTC) although routine ipsilateral PCND is required.

Objective

The aim of this study was to develop an improved nomogram including clinical features, ultrasound, and acoustic radiation force impulse (ARFI) elastography for the prediction of contralateral central lymph node metastasis (CLNM) in patients with solitary and cN₀ PTC in the preoperative period.

Materials and Methods

A total of 340 patients were retrospectively included as the training cohort and 170 patients as the external validation cohort. Patients were grouped according to the pathological results of contralateral CLNM. The association between the clinical characteristics, ultrasound, and ARFI elastography and the risk for contralateral CLNM were analyzed. A nomogram was established based on the result of multivariable logistic analysis to predict the risk of contralateral CLNM, which was assessed by internal and external validation.

Results

CLNM was found in 213 patients (41.8%), among whom 142 (27.8%) had ipsilateral CLNM and 95 (18.6%) had contralateral CLNM (including 68 (13.3%) with bilateral CLNM). Multivariable analysis revealed that patients with younger age, male gender, larger tumor size, closer distance from the capsule, microcalcification, and larger SWV_mean were independent predictors associated with the contralateral CLNM (P < 0.05), which was served as the basis of the nomogram. It showed good discrimination (C-index: 0.856) and calibration (χ² = 9.028, P = 0.340, Hosmer–Lemeshow test) in the training cohort, and good discrimination was maintained in the external validation cohort (C-index: 0.792).

Conclusion

The nomogram utilizing the features of ultrasound combined with ARFI elastography in preoperatively predicting the risk of contralateral CLNM in patients with solitary and cN₀ PTC was established, which showed superior performance both in internal and external validation.

MRI-based radiomics analysis to predict preoperative lymph node metastasis in papillary thyroid carcinoma

Background

The aim of the present study was to develop a magnetic resonance imaging (MRI) radiomics model and evaluate its clinical value in predicting preoperative lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC).

Methods

Data of 129 patients with histopathologically confirmed PTC were retrospectively reviewed in our study (90 in training group and 39 in testing group). 395 radiomics features were extracted from T2 weighted imaging (T2WI), diffusion weighted imaging (DWI) and T1 weighted multiphase contrast enhancement imaging (T1C+) respectively. Minimum redundancy maximum relevance (mRMR) was used to eliminate irrelevant and redundant features and least absolute shrinkage and selection operator (LASSO), to additionally select an optimized features' subset to construct the radiomics signature. Predictive performance was validated using receiver operating characteristic curve (ROC) analysis, while decision curve analyses (DCA) were conducted to evaluate the clinical worth of the four models according to different sequences. A radiomics nomogram was built using multivariate logistic regression model. The nomogram's performance was assessed and validated in the training and validation cohorts, respectively.

Results

Seven key features were selected from T2WI, five from DWI, ten from T1C+ and seven from the combined images. The scores (Rad-scores) of patients with LNM were significantly higher than patients with non-LNM in both the training cohort and the validation cohort. The combined model performed better than the T2WI, DWI, and T1C+ models alone in both cohorts. In the training cohort, the area under the ROC (AUC) values of T2WI, DWI, T1C+ and combined features were 0.819, 0.826, 0.808, and 0.835, respectively; corresponding values in the validation cohort were 0.798, 0.798, 0.789, and 0.830. The clinical utility of the combined model was confirmed using the radiomics nomogram and DCA.

Conclusions

MRI radiomic model based on anatomical and functional MRI images could be used as a non-invasive biomarker to identify PTC patients at high risk of LNM, which could help to develop individualized treatment strategies in clinical practice.

Comparisons of ACR TI-RADS, ATA guidelines, Kwak TI-RADS, and KTA/KSThR guidelines in malignancy risk stratification of thyroid nodules

OBJECTIVE

To compare the diagnostic performance and the unnecessary biopsy rates for recommending fine needle aspiration (FNA) of Thyroid Imaging Reporting and Data Systems proposed by American College of Radiology (ACR TI-RADS), American Thyroid Association (ATA) guidelines, TI-RADS proposed by Kwak (Kwak TI-RADS), and Korean Thyroid Association/Korean Society of Thyroid Radiology (KTA/KSThR) guidelines for malignancy risk stratification of thyroid nodules (TNs).

METHODS

The study included 1271 TNs whose cytologic results or surgical pathologic findings were available. Ultrasound images of these TNs were retrospectively reviewed and categorized according to the four guidelines. The diagnostic performances and the unnecessary biopsy rates for recommending FNA of the four guidelines were evaluated.

RESULTS

After multivariate analysis, the most significant independent predictor for malignancy was hypoechogenicity/marked hypoechogenicity (OR: 9.37, 95% CI: 5.40-16.26) (P < 0.001) among the suspicious ultrasound images features. For all nodules and two subgroups (i.e. nodules <10 mm group and nodules ≥10 mm group), ACR TI-RADS demonstrated higher specificities (all P < 0.05) and lower sensitivities (all P < 0.001) than the other guidelines. In the all nodules group and the nodules<10 mm group, ACR TI-RADS and Kwak TI-RADS had higher Azs than the other guidelines (all P < 0.01). The unnecessary biopsy rates for recommending FNA of ACR TI-RADS in the all nodules (≥10 mm) group and the subgroup (10∼19 mm) were all lower than those of the others guidelines (P < 0.001 for all). For the subgroup (≥20 mm), the unnecessary biopsy rate of ACR was lower than that of ATA guidelines and KTA/KSThR guidelines (P < 0.001).

CONCLUSIONS

The four guidelines have good diagnostic efficiency in differentiating TNs. ACR TI-RADS and Kwak TI-RADS have better diagnostic performance than the other guidelines in the all nodules group and the nodules<10 mm group. Considering the comprehensive diagnostic efficacy and unnecessary biopsy rate, ACR TI-RADS is a more desirable classification guideline in clinical practice.

Prediction of cervical lymph node metastasis with contrast-enhanced ultrasound and association between presence of BRAFV600E and extrathyroidal extension in papillary thyroid carcinoma

Objective:

This study aimed to evaluate the correlation between cervical lymph node metastasis (CLNM) and each of the ultrasound features, immunohistochemical factors, and B-type Raf (BRAF^V600E) mutation.

Methods:

A retrospective analysis was performed on 405 patients with single papillary thyroid carcinoma (PTC) nodules, all of whom underwent preoperative sonographic examinations, including gray-scale ultrasound, color Doppler ultrasound, and contrast-enhanced ultrasound (CEUS). All PTC patients were evaluated using 14 clinical and sonographic features, eight immunohistochemical factors, and BRAF^V600E. Multivariate analyses were performed to identify the risk factors for CLNM, and an equation for CLNM was established. The diagnostic value of each modality was compared with a receiver operating characteristic (ROC) curve.

Results:

Among the 405 PTC nodules removed surgically, CLNM was confirmed in 138 patients, whereas extrathyroidal extension was confirmed in 185 patients. Multivariate analyses indicated significant differences between CLNM and non-CLNM groups in three conventional ultrasound features (p < 0.05), whereas other sonographic features, eight immunohistochemical factors, and BRAF^V600E did not indicate significant differences. A ROC curve of 0.757 in the equation exhibited a significant difference compared with the solo factors (p < 0.05 for all). Hyper or isoechoic enhancement at peak time on CEUS was associated with CLNM, whereas the presence of the BRAF^V600E mutation was associated with extrathyroidal extensions although BRAF appeared to be uncorrelated with CLNM in the present study.

Conclusion:

Intensity at peak time, homogeneity, and size are the three most significant features in predicting CLNM in PTC patients, and the presence of the BRAF^V600E mutation was associated with extrathyroidal extensions when PTCs showed a hyper or isoechoic enhancement at peak time in CEUS.

Development and validation of an ultrasound-based nomogram for preoperative prediction of cervical central lymph node metastasis in papillary thyroid carcinoma

Background

Preoperative prediction of central lymph node metastasis (CLNM) holds significant value in determining a patient's suitability for surgical resection and the need for adjuvant treatment, thereby contributing to better therapeutic strategies. This study aimed to build and confirm a nomogram that integrates ultrasound (US) characteristics with clinical features to predict CLNM in patients with papillary thyroid carcinoma (PTC) preoperatively.

Methods

The prediction model was set up with a training dataset that included 512 patients with histopathologically confirmed PTC. The least absolute shrinkage and selection operator (LASSO) regression method was applied to select US features in the development cohort. The patients' US characteristics and clinical features were incorporated into a multivariate logistic regression analysis to develop the nomogram. The clinical feasibility, calibration, and discriminatory ability of the nomogram were evaluated in an independent validation cohort of 306 patients.

Results

Age, sex, tumor size, multiple tumors, and US-based CLNM status were included as independent predictors in the personalized nomogram. The nomogram showed good calibration and discrimination in the training and validation datasets. The addition of the BRAF V600E mutation status did not improve the performance of the nomogram. The decision curve analysis showed the nomogram to have clinical feasibility.

Conclusions

A nomogram that integrates US characteristics with patients' clinical features was built. This US-based nomogram can be expediently applied to promote the personalized preoperative prediction of CLNM and to develop surgical strategies, such as tailored central compartment neck dissection, in patients with PTC.

Papillary thyroid carcinoma: an ultrasound-based nomogram improves the prediction of lymph node metastases in the central compartment

OBJECTIVES

To develop a nomogram based on postoperative clinical and ultrasound findings to quantify the probability of central compartment lymph node metastases (CLNM).

METHODS

A total of 952 patients with histologically confirmed papillary thyroid carcinoma (PTC) were included in this retrospective study and assigned to three groups based on sex and age. The strongest predictors for CLNM were selected according to ultrasound imaging features, and an ultrasound (US) signature was constructed. By incorporating clinical characteristics, a predictive model presented as a nomogram was developed, and its performance was assessed with respect to calibration, discrimination and clinical usefulness.

RESULTS

Predictors contained in the nomogram included US signature, US-reported LN status and age. The US signature was constructed with tumour size and microcalcification. The nomogram showed excellent calibration in the training dataset, with an AUC of 0.826 (95% CI, 0.765-0.887) for male patients, 0.818 (95% CI, 0.746-0.890) for young females and 0.808 (95% CI, 0.757-0.859) for elder females. For male and young female patients, application of the nomogram to the validation cohort revealed good discrimination, with AUCs of 0.813 (95% CI, 0.722-0.904) and 0.814 (95% CI, 0.712-0.915), respectively. Conversely, for elderly female patients, the nomogram failed to show good performance with an AUC of 0.742 (95% CI, 0.661-0.823).

CONCLUSION

This ultrasound-based nomogram may serve as a useful clinical tool to provide valuable information for treatment decisions, especially for male and younger female patients.

KEY POINTS

• Age, gender, US-reported LN status and US signature were the strongest predictors of CLNM in PTC patients and informed the development of a predictive nomogram. • Microcalcification was the strongest predictor in the US signature, as CLMN was identified in approximately 92% of patients characterised by diffuse microcalcification. • Stratified by sex and age, this nomogram achieved good performance in predicting CLNM, especially in male and young female patients. This prediction tool may be useful as an imaging marker for identifying CLNM preoperatively in PTC patients and as a guide for personalised treatment.

Differential diagnosis of <3 cm renal tumors by ultrasonography: a rapid, quantitative, elastography self-corrected contrast-enhanced ultrasound imaging mode beyond screening

OBJECTIVES

To assess the combined diagnostic strategy of contrast-enhanced ultrasound (CEUS) and acoustic radiation force impulse (ARFI) in the precise differential diagnosis of clear cell renal cell carcinoma (CCRCC) and urothelium carcinoma of the renal pelvis (UCRP) with other small renal tumors (SRTs) ＜3 cm in size.

METHODS

The elastography self-corrected CEUS (ESC) mode was established to perform the quantitative differential diagnosis of SRTs (<3 cm). The kidney shear wave velocity (SWV) value recorded by ARFI showed substantial variability in patients with CCRCC (high elasticity value) and UCRP (low elasticity value) compared with other renal masses, thus providing critical self-correction information for the ultrasound differential diagnosis of SRTs.

RESULTS

In this work, the ESC observations and the corresponding ESC criteria show a remarkable 94.6% accuracy in reference to the gold standards, thus allowing the quantitative, early triple distinction of CCRCC with UCRP and other SRTs in patients with suspicious SRTs.

CONCLUSIONS

This ARFI self-corrected CEUS diagnostic strategy is far beyond a screening method and may have the potential to identify a window of therapeutic opportunity in which emerging therapies might be applied to patients with CCRCC and UCRP, reducing overtreatment and medical costs.

ADVANCES IN KNOWLEDGE

In our study, a new rapid and non-invasive elastography self-corrected CEUS (ESC) ultrasound imaging mode was developed, which was useful in the triple distinction of CCRCC, UCRP, and other SRTs with 94.6% accuracy. ESC is a promising method in the differential diagnosis of SRTs with accuracy and practicability far beyond a single screening model.

Ultrasound validation of predictive model for central cervical lymph node metastasis in papillary thyroid cancer on BRAF

Aim: To compare the value of predictive power of the models for central cervical lymph node metastasis (CLNM) in papillary thyroid carcinomas (PTCs). Patients & methods: 220 PTCs were prospectively enrolled into the study with pathological examination. We established a new risk model with univariate and multivariate analyses and receiver-operating characteristic curves were plotted. Z-test was performed to compare the area under two curves and validated the predictive model for central CLNM in PTCs. The comparison of previous and new predictive model was analyzed. Results: Microcalcification, capsule contact or involvement, internal flow and BRAF^V600E mutation were four independent risk factors for PTCs with central CLNMs. The area under the curves for the new and the previous model were 0.948 and 0.934 (p = 0.572), respectively. Conclusion: Two predictive models showed strong consistency in predicting central CLNM in PTCs. The predictive model may be helpful in selecting appropriate treatment method in PTCs.

Role of Ultrasound Acoustic Radiation Force Impulse in Differentiating Benign from Malignant Superficial Lymph Nodes

Objective:

The purpose of this study was to evaluate the diagnostic performance of acoustic radiation force impulse (ARFI) imaging in differentiating benign from malignant peripheral lymphadenopathy.

Materials and Methods:

This was a prospective study approved by the Institutional Review Board with financial grant for the same. Ultrasound and ARFI imaging of peripheral lymph nodes were performed and correlated with pathological results, which were used as the reference standard. The virtual touch tissue imaging and virtual touch tissue quantification parameters of ARFI were analyzed in 86 lymph nodes, of which 78 were included in the study. Using receiver operating characteristic curve analysis, the diagnostic usefulness of ARFI values were evaluated with respect to their sensitivity, specificity, and area under the curve.

Results:

The mean area ratio of benign lymph nodes was 0.88 (±0.2) and that of malignant lymph nodes was 1.17 (±0.14). The mean shear wave velocities (SWV) of benign and malignant lymph nodes were 2.02 m/s (±0.94) and 3.7 m/s (±2.27), respectively. The sensitivity and specificity of virtual touch imaging area ratio in differentiating benign from malignant lymph nodes was 97% and 77%, of SWV was 71% and 70%, and of SWV ratio was 68% and 79%, respectively.

Conclusion:

As ARFI was found to have a superior diagnostic performance over conventional ultrasound and color Doppler in the characterization of lymph nodes, we recommend its routine use in differentiating benign from malignant nodes.

Predicting the factors associated with central lymph node metastasis in clinical node-negative (cN0) papillary thyroid microcarcinoma

PURPOSE

The aim of the present study was to investigate the risk factors associated with central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC).

METHODS

A total of 553 patients with PTMC confirmed by histological examination, who underwent thyroidectomy and central neck dissection (CND), were enrolled. The clinicopathological and ultrasonographic features from the patients were analyzed retrospectively.

RESULTS

PTMC patient age, Hashimoto thyroiditis (HT), tumor location, extrathyroidal extension (ETE), microcalcification and higher E values were correlated with the incidence of CLNM. Multivariate logistic regression analysis showed that age, HT, tumor location, ETE and E_max were related to the extent of CLNM. Chi-squared automatic interaction detection (CHAID) classification tree model showed that patients with tumor in upper/lower third combined ETE had a high risk of CLNM. Furthermore, cN0 PTMC patients with age ≤ 45 years and ETE had more extensive CLNM.

CONCLUSION

Our observations could be helpful for the assessment of prognostic factors of PTMC patients with CLNM.

Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model

OBJECTIVE

To develop a machine learning-based ultrasound (US) radiomics model for predicting tumour deposits (TDs) preoperatively.

METHODS

From December 2015 to December 2017, 127 patients with rectal cancer were prospectively enrolled and divided into training and validation sets. Endorectal ultrasound (ERUS) and shear-wave elastography (SWE) examinations were conducted for each patient. A total of 4176 US radiomics features were extracted for each patient. After the reduction and selection of US radiomics features , a predictive model using an artificial neural network (ANN) was constructed in the training set. Furthermore, two models (one incorporating clinical information and one based on MRI radiomics) were developed. These models were validated by assessing their diagnostic performance and comparing the areas under the curve (AUCs) in the validation set.

RESULTS

The training and validation sets included 29 (33.3%) and 11 (27.5%) patients with TDs, respectively. A US radiomics ANN model was constructed. The model for predicting TDs showed an accuracy of 75.0% in the validation cohort. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and AUC were 72.7%, 75.9%, 53.3%, 88.0% and 0.743, respectively. For the model incorporating clinical information, the AUC improved to 0.795. Although the AUC of the US radiomics model was improved compared with that of the MRI radiomics model (0.916 vs. 0.872) in the 90 patients with both ultrasound and MRI data (which included both the training and validation sets), the difference was nonsignificant (p = 0.384).

CONCLUSIONS

US radiomics may be a potential model to accurately predict TDs before therapy.

KEY POINTS

• We prospectively developed an artificial neural network model for predicting tumour deposits based on US radiomics that had an accuracy of 75.0%. • The area under the curve of the US radiomics model was improved than that of the MRI radiomics model (0.916 vs. 0.872), but the difference was not significant (p = 0.384). • The US radiomics-based model may potentially predict TDs accurately before therapy, but this model needs further validation with larger samples.

Homogeneity Parameter in Contrast-Enhanced Ultrasound Imaging Improves the Classification of Abnormal Cervical Lymph Node after Thyroidectomy in Patients with Papillary Thyroid Carcinoma

Objective

To explore the conventional and contrast-enhanced ultrasound (CEUS) features of cervical lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC) patients postoperatively and analyze its pathological basis.

Materials and Methods

Conventional and CEUS were performed in 86 abnormal cervical lymph nodes (ACLNs) from 56 PTC patients who had received thyroidectomy. Then, fine-needle aspiration (FNA) was taken to confirm pathological results, a multivariate analysis was performed to correlate the sonographic features of the CLNM, and then an equation for CLNM was established.

Results

Fifty-four lymph nodes were confirmed to be metastasis of PTC by FNA. Intensity at peak time, homogeneity, and color flow patterns, cystic change, or microcalcification and echogenicity were significantly associated with CLNM. Multivariate analysis showed three strongest features (homogeneity, intensity of peak, and cystic change or calcification) to be significantly associated with the evidence of CLNM. Then, the equation was established with the following significant predictive factors: P = 1/1 + exp∑[−3.213 + 2.77 ∗ cystic or calcification + 0.13 ∗ CDFI patterns + 3.65 ∗ homogeneity + 2.43 ∗ intensity at peak time].

Conclusion

Depiction of a heterogeneous hyperenhancement of cervical lymph nodes within CEUS studies and cystic change or microcalcification in conventional ultrasound were identified as predictive for metastatic lymph node invasion, and the equation was more accurate for predicting CLNM compared to single B-mode ultrasound and CEUS feature.

Predicting cervical lymph node metastasis in patients with papillary thyroid cancer (PTC) - Why contrast-enhanced ultrasound (CEUS) was performed before thyroidectomy

The objective of this research was to investigate the clinical value of contrast-enhanced ultrasound (CEUS) for prediction of cervical lymph node metastasis (CLNM) in papillary thyroid cancer (PTC).One hundred and eighty-six patients with PTC confirmed by fine needle aspiration (FNA) were preoperatively performed CEUS.A multivariate analysis was performed to predict CLNM by 15 independent variables. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance.There were totally 37 patients with CLNM confirmed by pathology. Multivariate analysis demonstrated that intensity at peak time, capsule contact and size on CEUS were the three strongest independent predictors for CLNM. ROC analyses of these characteristics showed the areas under the curve (Az), sensitivity, and specificity were 0.650, 48.6 %, 79.8 %; 0.586, 67.6%, 49.7%; and 0.612, 56.8%, 64.4% for intensity at peak time, capsule contact, and size, respectively.The CEUS patterns of PTC are relative to not only the size of PTC but also the possibility of CLNM after thyroidectomy. CEUS seem to be a tool to predict CLNM in PTC patients.

Preoperative serum VEGF-C but not VEGF-A level is correlated with lateral neck metastasis in papillary thyroid carcinoma

BACKGROUND

This study aimed to investigate the relationships between serum vascular endothelial growth factor (VEGF)-A or VEGF-C levels and lymph node metastasis (LNM) status in patients with papillary thyroid carcinoma (PTC).

METHODS

The study enrolled 150 patients with pathologically proven PTC who underwent surgery: PTC without LNM, PTC with central neck metastasis, and PTC with lateral neck metastasis.

RESULTS

Preoperative serum VEGF-A levels were 300.12 ± 80.80 pg/mL overall and were not correlated with the presence of LNM. Preoperative serum VEGF-C levels were 132.41 ± 48.48 pg/mL overall and were significantly correlated with the presence of LNM. Serum VEGF-C levels were further increased in patients with lateral neck metastasis and positively correlated with the number of metastatic LNs (rho = 0.252, P = 0.002). Serum VEGF-C, but not VEGF-A, was identified as a significant predictor of lateral neck metastasis.

CONCLUSION

Serum VEGF-C might be a clinically relevant biomarker of lateral neck metastasis in patients with PTC.

Efficacy of apparent diffusion coefficient in predicting aggressive histological features of papillary thyroid carcinoma

PURPOSE

We aimed to evaluate preoperative diffusion-weighted magnetic resonance imaging (DWI) for predicting aggressive histological features in papillary thyroid cancer (PTC).

METHODS

This prospective study included 141 PTC patients, who underwent DWI prior to thyroidectomy; 88 patients with 88 PTC lesions were finally analyzed. Multiple comparisons of mean and minimum apparent diffusion coefficient (ADC) values (ADCmean and ADCmin) and ADC of the solid component (ADCsolid) between the lowly aggressive PTC, highly aggressive PTC without hobnail, and hobnail variant PTC groups were performed by one-way ANOVA or the Welch test. The nonparametric Kruskal-Wallis H-test was used to assess lesion size differences. Receiver-operating characteristic (ROC) curve analysis was also performed.

RESULTS

ADC values in the lowly aggressive PTC group were found to be significantly higher than those in the highly aggressive PTC without hobnail group (ADCmean: 1.35±0.20×10-3 mm2/s vs. 1.16±0.17×10-3 mm2/s, P = 0.003; ADCmin: 1.10±0.17×10-3 mm2/s vs. 0.88±0.16×10-3 mm2/s, P < 0.001; ADCsolid: 1.26±0.23×10-3 mm2/s vs. 1.04±0.17×10-3 mm2/s, P < 0.001). No significant differences for the ADCmean, ADCmin, and ADCsolid were observed between the lowly aggressive and hobnail variant PTC groups (all P > 0.05). Lesion sizes in the hobnail variant PTC group was significantly elevated compared with the lowly aggressive PTC group (2.19±1.21 cm vs. 0.93±0.37 cm, P < 0.001). Areas under the curves (AUCs) for ADCmean, ADCmin, and ADCsolid between the lowly aggressive PTC and highly aggressive PTC group without hobnail were 0.758, 0.851, and 0.787, respectively. The AUC for size between the lowly aggressive and hobnail variant PTC group was 0.896.

CONCLUSION

ADCmin from DWI could potentially provide quantitative information to differentiate lowly aggressive PTC from highly aggressive PTC lesions without hobnail variants.

Value of contrast-enhanced ultrasound combined with elastography in evaluating cervical lymph node metastasis in papillary thyroid carcinoma

BACKGROUND

Cervical lymph node metastasis in papillary thyroid carcinoma (PTC) affects the treatment and prognosis of patients. Ultrasound is a common imaging method for detecting cervical lymph nodes in PTC patients; however, it is not accurate in determining lymph node metastasis.

AIM

To evaluate the value of contrast-enhanced ultrasound combined with elastography in evaluating cervical lymph node metastasis in PTC.

METHODS

A total of 94 patients with PTC were recruited. According to pathological results, lymph nodes were divided into two groups: metastatic group (n = 50) and reactive group (n = 63). The routine ultrasound findings, contrast-enhanced ultrasound and elastography data were recorded and compared. Logistic regression was used to generate predictive probability distributions for the diagnosis of lymph node metastasis with different indicators. Receiver operating characteristic curve analysis was used to test the efficacy of contrast-enhanced ultrasound combined with elastography based on routine ultrasound in evaluating PTC cervical lymph node metastasis.

RESULTS

The ratio of long diameter/short diameter (L/S) ≤ 2, irregular marginal morphology, missing lymphatic portal, peripheral or mixed blood flow distribution, peak intensity (PI), non-uniform contrast distribution and elasticity score in the metastatic group were significantly higher than those in the reactive group (P < 0.05). L/S ratio, missing lymphatic portal, PI and elasticity score had a significant influence on the occurrence of PTC cervical lymph node metastasis (P < 0.05). Furthermore, the area under the curve (AUC) for lymph node metastasis diagnosed using the combination of PI ratio, elasticity score, missing lymphatic portal and LS was 0.936, which was significantly higher than the AUC for PI ratio alone. The difference was statistically significant (P < 0.05). The fitting equation for the combined diagnosis was logit(P) = -12.341 + 1.482 × L/S ratio + 3.529 × missing lymphatic portal + 0.392 × PI + 3.288 × elasticity score.

CONCLUSION

Based on the gray-scale ultrasound, the combination of contrast-enhanced ultrasound and elastography can accurately assess PTC cervical lymph node metastasis.

Association Between Preoperative US, Elastography Features and Prognostic Factors of Papillary Thyroid Cancer With BRAFV600E Mutation

Purpose: To investigate the value of US and elastography for predicting prognostic factors of papillary thyroid cancer (PTC) in the positive BRAF^V600E Mutation group. Materials and Methods: A total of 116 BRAF^V600E Mutation patients with PTCs were enrolled in this prospective study, who were preoperatively evaluated by US, US elasticity imaging (EI), and Virtual Touch tissue imaging (VTI) and Virtual Touch tissue quantification (VTQ) of acoustic radiation force impulse (ARFI) imaging. Multivariate logistic regression analysis was performed to assess 23 independent variables for predicting prognostic factors. Diagnostic performance was evaluated with receiver operating characteristic (ROC) curve analysis. Results: Forty-two (36.2%) of 116 PTC patients with BRAF^V600E Mutation had central lymph node metastasis (LNM). Nine (7.8%) and fifty-six (48.3%) had lateral LNM and extra-thyroidal extension (ETE), respectively. In multivariate logistic regression analyses, rich internal flow [odds ratio [OR]: 6.66] was the best predictor for central LNM, followed by male sex (OR: 4.22), halo sign absence (OR: 2.78) (all P < 0.05). VTQ ratio (OR: 1.57) was the only predictor for lateral LNM (P = 0.02). Rich internal flow (OR: 6.33) was the strongest predictor for ETE, followed by male sex (OR: 3.29), halo sign absence (OR: 2.90), and VTQ ratio (OR: 1.63) (all P < 0.05). Conclusion: VTQ ratio on ARFI imaging, rich internal flow and halo sign absence on US are the predicting prognostic factors in PTC patients with BRAF^V600E Mutation. The specificities were significantly increased by combining ARFI imaging and US features, which has a potential to avoid unnecessary therapeutic neck dissection in the high-risk PTC patients.

Ultrasound elastography of the thyroid: principles and current status

Ultrasound (US) elastography has been introduced as a non-invasive technique for evaluating thyroid diseases. This paper presents a detailed description of the technical principles, peculiarities, and limitations of US elastography techniques, including strain elastography and shear-wave elastography. This review was conducted from a clinical perspective, and aimed to assess the usefulness of US elastography for thyroid diseases in specific clinical scenarios. Although its main focus is on thyroid nodules, the applications of US elastography for other thyroid diseases, such as diffuse thyroid diseases and thyroiditis, are also presented. Furthermore, unresolved questions and directions for future research are also discussed.

Conventional Ultrasound, Immunohistochemical Factors and BRAFV600E Mutation in Predicting Central Cervical Lymph Node Metastasis of Papillary Thyroid Carcinoma

The study was aimed at evaluating the correlation between central cervical lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC) patients and ultrasound (US) features, immunohistochemical factors and BRAF^V600E mutation. A total of 225 consecutive patients (225 PTCs) who had undergone surgery were included. All PTCs were pre-operatively analysed by US with respect to size, components, echogenicity, shape, margins, microcalcification, multiple cancers or not, internal vascularity and capsule contact or involvement. The presence of four immunohistochemical factors, including cytokeratin 19, human bone marrow endothelial cell 1, galectin-3 and thyroid peroxidase, and BRAF^V600E mutation was also evaluated. Univariate and multivariate analyses were performed to identify the risk factors for central CLNM, and a risk model was established. Pathologically, 44% (99/225) of the PTCs had central CLNMs. Multivariate analysis revealed that size ≤10mm, microcalcification, internal vascularity, capsule contact or involvement and BRAF^V600E mutation were independent risk factors for central CLNM. The risk score for central CLNM was calculated as follows: risk score = 1.5 × (if lesion size ≤10 mm) + 1.9 × (if microcalcification) + 0.8 × (if internal flow) + 3.0 × (if capsule contact or involvement) + 1.5 × (if BRAF^V600E mutation). The rating result was divided into six stages, and the relevant risk rates of central CLNM were 0% (0/1), 0% (0/22), 7.4% (4/54), 48.6% (34/70), 71.2% (42/59) and 100% (19/19), respectively. In conclusion, PTC ≤10mm, microcalcification, internal vascularity, capsule contact or involvement and BRAF^V600E mutation are risk factors for central CLNM. The risk model may be useful in treatment planning and management of patients with PTCs.

Clinical value of the VTIQ technology in the differential diagnosis of superficially enlarged lymph nodes

Background Lymph node enlargement is a common clinical finding in clinical practice with different treatment strategies. Purpose To investigate the application of Virtual Touch Image Quantification (VTIQ) to diagnose benign and malignant superficial enlarged lymph nodes. Material and Methods Between December 2015 and August 2016, 116 superficial enlarged lymph nodes were examined by VTIQ. Maximum (V_max), minimum (V_min), and average (V_mean) shear wave velocities (SWV) were obtained from the lymph nodes and from normal muscular tissues (V_n) located at the same level and within 5 mm from the target lymph node. The pathological results were used as the gold standard to evaluate VTIQ. Results All 116 patients underwent fine-needle aspiration biopsy for pathological examination. Forty patients had malignant lymph nodes and 76 patients had benign lymph nodes. Lymph node characteristics on B-mode ultrasound showed no differences between malignant and benign lymph nodes, but there were differences in VTIQ parameters (all P < 0.001). Compared with pathological diagnosis as the gold standard, the area under the ROC curves of V_max, V_min, and V_mean were 0.815, 0.746, and 0.795. The V_max cutoff value to diagnose benign from malignant lymph nodes was 3.045 m/s. The sensitivity, specificity, and positive and negative predictive values were 70%, 78.9%, 63.6%, and 83.3%. Conclusion VTIQ has a clinical application in the differential diagnosis of superficial enlarged lymph nodes.

Risk stratification of thyroid nodules with Bethesda category III results on fine-needle aspiration cytology: The additional value of acoustic radiation force impulse elastography

To assess the value of conventional ultrasound, conventional strain elastography (CSE) and acoustic radiation force impulse (ARFI) elastography in differentiating likelihood of malignancy for Bethesda category III thyroid nodules. 103 thyroid nodules with Bethesda category III results on fine-needle aspiration cytology (FNAC) in 103 patients were included and all were pathologically confirmed after surgery. Conventional ultrasound, CSE and ARFI elastography including ARFI imaging and point shear wave speed (SWS) measurement were performed. Univariate and multivariate analyses were performed to identify the independent factors associated with malignancy. Area under the receiver operating characteristic curve (Az) was calculated to assess the diagnostic performance. Pathologically, 65 nodules were benign and 38 were malignant. Significant differences were found between benign and malignant nodules in ARFI. The cut-off points were ARFI imaging grade ≥ 4, SWS > 2.94 m/s and SWS ratio > 1.09, respectively. ARFI imaging (Az: 0.861) had the highest diagnostic performance to differentiate malignant from benign nodules, following by conventional ultrasound (Az: 0.606 - 0.744), CSE (Az: 0.660) and point SWS measurement (Az: 0.725 - 0.735). Multivariate logistic regression analysis showed that ARFI imaging grade ≥ 4 was the most significant independent predictor. The combination of ARFI imaging with point SWS measurement significantly improved the specificity (100% vs. 80.0%) and positive predictive value (100 % vs. 72.9%) in comparison with ARFI imaging alone. ARFI elastography is a useful tool in differentiating malignant from benign thyroid nodules with Bethesda category III results on FNAC.

Active Surveillance for Papillary Thyroid Microcarcinoma: Challenges and Prospects

Active surveillance (AS) can be considered as an alternative to immediate surgery in low-risk papillary thyroid microcarcinoma (PTMC) without clinically apparent lymph nodes, gross extrathyroidal extension (ETE), and/or distant metastasis according to American Thyroid Association. However, in the past AS has been controversial, as evidence supporting AS in the management of PTMC was scarce. The most prominent of these controversies included, the limited accuracy and utility of ultrasound (US) in the detection of ETE, malignant lymph node involvement or the advent of novel lymph node malignancy during AS, and disease progression. We summarized publications and indicated: (1) US, performer-dependent, could not accurately diagnose gross ETE or malignant lymph node involvement in PTMC. However, the combination of computed tomography and US provided more accurate diagnostic performance, especially in terms of selection sensitivity. (2) Compared to immediate surgery patients, low-risk PTMC patients had a slightly higher rate of lymph node metastases (LNM), although the overall rate for both groups remained low. (3) Recent advances in the sensitivity and specificity of imaging and incorporation of diagnostic biomarkers have significantly improved confidence in the ability to differentiate indolent vs. aggressive PTMCs. Our paper reviewed current imagings and biomarkers with initial promise to help select AS candidates more safely and effectively. These challenges and prospects are important areas for future research to promote AS in PTMC.

MiR-324-5p assists ultrasonography in predicting lymph node metastasis of unifocal papillary thyroid microcarcinoma without extracapsular spread

Ultrasonography is the first choice of lymph node metastasis (LNM) detection which is crucial for therapeutic options of papillary thyroid cancer (PTC). However, the sensitivity of ultrasonography in detecting LNM of PTC is relatively low; especially in central LNM. MiR-324-5p has been reported to play important roles in the metastasis of various cancers. To explore the relationship between miR-324-5p and LNM in PTC, quantitative real-time polymerase chain reaction was performed in PTC tissue and fine needle aspiration (FNA) washout successively. Its correlation with LNM of PTC was analyzed. The clinicopathological and sonographic factors relating to LNM were also studied. Additionally, the function assay of miR-324-5p in PTC cells was conducted. Current study demonstrated that age was an independent protective factor and multifocality, advanced TNM stage, increased transverse diameter of thyroid nodule, ultrasound suspected LNM were independent risk factors of LNM. MiR-324-5p promoted proliferation, migration and invasion of PTC cell line. MiR-324-5p could serve as a candidate predictor along with ultrasonography in predicting LNM, especially central LNM of unifocal papillary thyroid microcarcinoma without extracapsular spread.