Role of ultrasonographic features and quantified BRAFV600E mutation in lymph node metastasis in Chinese patients with papillary thyroid carcinoma

Quantitative parameters of contrast-enhanced ultrasound effectively promote the prediction of cervical lymph node metastasis in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC), the most common endocrine tumor, often spreads to cervical lymph nodes metastasis (CLNM). Preoperative diagnosis of CLNM is important when selecting surgical strategies. Therefore, we aimed to explore the effectiveness of quantitative parameters of contrast-enhanced ultrasound (CEUS) in predicting CLNM in PTC. We retrospectively analyzed 193 patients with PTC undergoing conventional ultrasound (CUS) and CEUS. The CUS features and quantitative parameters of CEUS were evaluated according to PTC size ≤ 10 or > 10 mm, using pathology as the gold standard. For the PTC ≤ 10 mm, microcalcification and multifocality were significantly different between the CLNM (+) and CLNM (-) groups (both P < 0.05). For the PTC > 10 mm, statistical significance was noted between the two groups with respect to the margin, capsule contact, and multifocality (all P < 0.05). For PTC ≤ 10 mm, there was no significant difference between the CLNM (+) and CLNM (-) groups in all quantitative parameters of CEUS (all P > 0.05). However, for PTC > 10 mm, the peak intensity (PI), mean transit time, and slope were significantly associated with CLNM (all P < 0.05). Multivariate analysis showed that PI > 5.8 dB was an independent risk factor for predicting CLNM in patients with PTC > 10 mm (P < 0.05). The area under the curve of PI combined with CUS (0.831) was significantly higher than that of CUS (0.707) or PI (0.703) alone in the receiver operator characteristic curve analysis (P < 0.05). In conclusion, PI has significance in predicting CLNM for PTC > 10 mm; however, it is not helpful for PTC ≤ 10 mm.

Integrating US-guided FNAB, BRAFV600E mutation, and clinicopathologic characteristics to predict cervical central lymph-node metastasis in preoperative patients with cN0 papillary thyroid carcinoma

BACKGROUND

The prevalence of cervical central lymph-node metastasis (CLNM) is high in patients with papillary thyroid carcinoma (PTC). There is considerable controversy surrounding the benefits of prophylactic central lymph-node dissection (pCLND) in patients with clinically negative central compartment lymph nodes (cN0). Therefore, it is crucial to accurately predict the likelihood of cervical CLNM before surgery to make informed surgical decisions.

METHODS

Date from 214 PTC patients (cN0) who underwent partial or total thyroidectomy and pCLND at the Guizhou Provincial People's Hospital were collected and retrospectively analyzed. They were divided into two groups in accordance with cervical CLNM or not. Their information, including clinical characteristics, ultrasound (US) features, pathological results of fine-needle aspirations biopsy (FNAB), and other characteristics of the groups, was analyzed and compared using univariate and multivariate logistic regression analyses.

RESULTS

A total of 214 patients were eligible in this study. Among them, 43.5% (93/214) of PTC patients had cervical CLNM, and 56.5% (121/214) did not. The two groups were compared using a univariate analyses, and there were no significant differences between the two groups in aspect ratio, boundary, morphology, component, and BRAFV600E (P > 0.05), and there were significant differences between gender, age, maximum tumor size, tumor location, capsule contact, microcalcifications, color Doppler flow imaging (CDFI), and Hashimoto's thyroiditis (HT) (P < 0.05). A multivariate logistic regression analysis was performed to further clarify the correlation of these indices. However, only age (OR = 2.455, P = 0.009), maximum tumor size (OR = 2.586, P = 0.010), capsule contact (OR = 3.208, P = 0.001), and CDFI (OR = 2.225, P = 0.022) were independent predictors of cervical CLNM. Combining these four factors, the area under the receiver-operating characteristic (ROC) curve for the joint diagnosis is 0.8160 (95% 0.7596-0.8725). Univariate analysis indicated that capsule contact (P = 0.001) was a possible predictive factor of BRAFV600E mutation.

CONCLUSIONS

In conclusion, four independent predictors of cervical CLNM, including age < 45 years, tumor size > 1.0 cm, capsule contact, and rich blood flow, were screened out. Therefore, a comprehensive assessment of these risk factors should be conducted when designing individualized treatment regimens for PTC patients.

Radiomics features from whole thyroid gland tissue for prediction of cervical lymph node metastasis in the patients with papillary thyroid carcinoma

OBJECTIVE

We aimed to develop a clinical-radiomics nomogram that could predict the cervical lymph node metastasis (CLNM) of patients with papillary thyroid carcinoma (PTC) using clinical characteristics as well as radiomics features of dual energy computed tomography (DECT).

METHOD

Patients from our hospital with suspected PTC who underwent DECT for preoperative assessment between January 2021 and February 2022 were retrospectively recruited. Clinical characteristics were obtained from the medical record system. Clinical characteristics and rad-scores were examined by univariate and multivariate logistic regression. All features were incorporated into the LASSO regression model, with penalty parameter tuning performed using tenfold cross-validation, to screen risk factors for CLNM. An easily accessible radiomics nomogram was constructed. Receiver Operating Characteristic (ROC) curve together with Area Under the Curve (AUC) analysis was conducted to evaluate the discrimination performance of the model. Calibration curves were employed to assess the calibration performance of the clinical-radiomics nomogram, followed by goodness-of-fit testing. Decision curve analysis (DCA) was performed to determine the clinical utility of the established models by estimating net benefits at varying threshold probabilities for training and testing groups.

RESULTS

A total of 461 patients were retrospectively recruited. The rates of CLNM were 49.3% (70 /142) in the training cohort and 53.3% (32/60) in the testing cohort. Out of the 960 extracted radiomics features, 192 were significantly different in positive and negative groups (p < 0.05). On the basis of the training cohort, 12 stable features with nonzero coefficients were selected using LASSO regression. LASSO regression identified 7 risk factors for CLNM, including male gender, maximum tumor size > 10 mm, multifocality, CT-reported central CLN status, US-reported central CLN status, rad-score, and TGAb. A nomogram was developed using these factors to predict the risk of CLNM. The AUC values in each cohort were 0.850 and 0.797, respectively. The calibration curve together with the Hosmer-Lemeshow test for the nomogram indicated good agreement between predicted and pathological CLN statuses in the training and testing cohorts. Results of DCA proved that the nomogram offers a superior net benefit for predicting CLNM compared to the "treat all or none" strategy across the majority of risk thresholds.

CONCLUSION

A nomogram comprising the clinical characteristics as well as radiomics features of DECT and US was constructed for the prediction of CLNM for patients with PTC, which in determining whether lateral compartment neck dissection is warranted.

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

OBJECTIVES

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

METHOD

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

RESULTS

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

CONCLUSION

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

Development and validation of a dynamic nomogram based on conventional ultrasound and contrast-enhanced ultrasound for stratifying the risk of central lymph node metastasis in papillary thyroid carcinoma preoperatively

Purpose

The aim of this study was to develop and validate a dynamic nomogram by combining conventional ultrasound (US) and contrast-enhanced US (CEUS) to preoperatively evaluate the probability of central lymph node metastases (CLNMs) for patients with papillary thyroid carcinoma (PTC).

Methods

A total of 216 patients with PTC confirmed pathologically were included in this retrospective and prospective study, and they were divided into the training and validation cohorts, respectively. Each cohort was divided into the CLNM (+) and CLNM (-) groups. The least absolute shrinkage and selection operator (LASSO) regression method was applied to select the most useful predictive features for CLNM in the training cohort, and these features were incorporated into a multivariate logistic regression analysis to develop the nomogram. The nomogram's discrimination, calibration, and clinical usefulness were assessed in the training and validation cohorts.

Results

In the training and validation cohorts, the dynamic nomogram (https://clnmpredictionmodel.shinyapps.io/PTCCLNM/) had an area under the receiver operator characteristic curve (AUC) of 0.844 (95% CI, 0.755-0.905) and 0.827 (95% CI, 0.747-0.906), respectively. The Hosmer-Lemeshow test and calibration curve showed that the nomogram had good calibration (p = 0.385, p = 0.285). Decision curve analysis (DCA) showed that the nomogram has more predictive value of CLNM than US or CEUS features alone in a wide range of high-risk threshold. A Nomo-score of 0.428 as the cutoff value had a good performance to stratify high-risk and low-risk groups.

Conclusion

A dynamic nomogram combining US and CEUS features can be applied to risk stratification of CLNM in patients with PTC in clinical practice.

Construction and Validation of a Predictive Nomogram Based on Ultrasound for Lymph Node Metastasis of Papillary Thyroid Carcinoma in the Cervical Central Region

ABSTRACT

To establish and validate a nomogram for predicting lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) in the cervical central region. This retrospective study included 287 PTC patients with 309 nodules treated from December 2018 to May 2020 at our hospital. The cohort was divided randomly into a training set and a testing set according to a 7:3 ratio. The training set contained 216 nodules, and the testing set contained 93 nodules. The nomogram was developed using the training set, and the data of the testing set were used to validate the performance of nomogram. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve. The study showed multifocality, thyroid lesion size, and American College of Radiology Thyroid Imaging, Reporting and Data System (TI-RADS) score were significantly independently associated with LNM in the cervical central region. In the testing set, the calibration curve showed that the nomogram had good discrimination with a C-index of 0.775 (95% confidence interval, 0.680-0.869) and adequate calibration ( P = 0.808). By decision curve analysis and clinical impact curve analysis, the nomogram was shown to have a satisfactory net benefit between thresholds of 0.40 and 0.75. The nomogram can be used for predicting LNM of PTC in the cervical central region and may provide valuable guidance for planning the surgical treatment of PTC patients.

Deep learning-based multifeature integration robustly predicts central lymph node metastasis in papillary thyroid cancer

BACKGROUND

Few highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy development.

METHODS

This study included 488 patients diagnosed with PTC by ultrasound-guided fine-needle aspiration biopsy, collected clinicopathological data, analyzed the correlation between CLNM and clinicopathological features using univariate analysis and binary logistic regression, and constructed prediction models.

RESULTS

Binary logistic regression analysis showed that age, maximum diameter of thyroid nodules, capsular invasion, and BRAF V600E gene mutation were independent risk factors for CLNM, and statistically significant indicators were included to construct a nomogram prediction model, which had an area under the curve (AUC) of 0.778. A convolutional neural network (CNN) prediction model built with an artificial intelligence (AI) deep learning algorithm achieved AUCs of 0.89 in the training set and 0.78 in the test set, which indicated a high prediction efficacy for CLNM. In addition, the prediction models were validated in the subclinical metastasis and clinical metastasis groups with high sensitivity and specificity, suggesting the broad applicability of the models. Furthermore, CNN prediction models were constructed for patients with nodule diameters less than 1 cm. The AUCs in the training set and test set were 0.87 and 0.76, respectively, indicating high prediction efficacy.

CONCLUSIONS

The deep learning-based multifeature integration prediction model provides a reference for the clinical diagnosis and treatment of PTC.

A LASSO-based model to predict central lymph node metastasis in preoperative patients with cN0 papillary thyroid cancer

Introduction

Central lymph node metastasis (CLNM) is common in papillary thyroid carcinoma (PTC). Prophylactic central lymph node dissection (PCLND) in clinically negative central compartment lymph node (cN0) PTC patients is still controversial. How to predict CLNM before the operation is very important for surgical decision making.

Methods

In this article, we retrospectively enrolled 243 cN0 PTC patients and gathered data including clinical characteristics, ultrasound (US) characteristics, pathological results of fine-needle aspiration (FNA), thyroid function, eight gene mutations, and immunoenzymatic results. Least absolute shrinkage and selection operator (LASSO) analysis was used for data dimensionality reduction and feature analysis.

Results

According to the results, the important predictors of CLNM were identified. Multivariable logistic regression analysis was used to establish a new nomogram prediction model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve were used to evaluate the performance of the new prediction model.

Discussion

The new nomogram prediction model was a reasonable and reliable model for predicting CLNM in cN0 PTC patients, but further validation is warranted.

Models of ultrasonic radiomics and clinical characters for lymph node metastasis assessment in thyroid cancer: a retrospective study

Background

Preoperative prediction of cervical lymph node metastasis in papillary thyroid carcinoma provided a basis for tumor staging and treatment decision. This study aimed to investigate the utility of machine learning and develop different models to preoperatively predict cervical lymph node metastasis based on ultrasonic radiomic features and clinical characteristics in papillary thyroid carcinoma nodules.

Methods

Data from 400 papillary thyroid carcinoma nodules were included and divided into training and validation group. With the help of machine learning, clinical characteristics and ultrasonic radiomic features were extracted and selected using randomforest and least absolute shrinkage and selection operator regression before classified by five classifiers. Finally, 10 models were built and their area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value were measured.

Results

Among the 10 models, RF-RF model revealed the highest area under curve (0.812) and accuracy (0.7542) in validation group. The top 10 variables of it included age, seven textural features, one shape feature and one first-order feature, in which eight were high-dimensional features.

Conclusions

RF-RF model showed the best predictive performance for cervical lymph node metastasis. And the importance features selected by it highlighted the unique role of higher-dimensional statistical methods for radiomics analysis.

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.

Younger Than 55 Years Old and BRAF V600E Mutation are Risk Factors for Lymph Node Metastasis in Papillary Thyroid Carcinomas ≤1.0 cm but Not in >1.0 cm

Background

Studies on the relationship between BRAF V600E mutation and the clinicopathologic features of papillary thyroid carcinoma (PTC), risk of lymph node metastasis in papillary thyroid microcarcinoma (PTMC) have shown inconsistent results.

Methods

In this retrospective analysis, clinicopathological data of the patients were collected, and molecular testing was done for BRAF V600E mutation. PTC patients are divided into PTC≤1.0cm (PTMC) and PTC>1.0cm, and the relationship between BRAF V600E mutation and clinicopathologic features was analyzed respectively.

Results

Of the 520 PTC patients, 432 (83.1%) were female and 416 (80.0%) were <55 years old. BRAF V600E mutation was detected in 422 (81.2%) tumour samples of PTC. There was no significant difference in the frequency of BRAF V600E mutation between different age groups. There were 250 (48.1%) patients with PTMC and 270 (51.9%) patients with PTC>1.0cm. BRAF V600E mutation was significantly associated with bilateral cancer (23.0% vs 4.9%, P=0.005) and lymph node metastasis (61.7% vs 39.0%, P=0.009) in PTMC patients, while BRAF V600E mutation was significantly associated with bilateral cancer (24.9% vs 12.3%, P=0.048) in PTC>1.0cm patients. Logistic regression analysis showed that, after adjusting for gender, Hashimoto's thyroiditis and calcification, we found that younger age (<55 years old) (OR: 2.384, 95% CI: 1.241-4.579, P=0.009) and BRAF V600E mutation (OR: 2.213, 95% CI: 1.085-4.512, P=0.029) were significantly associated with lymph node metastasis in PTMC, similar results were not obtained in PTC>1.0cm.

Conclusion

Younger age (<55 years old) and BRAF V600E mutation was independent risk factor for lymph node metastasis in PTMC.

Evaluation of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Clinical-Ultrasound Radiomic Machine Learning-Based Model

We aim to develop a clinical-ultrasound radiomic (USR) model based on USR features and clinical factors for the evaluation of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC). This retrospective study used routine clinical and US data from 205 PTC patients. According to the pathology results, the enrolled patients were divided into a non-CLNM group and a CLNM group. All patients were randomly divided into a training cohort (n = 143) and a validation cohort (n = 62). A total of 1046 USR features of lesion areas were extracted. The features were reduced using Pearson's Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified 15-fold cross-validation. Several machine learning classifiers were employed to build a Clinical model based on clinical variables, a USR model based solely on extracted USR features, and a Clinical-USR model based on the combination of clinical variables and USR features. The Clinical-USR model could discriminate between PTC patients with CLNM and PTC patients without CLNM in the training (AUC, 0.78) and validation cohorts (AUC, 0.71). When compared to the Clinical model, the USR model had higher AUCs in the validation (0.74 vs. 0.63) cohorts. The Clinical-USR model demonstrated higher AUC values in the validation cohort (0.71 vs. 0.63) compared to the Clinical model. The newly developed Clinical-USR model is feasible for predicting CLNM in patients with PTC.

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

Background

An accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC.

Methods

A total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness.

Results

The radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis.

Conclusion

The nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00809-2.

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

Purpose

No non-invasive method can accurately determine the presence of central cervical lymph node (CCLN) metastasis in papillary thyroid cancer (PTC) until now. This study aimed to investigate factors significantly associated with CCLN metastasis and then develop a model to preoperatively predict CCLN metastasis in fine-needle aspiration (FNA) reporting suspicious papillary thyroid cancer (PTC) or PTC without lateral neck metastasis.

Patients and Methods

Consecutive inpatients who were diagnosed as suspicious PTC or PTC in FNA and underwent partial or total thyroidectomy and CCLN dissection between May 1^st, 2016 and June 30^th, 2018 were included. The total eligible patients were randomly divided into a training set and an internal validation set with the ratio of 7:3. Univariate analysis and multivariate analysis were conducted in the training set to investigate factors associated with CCLN metastasis. The predicting model was built with factors significantly correlated with CCLN metastasis and validated in the validation set.

Results

A total of 770 patients were eligible in this study. Among them, 268 patients had histologically confirmed CCLN metastasis, while the remaining patients did not. Factors including age, BRAF mutation, multifocality, size, and capsule involvement were found to be significantly correlated with the CCLN metastasis in univariate and multivariate analysis. A model used to predict the presence CCLN metastasis based on these factors and US CCLN status yielded AUC, sensitivity, specificity and accuracy of 0.933 (95%CI: 0.905-0.960, p < 0.001), 0.816, 0.966 and 0.914 in the training set and 0.967 (95%CI: 0.943-0.991, p < 0.001), 0.897, 0.959 and 0.936 in the internal validation set.

Conclusion

Age, BRAF mutation, multifocality, size, and capsule involvement were independent predictors of CCLN metastasis in FNA reporting suspicious PTC or PTC without lateral neck metastasis. A simple model was successfully built and showed excellent discrimination to distinguish patients with or without CCLN metastasis.

Nomogram based on radiomics analysis of ultrasound images can improve preoperative BRAF mutation diagnosis for papillary thyroid microcarcinoma

BACKGROUND

The preoperative identification of BRAF mutation could assist to make appropriate treatment strategies for patients with papillary thyroid microcarcinoma (PTMC). This study aimed to establish an ultrasound (US) radiomics nomogram for the assessment of BRAF status.

METHODS

A total of 328 PTMC patients at the China-Japan Friendship Hospital between February 2019 and November 2021 were enrolled in this study. They were randomly divided into training (n = 232) and validation (n = 96) cohorts. Radiomics features were extracted from the US images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the BRAF status-related features and calculate the radiomics score (Rad-score). Univariate and multivariate logistic regression analyses were subsequently performed to identify the independent factors among Rad-score and conventional US features. The US radiomics nomogram was established and its predictive performance was evaluated via discrimination, calibration, and clinical usefulness in the training and validation sets.

RESULTS

Multivariate analysis indicated that the Rad-score, composition, and aspect ratio were independent predictive factors of BRAF status. The US radiomics nomogram which incorporated the three variables showed good calibration. The discrimination of the US radiomics nomogram showed better discriminative ability than the conventional US model both in the training set (AUC 0.685 vs. 0.592) and validation set (AUC 0.651 vs. 0.622). Decision curve analysis indicated the superior clinical applicability of the nomogram compared to the conventional US model.

CONCLUSIONS

The US radiomics nomogram displayed better performance than the conventional US model in predicting BRAF mutation in patients with PTMC.

Exploring Somatic Alteration Associating With Aggressive Behaviors of Papillary Thyroid Carcinomas by Targeted Sequencing

Wisely differentiating high-risk papillary thyroid carcinoma (PTC) patients from low-risk PTC patients preoperatively is necessary when comes to making a personalized treatment plan. It is not easy to stratify the risk of patients according to sonography or lab results before surgery. This study aims to seek out potential mutation gene markers that may be helpful in stratifying the risk of PTC. A custom panel of 439 PTC relevant and classic tumor metabolic pathway relevant genes was designed. Targeted capture sequencing was performed on 35 pairs of samples from 35 PTC tumors and 35 para-tumor thyroid tissues obtained during surgery. Variant calling and detection of cancer gene mutations were identified by bio-information analysis. Ingenuity Pathway Analysis (IPA) was performed to do functional enrichment analysis of high-frequency mutant genes. Immunohistochemistry (IHC) was performed on 6 PTC patients to explore the expression of protein associated with interested genes. Event-free survival (EFS) was calculated to determine which genes might affect the prognosis of patients. We have identified 32 high-frequency mutant genes in PTC including BRAF. RBL2 was found to be significantly correlated to event-free survival, FOXO1, MUC6, PCDHB9, NOTCH1, FIZ1, and RTN1 were significantly associated with EFS, while BRAF mutant was not correlated to any of the prognosis indicators. Our findings in this study might open more choices when designing thyroid gene panels used in FNA samples to diagnose PTC and predict the potentially aggressive behavior of PTC.

Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery

PURPOSE

To compare predictive efficiency of multiple classifiers modeling and establish a combined magnetic resonance imaging (MRI) radiomics model for identifying lymph node (LN) metastases of papillary thyroid cancer (PTC) preoperatively.

MATERIALS AND METHODS

A retrospective analysis based on the preoperative MRI scans of 109 PTC patients including 77 patients with LN metastases and 32 patients without metastases was conducted, and we divided enroll cases into trained group and validation group. Radiomics signatures were selected from fat-suppressed T2-weighted MRI images, and the optimal characteristics were confirmed by spearman correlation test, hypothesis testing and random forest methods, and then, eight predictive models were constructed by eight classifiers. The receiver operating characteristic (ROC) curves analysis were performed to demonstrate the effectiveness of the models.

RESULTS

The area under the curve (AUC) of ROC based on MRI texture diagnosed LN status by naked eye was 0.739 (sensitivity = 0.571, specificity = 0.906). Based on the 5 optimal signatures, the best AUC of MRI radiomics model by logistics regression classifier had a considerable prediction performance with AUCs 0.805 in trained group and 0.760 in validation group, respectively, and a combination of best radiomics model with visual diagnosis of MRI texture had a high AUC as 0.969 (sensitivity = 0.938, specificity = 1.000), suggesting combined model had a preferable diagnostic efficiency in evaluating LN metastases of PTC.

CONCLUSION

Our combined radiomics model with visual diagnosis could be a potentially effective strategy to preoperatively predict LN metastases in PTC patients before clinical intervention.

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.

[Research progress on the relationship between the Raf murine sarcoma viral oncogene homolog B gene mutation and lymph node metastasis of papillary thyroid carcinoma]

In recent years, with the improvement of the sensitivity of examination equipment and the change of people's living environment and diet, the rate of thyroid cancer has risen rapidly, which has increased nearly five folds in 10 years. The pathogenesis, clinical manifestation, biological behavior, treatment and prognosis of thyroid carcinoma of different pathological types are obviously different. Papillary thyroid carcinoma (PTC) can develop at any age, which accounts for about 90% of thyroid cancer. It progresses slowly and has favourable prognosis, but lymph node metastasis appears easily. Whether PTC is accompanied by lymph node metastasis has an important impact on its prognosis and outcome. The Raf murine sarcoma viral oncogene homolog B(BRAF)gene mutation plays a crucial role in PTC lymph node metastasis. Having an in-depth understanding of the specific role and mechanism of BRAF gene mutation in PTC is expected to provide new ideas for diagnosis and treatment of PTC.

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

OBJECTIVES

To screen out the predictors of central cervical lymph node metastasis (CLNM) for papillary thyroid carcinoma (PTC) and establish a prediction model to guide the operation of PTC patients (cN0).

METHODS

Data from 296 PTC patients (cN0) who underwent thyroid operation at the Second Affiliated Hospital of Chongqing Medical University were collected and retrospectively analyzed. They were divided into two groups in accordance with central CLNM or not. Their information, including ultrasound (US) features, BRAF^V600E status, and other characteristics of the two groups, was analyzed and compared using univariate and multivariate logistic regression analyses, and the independent predictors were selected to construct a nomogram. The calibration plot, C-index, and decision curve analysis were used to assess the prediction model's calibration, discrimination, and clinical usefulness.

RESULTS

A total of 37.8% (112/296) of PTC patients had central CLNM, and 62.2% (184/296) did not. The two groups were compared using a univariate logistic regression analysis, and there were no significant differences between the two groups in sex, aspect ratio, boundary, morphology, hypoechoic nodule, thyroid peroxidase antibody, or tumor location (P>0.05), and there were significant differences between age, tumor size, capsule contact, microcalcifications, blood flow signal, thyroglobulin antibodies (TgAb), and BRAF gene status (P<0.05). A multivariate logistic regression analysis was performed to further clarify the correlation of these indices. However, only tumor size (OR=2.814, 95% Cl=1.634~4.848, P<0.001), microcalcifications (OR=2.839, 95% Cl=1,684~4.787, P<0.001) and TgAb (OR=1.964, 95% Cl=1.039~3,711, P=0.038) were independent predictors of central CLNM and were incorporated and used to construct the prediction nomogram. The model had good discrimination with a C-index of 0.715. An ROC curve analysis was performed to evaluate the accuracy of this model. The decision curve analysis showed that the model was clinically useful when intervention was decided in the threshold range of 16% to 80%.

CONCLUSION

In conclusion, three independent predictors of central CLNM, including tumor size (> 1.0 cm), US features (microcalcifications), and TgAb (positive), were screened out. A visualized nomogram model was established based on the three predictors in this study, which could be used as a basis of central cervical lymph node dissection (CLND) for PTC patients (cN0).

Associations of BRAF V600E, clinical pathology and imaging factors with the recurrence rate of papillary thyroid microcarcinoma

In the present study, the recurrence rate of papillary thyroid microcarcinoma (PTMC) was assessed by analyzing postoperative follow-up data of affected patients and its associations with BRAF V600E, clinical pathology and imaging factors were explored. A total of 506 patients with PTMC were selected who underwent surgery from January 2014 to March 2016. The maximal diameter of thyroid nodules was ≤1 cm and all patients who underwent BRAF V600E testing and evaluation for lymph node metastasis. Postoperatively, each patient was regularly followed up to detect recurrence. Categorical variables were comparatively analyzed using univariate Cox linear regression analysis to screen for protective and adverse factors influencing recurrence of PTMC. A stepwise Cox proportional hazards regression model analysis was performed to explore risk factors affecting recurrence. Among the 506 patients, 477 were followed up, 29 were lost to follow-up and 26 patients experienced recurrence. The 5-year recurrent rate of PTMC was 5.45%. The univariate Cox regression analysis indicated that PTMC recurrence was influenced by BRAF V600E, sex, multifocality, capsular invasion and lateral cervical lymph node metastasis (P<0.05), but not by age, tumor location on the thyroid, size, single central lymph node metastasis, distant metastasis and operative approach (P>0.05). The significant factors associated with recurrent PTMC were subjected to stepwise multivariate Cox proportional hazards regression model analysis and the results indicated that BRAF V600E, sex, multifocality and lateral cervical lymph node metastasis were independent factors influencing recurrence in patients with PTMC, with a statistically significant difference (P<0.05). In conclusion, BRAF V600E, sex, multifocality and lateral cervical lymph node metastasis are independent risk factors for recurrent PTMC.

Genetic alterations in cfDNA of benign and malignant thyroid nodules based on amplicon-based next-generation sequencing

Background

Circulating cell-free DNA (cfDNA) serves as a biomarker in multiple malignant diseases. However, controversy still surrounds the role of cfDNA detection in the diagnosis and monitoring of papillary thyroid carcinoma (PTC). This study set out to identify the role of cfDNA detection in distinguishing between benign and malignant thyroid nodules.

Methods

Tissue, blood cell, and plasma samples were collected from 10 patients with benign nodules and 10 patients with malignant nodules. The DNA isolated from these samples was subject to PCR-based amplification using primers designed for 50 proto-oncogenes and tumor suppressor genes. PCR products were sequenced using Illumina technology, and the mutations were detected with varScan among sequencing data for each sample and comparative analysis was carried out.

Results

Through amplicon sequencing, we found one non-synonymous somatic mutation in the benign nodules and three in the malignant nodules. Among these four mutations, BRAF^V600E mutation was detected in the tissue samples of 8 out of the 10 PTC patients, but it was not detected in the benign nodules. However, no BRAF^V600E mutation was detected in cfDNA. Further differential analysis of cfDNA indicated that some genes had more mutations in benign patients than in malignant patients, such as MET and IDH, and some genes had more mutations in malignant patients, such as PIK3CA and EZH2.

Conclusions

We found that BRAF^V600E mutation was a credible disease-related mutation in PTC; however, it could not be detected in cfDNA. Moreover, there was a large difference in mutation gene distribution between benign and malignant thyroid nodules.

Antibacterial, anti-efflux, anti-biofilm, anti-slime (exopolysaccharide) production and urease inhibitory efficacies of novel synthesized gold nanoparticles coated Anthemis atropatana extract against multidrug- resistant Klebsiella pneumoniae strains

In this study, the antibacterial, anti-efflux, anti-biofilm, anti-slime (exopolysaccharide) production and urease inhibitory efficacies of green synthesized gold nanoparticles (AuNPs) coated Anthemis atropatana extract against multidrug- resistant (MDR) Klebsiella pneumoniae strains were evaluated. The green synthesized AuNPs were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffractometer (XRD), particle size distribution, zeta potential and Fourier-transform infrared spectroscopy (FTIR). Then, antibacterial, anti-slime (exopolysaccharide) production, anti-biofilm and anti-efflux activities of AuNPs were investigated using micro-dilation, Congored agar, microtiter plate and MIC of ethidium bromide methods, respectively. Subsequently, the expression of mrkA, wzm and acrB genes was evaluated using quantitative Real-Time PCR (qRT-PCR). The synthesized AuNPs exhibited antibacterial activity against MDR strains of K. pneumoniae (minimum inhibitory concentration (MIC) of 6.25-50 µg/ml), as well as showed significant anti-slime (exopolysaccharide) production, anti-biofilm and anti-efflux activities against MDR strains. AuNPs showed significant inhibition against jack-bean urease and down-regulated the expression of mrkA, wzm and acrB genes. Moreover, the in vitro cytotoxic activity confirmed by MTT assay on the HEK-293 normal cell line showed negligible cytotoxicity. Thus, the present study suggests the potential use of AuNPs in the development of novel therapeutics for the prevention of biofilm-associated K. pneumoniae infections.

Genomic characterization of high-recurrence risk papillary thyroid carcinoma in a southern Chinese population

BACKGROUND

The objective of this study was to investigate genetic variations and the relationships between these genetic variations and clinicopathological features of high-recurrence risk papillary thyroid carcinoma in a southern Chinese population.

METHODS

One hundred sixty-eight patients of high-recurrence risk papillary thyroid carcinoma were recruited for this study from 2017 to 2018. Formalin-fixed paraffin-embedded tissue and the data of clinicopathological characteristics were all collected and analyzed from these patients. We used next-generation sequencing technology to investigate the targeted gene mutations and gene fusions of the pathology specimens.

RESULTS

The frequency of candidate tumor driver gene mutation was 85.1% in 143 patients, including BRAF V600E mutation in 119 patients(70.8%), RET fusion in 13 patients(7.7%), TERT promoter mutations in 11 patients(6.5%), RAS (HRAS, NRAS, KRAS) gene mutations in 10 patients(6.0%), and other mutations involving TP53, PIK3CA, AKT1, PTEN and NTRK1. Concomitant presence of more than two genetic aberrations was seen in 27 patients (16.1%). Our study showed that BRAF V600E mutation is highly correlated with conventional PTC (p < 0.001), BRAF V600E and TERT promoter mutation duet was associated with older patient age (> 45, p = 0.003) and higher disease stage of III or IV (p = 0.002). RAS gene and BRAF V600E co-mutations were only seen in multifocal PTC (p = 0.015).

CONCLUSION

In our high-recurrence risk PTC cohort, most patients had more than one driver gene aberration. Coexistence of BRAF V600E with TERT promoter mutations or with RAS mutations were significantly correlated with worse clinicopathological characteristics.

Ultrasound Elastic Parameters Predict Central Lymph Node Metastasis of Papillary Thyroid Carcinoma

BACKGROUND

This study aimed to explore the new factors that can predict central lymph node metastasis (CLNM) of papillary thyroid carcinoma (PTC) independently from ultrasound characteristics, elastic parameters, and endocrine indicators.

METHODS

A total of 391 patients with PTC undergoing thyroidectomy and prophylactic central lymph node dissection from January 2017 to June 2019 were collected to determine the independent predictors of CLNM by single-factor and multivariate logistic regression analysis.

RESULTS

Multivariate logistic regression analysis showed 9 independent predictors of CLNM, age, male, tumors in the middle or lower poles (without tumors in the isthmus), tumors in the isthmus, multiple tumors, and maximum tumor diameter measured by ultrasound, microcalcification, visible surrounding blood flow signal, and the maximum value of elastic modulus (Emax).We used the aforementioned factors to establish a scoring prediction model: predictive score Y(P) = 1/[1 + exp (1.444 + 0.084 ∗ age - 0.834 ∗ men - 0.73 ∗ multifocality - 2.718 ∗ tumors in the isthmus - 0.954 ∗ tumors in the middle or lower poles - 0.086 ∗ tumor maximum diameter - 1.070 ∗ microcalcification - 0.892 ∗ visible surrounding blood flow signal - 0.021 ∗ Emax)]. The area under the curve of the receiver operating characteristic was 0.827. It was found that 0.524 was the highest index of Youden, and the best cutoff value for predicting CLNM. When Y(P)≥0.524, the risk of CLNM in patients with PTC is predicted to be high. Predictive accuracy was 78.5% and 72.4% in the internal validation group and 78.6% in the external validation group.

CONCLUSIONS

These data indicate that the scoring prediction model could provide a scientific and quantitative way to predict CLNM in patients with PTC.

Associations of the BRAF V600E Mutation and PAQR3 Protein Expression with Papillary Thyroid Carcinoma Clinicopathological Features

The BRAF^V600E mutation is the most prevalent genetic event in patients with papillary thyroid cancer (PTC). However, no study has investigated the expression of PAQR3 in papillary thyroid tissues in relation to the BRAF^V600E mutation and the clinicopathological features of PTC patients. Furthermore, the potential associations of the BRAF^V600E mutation, PAQR3 expression and clinicopathological parameters in the cancerous tissues of PTC patients have not been investigated. This study was conducted on 60 patients with PTC who were treated surgically at our institution from 2017 to 2018. PCR was used to amplify DNA by the amplification refractory mutation system (ARMS) method to detect BRAF^V600E gene mutations. In addition, immunohistochemical techniques were utilized to assess PAQR3 expression in tumor tissue sections. The BRAF^V600E mutation was associated with lymph node metastasis (LNM, p < 0.05) but not with other clinicopathological features. Low PAQR3 expression was associated with extrathyroidal extension and LNM (χ² = 7.143, p = 0.009; χ² = 6.459, p = 0.014, respectively). Furthermore, a statistically significant association was observed between chronic lymphocytic thyroiditis and LNM (χ² = 5.275, p = 0.0250). A linear relationship between the BRAF^V600E mutation and PAQR3 protein expression has not been identified. These factors may be independent risk factors of extrathyroidal extension and LNM in PTC and be used to indicate the invasiveness of PTC tumors. Higher quality, multivariate analyses based on larger samples from around the world are urgently needed to further validate and revise our findings in the future.

High BRAFV600E mutation frequency in Chinese patients with papillary thyroid carcinoma increases diagnostic efficacy in cytologically indeterminate thyroid nodules

To estimate the BRAFV600E mutation frequency in Chinese patients with papillary thyroid carcinoma (PTC), and the diagnostic value of BRAFV600E mutation status in thyroid nodules with indeterminate TBSRTC categories.A total of 4875 consecutive samples for thyroid ultrasound-guided fine-needle aspiration cytology (FNAC) and BRAF mutation analysis were collected from patients at Jiangsu Province Hospital on Integration of Chinese and Western Medicine. Among all the cases, 314 underwent thyroidectomy. According to TBSRTC categories, FNAC was performed for a preoperative diagnosis. ROC of the subject was constructed to evaluate the diagnostic value of these 2 methods and their combination.BRAF mutation in FNAC of thyroid nodules occurred in 2796 samples (57.35%). Of 353 nodule samples from 314 patients with thyroid operation, 333 were pathologically diagnosed as PTC. Of these PTC patients, 292 (87.69%) were found to have BRAF mutation in their preoperative FNAC. In 175 cytologically indeterminate thyroid nodules, BRAF mutation identified 88% of PTC. According to ROC data, BRAF mutation testing had an obviously higher sensitivity (87.69%) and specificity (100.00%) than TBSRTC. Combining BRAF mutation testing and TBSRTC achieved the largest AUC (0.954). For 41 PTC with a negative BRAF mutation in preoperative evaluation, the repeated BRAF mutation testing found out 12 samples with BRAF mutation. The true BRAF mutation rate of Chinese PTC patients was 91.29%.Chinese patients with PTC have a higher frequency of BRAF mutation. The BRAF mutation testing affords a high diagnostic value in thyroid nodules with indeterminate cytology.