Suspicious ultrasound characteristics correlate with multiple factors that predict central lymph node metastasis of papillary thyroid carcinoma: Significant role of HBME-1

The Association between Lymphocytic Thyroiditis and Papillary Thyroid Cancer Harboring Mutant BRAF: A Systematic Review and Meta-Analysis

Background: Papillary thyroid cancer (PTC) and lymphocytic thyroiditis (LT) co-occur with a prevalence of about 30%. PTC harboring BRAFV600E (PTC-BRAF) confers a worse prognosis, but it is unclear if LT alters prognostic features and recurrence of PTC. Objective: We compared the prevalence of PTC-BRAF with and without LT. The risk of adverse pathological features in (i) PTC in the presence and absence of BRAF mutation, irrespective of LT status, was compared to (ii) PTC in the presence and absence of LT, irrespective of BRAF status. Methods: We searched PubMed, Embase, and Web of Science Core Collection for observational studies published from 2010 to June 2023 on adult patients with PTC. The search strategy yielded 47 studies with relevant data. Data of baseline characteristics, clinicopathological features, and the quality assessment tool were extracted by two reviewers. The study was registered with PROSPERO (CRD42023437492). Results: Of the 47 studies, 39 studies with a total cohort of 28 143, demonstrated that the odds of PTC-BRAF were significantly lower in the presence of LT compared to its absence (odds ratio [OR] 0.53, 95% confidence interval [CI]: 0.48-0.58, p < 0.00001). In PTC-BRAF patients, there was a positive association of central neck nodal disease (CNND), PTC > 1 cm, extra-thyroidal extension, American Joint Committee on Cancer (AJCC) Stage 3-4, and multifocality with pooled ORs of 1.54 (95% CI: 1.16-2.04), 1.14 (95% CI: 0.82-1.58), 1.66 (95% CI: 1.40-1.97), 1.53 (95% CI: 1.35-1.75), and 1.24 (95% CI: 1.11-1.40) respectively, compared to wild-type PTC, irrespective of LT status. In the same studies, PTC with LT patients had lower pooled ORs of 0.64 (95% CI: 0.51-0.81) for CNND, 0.83 (95% CI: 0.73-0.95) for PTC > 1 cm, 0.71 (95% CI: 0.58-0.86) for ETE, 0.84 (95% CI: 0.75-0.94) for AJCC Stage 3-4 compared to PTC without LT, irrespective of BRAF status. PTC recurrence was not affected by BRAF or LT, with pooled ORs of 1.12 (95% CI: 0.66-1.90, p = 0.67) and 0.60 (95% CI: 0.28-1.30, p = 0.20) respectively. Similar results were seen with recurrence expressed as hazard ratio in this limited data-set. Conclusion: The odds of PTC-BRAF are significantly lower in the presence of LT than without. PTC with LT, irrespective of BRAF status, was significantly associated with better prognostic factors. Further studies are required to evaluate if LT inhibits PTC-BRAF, and whether this is relevant to the role of immunotherapy in advanced thyroid cancer.

Ultrasound and clinical factors predicting central lymph node metastases in patients with unilateral multifocal papillary thyroid carcinoma

OBJECTIVE

This retrospective study involving a large dataset of unilateral multifocal papillary thyroid carcinoma (UM-PTC) sought to identify factors that predict central lymph node metastases (CLNM) in patients.

METHODS

We identified a cohort of 158 patients who underwent cervical ultrasonography followed by UM-PTC diagnosis based on postoperative pathology. The relationship between CLNM and UM-PTC clinical ultrasound features was evaluated using univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis was used to determine the ability of total tumor diameter (TTD) to predict CLNM.

RESULTS

Among the 158 UM-PTC patients, the incidence of CLNM was 29.7% (47/158). Univariate and multivariate analyses revealed that a number of similarity of sonographic features (NSSF) ≥4 (odds ratio [OR] = 11.335, 95% confidence interval [CI]: 3.95-32.50, p = 0.000), microcalcifications (OR = 3.54, 95% CI: 1.30-9.70, p = 0.014), a TTD of ≥2 cm (OR = 4.48, 95% CI: 1.62-12.34, p = 0.004), number of nodules ≥3 (OR = 13.17, 95% CI: 3.24-53.52, p = 0.000), and Lateral cervical lymph node metastasis (LLNM) (OR = 5.57, 95% CI: 1.59-19.48, p = 0.007) were independently associated with CLNM in UM-PTC. ROC curve analysis revealed that the TTD cut-off of 1.795 cm had a sensitivity of 0.723 and a specificity of 0.676 for predicting CLNM.

CONCLUSIONS

Patients with UM-PTC are at high risk of CLNM. NSSF ≥4, microcalcifications, TTD of ≥2 cm, LLNM, and a number of nodules ≥3 were independently associated with CLNM. Our data show that ultrasound may guide surgical decisions in the treatment of UM-PTC.

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.

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.

A nomogram based on the risk factors of cervical lymph node metastasis in papillary thyroid carcinoma coexistent with Hashimoto's thyroiditis

OBJECTIVE

The purpose of this study was to explore the risk factors of cervical lymph node metastasis(LNM) in papillary thyroid carcinoma(PTC) coexistent with Hashimoto's thyroiditis(HT).

METHODS

The clinical data of patients who underwent thyroid operation between November 2016 and January 2020 in our hospital were analyzed retrospectively. The association between sonographic features and the risk factors of cervical LNM in PTC coexistent with HT was analyzed and a nomogram based on the risk factors was built.

RESULTS

Age, US features as calcification, blood flow type, distance between thyroid nodule and fibrous capsule were risk factors of cervical LNM(P < 0.05).Size, SWVmax and SWVmean of thyroid nodule, SWVratio between thyroid nodule and thyroid gland were higher in PTCs with LNM than those without LNM(P < 0.05). The ROC curve showed that the cutoff value of SWVratio for predicting LNM was 1.29 (Sensitivity = 0.806, Specificity = 0.775, AUC = 0.823, P < 0.001). Based on the risk factors above, a relevant nomogram prediction model was established. The model verification showed that the C-index of the modeling set was 0.814, indicating that the nomogram model had good predicted accuracy.

CONCLUSION

Based on the risk factors above, a relevant nomogram prediction model was established. The model verification showed that the C-index of the modeling set was 0.814, indicating that the nomogram model had good predicted accuracy. The nomogram based on the risk factors above had good prediction ability, which could optimize thyroidectomy and cervical lymph node dissection and improving prognosis.

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.

Integrating BRAFV600E mutation, ultrasonic and clinicopathologic characteristics for predicting the risk of cervical central lymph node metastasis in papillary thyroid carcinoma

Background

The advantages of prophylactic central lymph node dissection (CLND) for clinically node-negative patients remained a great deal of controversies. Our research was aimed to analyze the relationship between cervical central lymph node metastasis (CLNM) and BRAF^V600E mutation, ultrasonic and clinicopathologic characterizes in papillary thyroid carcinoma (PTC).

Methods and materials

In current study, a total of 112 consecutive PTC patients who experienced thyroidectomy plus cervical central neck dissection were included in our research. All PTC were pre-operatively analyzed by ultrasonic features, including tumor size, multifocality or not, tumor location, internal components, echogenicity, microcalcification, margins, orientation, taller than wide shape, and internal vascularity. The presence of clinicopathologic factors, including age, sex, T stage, Hashimoto’s thyroiditis, and BRAF^V600E mutation was then investigated. Univariate and multivariate analysis were conducted to check into the relationship between predictive factors and cervical CLNM in PTC patients, and then a predictive model was also established.

Results

Pathologically, 58.0% (65/112) of the PTC patients harbored cervical CLNM. Univariate and multivariate analysis were conducted to identify age < 55 years, tumor size > 10 mm, microcalcification, non-concomitant Hashimoto’s thyroiditis and BRAF^V600E mutation were predictive factors for cervical CLNM in PTC. The risk score for cervical CLNM in PTC patients was calculated: risk score = 1.284 × (if age < 55 years) + 1.241 × (if tumor size > 10 mm) + 1.143 × (if microcalcification) – 2.097 × (if concomitant Hashimoto’s thyroiditis) + 1.628 × (if BRAF^V600E mutation).

Conclusion

Age < 55 years old, PTC > 10 mm, microcalcification, non-concomitant Hashimoto’s thyroiditis and BRAF^V600E mutation are predictive factors for cervical CLNM. BRAF^V600E mutation by pre-operative US-FNA technology synergized with clinicopathologic and ultrasonic features is expected to guide the appropriate surgical management for PTC patients.

Real-Time Elastography: A Web-Based Nomogram Improves the Preoperative Prediction of Central Lymph Node Metastasis in cN0 PTC

BACKGROUND

Given the difficulty of accurately determining the central lymph node metastasis (CLNM) status of patients with clinically node-negative (cN0) papillary thyroid carcinoma (PTC) before surgery, this study aims to combine real-time elastography (RTE) and conventional ultrasound (US) features with clinical features. The information is combined to construct and verify the nomogram to foresee the risk of CLNM in patients with cN0 PTC and to develop a network-based nomogram.

METHODS

From January 2018 to February 2020, 1,157 consecutive cases of cN0 PTC after thyroidectomy and central compartment neck dissection were retrospectively analyzed. The patients were indiscriminately allocated (2:1) to a training cohort (771 patients) and validation cohort (386 patients). Multivariate logistic regression analysis of US characteristics and clinical information in the training cohort was performed to screen for CLNM risk predictors. RTE data were included to construct prediction model 1 but were excluded when constructing model 2. DeLong's test was used to select a forecast model with better receiver operator characteristic curve performance to establish a web-based nomogram. The clinical applicability, discrimination, and calibration of the preferable prediction model were assessed.

RESULTS

Multivariate regression analysis showed that age, sex, tumor size, bilateral tumors, the number of tumor contacting surfaces, chronic lymphocytic thyroiditis, and RTE were risk predictors of CLNM in cN0 PTC patients, which constituted prediction model 1. Model 2 included the first six risk predictors. Comparison of the areas under the curves of the two models showed that model 1 had better prediction performance (training set 0.798 vs. 0.733, validation set 0.792 vs. 0.715, p < 0.001) and good discrimination and calibration. RTE contributed significantly to the performance of the prediction model. Decision curve analysis showed that patients could obtain good net benefits with the application of model 1.

CONCLUSION

A noninvasive web-based nomogram combining US characteristics and clinical risk factors was developed in the research. RTE could improve the prediction accuracy of the model. The dynamic nomogram has good performance in predicting the probability of CLNM in cN0 PTC patients.

Predicting papillary thyroid carcinoma cervical lymph node metastases: an algorithm using the American College of Radiology Thyroid Imaging, Reporting and Data System

BACKGROUND

It is important to predict lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) preoperatively; however, the relationship between the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) score and cervical LNM remains unclear.

PURPOSE

To evaluate the association between the ACR TI-RADS score and cervical LNM in patients with PTC.

MATERIAL AND METHODS

This retrospective study consisted of 474 patients with 548 PTCs. Cervical LNM including central LNM (CLNM) and lateral LNM (LLNM) were confirmed by pathology. Univariate and multivariate analyses were performed to investigate the risk factors of CLNM and LLNM.

RESULTS

Multivariate logistic regression analyses indicated that younger age and multifocality were risk factors for CLNM in PTCs with TR5. In addition, younger age, larger tumor size, and Hashimoto's thyroiditis (HT) were risk factors for LLNM in PTCs ≥ 10 mm with TR5. In PTCs with TR4, ACR TI-RADS scores 5-6 conferred risks for LNM. In PTCs ≥ 10 mm with TR5, ACR TI-RADS scores ≥9 were risk factors for LLNM.

CONCLUSION

A higher ACR TI-RADS score is a predictor for cervical LNM in PTCs with TR4 and PTCs ≥ 10 mm with TR5.

Prediction Model of Pathologic Central Lymph Node Negativity in cN0 Papillary Thyroid Carcinoma

Background

Most patients with papillary thyroid carcinoma (PTC) have an excellent prognosis. Although central lymph node invasion is frequent, management via central lymph node dissection (CLND) remains controversial. The present study retrospectively investigated independent predictors of pathologic central lymph node negativity (pCLN-) and established a prediction model for pCLN- in clinical lymph node negativity (cN0) PTC.

Methods

A total of 2,687 patients underwent thyroid surgery for cN0 PTC from 2013 to 2018 at the First Affiliated Hospital of Chongqing Medical University, and lobectomy plus ipsilateral CLND was the basic surgical extent. Clinicopathological characteristics were reviewed and analyzed. Univariate and multivariate analyses were performed to identify factors related to pCLN-. A prediction model was established based on the results of multivariate analyses.

Results

The pCLN- rate was 51.5% (1,383/2,687). Multivariate analysis revealed that sex, age, thyroid stimulating hormone (TSH), size, location, laterality, unifocality and extrathyroidal extension negativity (ETE-) were independent predictors of pCLN-. The nomogram showed good discriminative ability (C-index: 0.784 and 0.787 in derivation and validation groups, respectively) and was well calibrated. We quantified the clinical usefulness of the nomogram by decision curve analysis. The median length of follow-up was 30 (range 12– 83) months, and 190 cases were lost, with a follow-up rate of 92.9% (2,497/2,687). Of the 2,687 patients included, 21 (0.8%) experienced recurrence.

Conclusion

This nomogram, which integrates available preoperative clinicopathological features and intraoperative frozen biopsy outcomes, is a reliable tool with high accuracy to predict pCLN- in cN0 PTC.

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.

Ultrasound and Contrast-Enhanced Ultrasound Characteristics Associated With cN1 and Microscopic pN1 in Papillary Thyroid Carcinoma

OBJECTIVES

Lymph node metastases (LNMs) could be stratified into clinical N1 (cN1) and microscopic pN1 (pathological N1), which bear different biological behavior and prognosis. Our study aimed to investigate the associations between LNMs and primary tumor's US (ultrasound) and CEUS (contrast-enhanced ultrasound) characteristics based on the stratification of LNMs into cN1 and microscopic pN1 in papillary thyroid carcinoma (PTC).

METHODS

From August 2019 to May 2020, 444 consecutive PTC patients who underwent preoperative neck US and CEUS evaluation were included. According to regional lymph node status, the patients were classified into cN1 group versus cN0 (clinical N0) group and microscopic pN1 group versus pN0 (pathological N0) group. For multiple PTCs, the largest one was selected for the evaluation of US, CEUS and clinical features. Univariate and multivariate analyses were performed to determine independent predictors of cN1 and microscopic pN1.

RESULTS

85 cN1 versus 359 cN0 patients and 117 microscopic pN1 versus 242 pN0 patients were analyzed. Multivariate logistic regression analysis showed that <55-years-old (OR: 2.56 (1.08-6.04), male [OR: 2.18 (1.22-3.91)], large size [OR: 2.59 (1.71-3.92)], calcification [OR: 3.88 (1.58-9.51)], and hyper-enhancement [OR: 2.78 (1.22-6.30)] were independent risk factors of cN1, while <55-years-old [OR: 1.91 (1.04-3.51)], large size [OR: 1.56 (1.003-2.42)], multifocality [OR: 1.67 (1.04-2.66)] were independent risk factors of microscopic pN1.

CONCLUSIONS

For patients with PTC, young age, male, large size, calcification, and hyper-enhancement were independent predictors of cN1, while young age, large size and multifocality were independent predictors of microscopic pN1.