Central Lymph Node Metastasis In Papillary Thyroid Carcinoma Patients Research Articles

Value of clinical features combined with multimodal ultrasound in predicting lymph node metastasis in cervical central area of papillary thyroid carcinoma.

To explore the clinical features, multimodal ultrasound features and multimodal ultrasound imaging features in predicting lymph node metastasis in the central cervical region of papillary thyroid carcinoma. A total of 129 patients with papillary thyroid carcinoma (PTC) confirmed by pathology were selected from our hospital from September 2020 to December 2022. According to the pathological results of cervical central lymph nodes, these patients were divided into metastatic group and non-metastatic group. Patients were randomly sampled and divided into training group (n = 90) and verification group (n = 39) according to the ratio of 7:3. The independent risk factors for central lymph node metastasis (CLNM) were determined by least absolute shrinkage and selection operator and multivariate logistic regression. Based on independent risk factors to build a prediction model, select the best diagnostic effectiveness of the prediction model sketch line chart, and finally, the line chart calibration and clinical benefits were evaluated. A total of 8, 11 and 17 features were selected from conventional ultrasound images, shear wave elastography (SWE) images and contrast-enhanced ultrasound (CEUS) images to construct the Radscore of conventional ultrasound, SWE and CEUS, respectively. After univariate and multivariate logistic regression analysis, male, multifocal, encapsulation, iso-high enhancement and multimodal ultrasound imaging score were independent risk factors for cervical CLNM in PTC patients (p < 0.05). Based on independent risk factors, a clinical combined with multimodal ultrasound feature model was constructed, and multimodal ultrasound Radscore were added to the clinical combined with multimodal ultrasound feature model to form a joint prediction model. In the training group, the diagnostic efficacy of combined model (AUC = 0.934) was better than that of clinical combined with multimodal ultrasound feature model (AUC = 0.841) and multimodal ultrasound radiomics model (AUC = 0.829). In training group and validation group, calibration curves show that the joint model has good predictive ability for cervical CLNM of PTC patients; The decision curve shows that most of the net benefits of the nematic chart are higher than those of clinical + multimodal ultrasound feature model and multimodal ultrasound radiomics model within a reasonable risk threshold range. Male, multifocal, capsular invasion and iso-high enhancement are independent risk factors of CLNM in PTC patients, and the clinical plus multimodal ultrasound model based on these four factors has good diagnostic efficiency. The joint prediction model after adding multimodal ultrasound Radscore to clinical and multimodal ultrasound features has the best diagnostic efficiency, high sensitivity and specificity, which is expected to provide objective basis for accurately formulating individualized treatment plans and evaluating prognosis.

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

Central lymph node metastasis (CLNM) is a predictor of poor prognosis for papillary thyroid carcinoma (PTC) patients. The options for surgeon operation or follow-up depend on the state of CLNM while accurate prediction is a challenge for radiologists. The present study aimed to develop and validate an effective preoperative nomogram combining deep learning, clinical characteristics and ultrasound features for predicting CLNM. In this study, 3359 PTC patients who had undergone total thyroidectomy or thyroid lobectomy from two medical centers were enrolled. The patients were divided into three datasets for training, internal validation and external validation. We constructed an integrated nomogram combining deep learning, clinical characteristics and ultrasound features using multivariable logistic regression to predict CLNM in PTC patients. Multivariate analysis indicated that the AI model-predicted value, multiple, position, microcalcification, abutment/perimeter ratio and US-reported LN status were independent risk factors predicting CLNM. The area under the curve (AUC) for the nomogram to predict CLNM was 0.812 (95% CI, 0.794-0.830) in the training cohort, 0.809 (95% CI, 0.780-0.837) in the internal validation cohort and 0.829(95%CI, 0.785-0.872) in the external validation cohort. Based on the analysis of the decision curve, our integrated nomogram was superior to other models in terms of clinical predictive ability. Our proposed thyroid cancer lymph node metastasis nomogram shows favorable predictive value to assist surgeons in making appropriate surgical decisions in PTC treatment.

The Value of Ki-67 Labeling Index in Central Lymph Node Metastasis and Survival of Papillary Thyroid Carcinoma: Evidence From the Clinical and Molecular Analyses.

Recent evidence suggests that the Ki-67 labeling index is associated with lymph node metastasis and the prognosis of papillary thyroid carcinoma (PTC). We retrospectively evaluated the clinicopathological features of consecutive PTC patients between Jan 2019 and Oct 2020 in our medical center. The molecular analysis was also conducted by using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) program. The Chi-square test was performed for the comparison of variables between patients with central lymph node metastasis (CLNM) and not. Besides, univariate and stepwise multivariate logistic regression analyses were further used to determine the risk factors for CLNM in PTC. Our results showed that male gender (odd ratio (OR) = 3.02; 95% CI: 1.81-5.04), tumor size >1cm (OR = 2.81; 95% CI: 1.84-4.29), multifocality (OR = 2.08; 95% CI: 1.31-3.30, and Ki-67 labeling index (>3% and ≤5%: OR = 1.20; 95% CI: .73-1.97; >5%: OR = 3.85; 95% CI: 1.62-9.14) were independent risk factors for CLNM. After excluding the patients with harvested central lymph nodes <3, increased Ki-67 labeling index was still associated with the number of CLNM and the lymph node ratio. Additionally, the expression level of Ki-67 was significantly correlated with a higher N stage and worse disease-free survival in TCGA and validated GSE60542 datasets. Higher Ki-67 labeling index (>5%) is significantly associated with the CLNM in PTC patients, like other indicators of the male gender, larger tumor size, and multifocality. Besides, the Ki-67 was also determined to be associated with CLNM and DFS in PTC patients, which may act as an important molecular marker in PTC.

Nomogram model based on preoperative serum thyroglobulin and clinical characteristics of papillary thyroid carcinoma to predict cervical lymph node metastasis

ObjectivePreoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) has been one of the serious clinical challenges. The present study aims at understanding the relationship between preoperative serum thyroglobulin (PS-Tg) and LNM and intends to establish nomogram models to predict cervical LNM.MethodsThe data of 1,324 PTC patients were retrospectively collected and randomly divided into training cohort (n = 993) and validation cohort (n = 331). Univariate and multivariate logistic regression analyses were performed to determine the risk factors of central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM). The nomogram models were constructed and further evaluated by 1,000 resampling bootstrap analyses. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) of the nomogram models were carried out for the training, validation, and external validation cohorts.ResultsAnalyses revealed that age, male, maximum tumor size >1 cm, PS-Tg ≥31.650 ng/ml, extrathyroidal extension (ETE), and multifocality were the significant risk factors for CLNM in PTC patients. Similarly, such factors as maximum tumor size >1 cm, PS-Tg ≥30.175 ng/ml, CLNM positive, ETE, and multifocality were significantly related to LLNM. Two nomogram models predicting the risk of CLNM and LLNM were established with a favorable C-index of 0.801 and 0.911, respectively. Both nomogram models demonstrated good calibration and clinical benefits in the training and validation cohorts.ConclusionPS-Tg level is an independent risk factor for both CLNM and LLNM. The nomogram based on PS-Tg and other clinical characteristics are effective for predicting cervical LNM in PTC patients.

Preoperatively Predicting the Central Lymph Node Metastasis for Papillary Thyroid Cancer Patients With Hashimoto's Thyroiditis.

BackgroundThe preoperative distinguishment of lymph nodes with reactive hyperplasia or tumor metastasis plays a pivotal role in guiding the surgical extension for papillary thyroid carcinoma (PTC) with Hashimoto’s thyroiditis (HT), especially in terms of the central lymph node (CLN) dissection. We aim to identify the preparative risk factors for CLN metastasis in PTC patients concurrent with HT.Materials and MethodsWe retrospectively reviewed and analyzed the data including the basic information, preoperative sonographic characteristics, and thyroid function of consecutive PTC patients with HT in our medical center between Jan 2019 and Apr 2021. The Chi-square and Fisher’s exact tests were used for comparison of qualitative variables among patients with or without CLN metastasis. Univariate and multivariate logistic regression analyses were used to determine the risk factors for CLN metastasis. The nomogram was constructed and further evaluated by two cohorts produced by 1,000 resampling bootstrap analysis.ResultsA total of 98 in 214 (45.8%) PTC patients were identified with CLN metastasis. In multivariate analysis, four variables including high serum thyroglobulin antibody (TgAb) level (>1,150 IU/ml), lower tumor location, irregular margin of CLN, and micro-calcification in the CLN were determined to be significantly associated with the CLN metastasis in PTC patients with HT. An individualized nomogram was consequently established with a favorable C-index of 0.815 and verified via two internal validation cohorts.ConclusionsOur results indicated that preoperatively sonographic characteristics of the tumor and lymph node condition combined with serum TgAb level can significantly predict the CLN in PTC patients with HT and the novel nomogram may further help surgeons to manage the CLN in this subpopulation.

Predicting factors of central lymph node metastasis and BRAFV600E mutation in Chinese population with papillary thyroid carcinoma

ObjectiveThe aim of this study was to evaluate the predictive factors of central lymph node metastasis (CLNM) and BRAFV600E mutation in Chinese patients with papillary thyroid carcinoma (PTC).MethodsA total of 943 PTC patients who underwent thyroidectomy from 2014 to 2016 at our hospital were enrolled. Those patients were divided into PTC > 10 mm and papillary thyroid microcarcinoma (PTMC) groups by tumor size. The BRAFV600E mutation was examined by quantitative real-time PCR. Univariate and multivariate analyses were used to examine risk factors associated with CLNM and the BRAFV600E mutation.ResultsThe frequency of CLNM was 53% (505/943). Both univariate and multivariate analyses suggested that the risk factors for CLNM in PTC patients were male, younger age, and larger tumor size (P < 0.05). Coexistent Hashimoto thyroiditis (HT) was an independent protective factor against CLNM when the tumor was > 10 mm (P = 0.006). Stratified analysis revealed that male, age ≤ 30 years, and tumor size > 5 mm were independent risk factors for CLNM. The BRAFV600E mutation rate was 85%. Multivariate logistic regression analysis revealed that age (P < 0.001) and coexistent HT (P = 0.005) were independent predictive factors of BRAFV600E mutation in PTC patients. Only age was a risk factor for the BRAFV600E mutation when the tumor was > 10 mm (P = 0.004). In the PTMC group, the BRAFV600E mutation was significantly correlated with tumor size (P < 0.001) and coexistent HT (P = 0.03). Stratified analysis revealed that age > 30 years and tumor size > 5 mm were independent predictive factors of BRAFV600E mutation. Furthermore, the incidence of CLNM was significantly higher in BRAFV600E mutation-positive patients (P = 0.009) when the tumor was ≤ 5 mm.ConclusionThe factors male, younger age (≤ 30 years), large tumor size (> 5 mm), and coexistent HT are independent predicative factors for CLNM. The BRAFV600E mutation is associated with both large size and without HT in PTMC patients, age > 30 years in the PTC > 10 mm group. The BRAFV600E mutation was an independent risk factor for CLNM when the tumor was ≤ 5 mm. For optimal management, these features should be comprehensively evaluated to determine the initial surgical approach for PTC patients.

A miRNA-clinicopathological nomogram for the prediction of central lymph node metastasis in papillary thyroid carcinoma-analysis from TCGA database.

It is of significance to evaluate central lymph node status in patients with papillary thyroid carcinoma (PTC), because it can decrease postoperative complications resulting from unnecessary prophylactic central lymph node dissection (CLND). Due to the low sensitivity and specificity of neck ultrasonography in the evaluation of central lymph node metastasis (CLNM), it is urgently required to find alternative biomarkers to predict CLNM in PTC patients, which is the main purpose of this study.RNA-sequencing datasets and clinical data of 506 patients with thyroid carcinoma from the Cancer Genome Atlas (TCGA) database were downloaded and analyzed to identify differentially expressed miRNAs (DEMs), which can independently predict CLNM in PTC. A nomogram predictive of CLNM was developed based on clinical characteristics and the identified miRNAs. Receiver operating characteristics curves were drawn to evaluate the predictive performance of the nomogram. Bioinformatics analyses, including target genes identification, functional enrichment analysis, and protein–protein interaction network, were performed to explore the potential roles of the identified DEMs related to CLNM in PTC.A total of 316 PTC patients were included to identify DEMs. Two hundred thirty-seven (75%) PTC patients were randomly selected from the 316 patients as a training set, while the remaining 79 (25%) patients were regarded as a testing set for validation. Two DEMs, miRNA-146b-3p (HR: 1.327, 95% CI = 1.135–1.551, P = .000) and miRNA-363–3p (HR: 0.714, 95% CI = 0.528–0.966, P = .029), were significantly associated with CLNM. A risk score based on these 2 DEMs and calculating from multivariate logistic regression analysis, was significantly lower in N0 group over N1a group in both training (N0 vs N1a: 2.04 ± 1.01 vs 2.73 ± 0.61, P = .000) and testing (N0 vs N1a: 2.20 ± 0.93 vs 2.79 ± 0.68, P = .003) sets. The nomogram including risk score, age, and extrathyroidal extension (ETE) was constructed in the training set and was then validated in the testing set, which showed better prediction value than the other three predictors (risk score, age, and ETE) in terms of CLNM identification. Bioinformatics analyses revealed that 5 hub genes, SLC6A1, SYT1, COL19A1, RIMS2, and COL1A2, might involve in pathways including extracellular matrix organization, ion transmembrane transporter activity, axon guidance, and ABC transporters.On the basis of this study, the nomogram including risk score, age, and ETE showed good prediction of CLNM in PTC, which has a potential to facilitate individualized decision for surgical plans.

Nomogram for Predicting Central Lymph Node Metastasis in Papillary Thyroid Cancer: A Retrospective Cohort Study of Two Clinical Centers.

PurposeCentral lymph node metastasis (CNM) are highly prevalent but hard to detect preoperatively in papillary thyroid carcinoma (PTC) patients, while the significance of prophylactic compartment central lymph node dissection (CLND) remains controversial as a treatment option. We aim to establish a nomogram assessing risks of CNM in PTC patients, and explore whether prophylactic CLND should be recommended.Materials and MethodsOne thousand four hundred thirty-eight patients from two clinical centers that underwent thyroidectomy with CLND for PTC within the period 2016–2019 were retrospectively analyzed. Univariate and multivariate analysis were performed to examine risk factors associated with CNM. A nomogram for predicting CNM was established, thereafter internally and externally validated.ResultsSeven variables were found to be significantly associated with CNM and were used to construct the model. These were as follows: thyroid capsular invasion, multifocality, creatinine > 70 μmol/L, age < 40, tumor size > 1 cm, body mass index < 22, and carcinoembryonic antigen > 1 ng/mL. The nomogram had good discrimination with a concordance index of 0.854 (95% confidence interval [CI], 0.843 to 0.867), supported by an external validation point estimate of 0.825 (95% CI, 0.793 to 0.857). A decision curve analysis was made to evaluate nomogram and ultrasonography for predicting CNM.ConclusionA validated nomogram utilizing readily available preoperative variables was developed to predict the probability of central lymph node metastases in patients presenting with PTC. This nomogram may help surgeons make appropriate surgical decisions in the management of PTC, especially in terms of whether prophylactic CLND is warranted.

Predictive Risk-scoring Model For Central Lymph Node Metastasis and Predictors of Recurrence in Papillary Thyroid Carcinoma

There are about half of papillary thyroid carcinoma (PTC) patients with the experience of central lymph node metastasis (CLNM), while the model to predict high-risk groups of CLNM from PTC patients is uncertain. The aim of this study was to evaluate candidate risk factors of CLNM and identify risk factors of recurrence to guide the postoperative therapeutic decision and follow-up for physicians and patients.A total of 4107 patients(4884 lesions) who underwent lymph node dissection in two hospitals from 2005 to 2014 were evaluated. CLNM risk was stratified and a risk-scoring model was developed on the basis of the identified independent risk factors for CLNM. Cox’s proportional hazards regression model was used to investigate the risk factors for recurrence.CLNM was proved in 37.96% (1559/4107) of patients and 33.96% (1659/4884) of lesions. In the multivariate analysis, Male, Age ≤35 years, Tumor size >0.5 cm,Lobe dissemination (+), Psammoma body (+), Multifocality and Capsule invasion (+) were independent risk predictors of CLNM (P < 0.01). A 14-point risk-scoring model was built to predict the stratified CLNM in PTC patients and the area under receiver operating characteristic curve of the model for the prediction of CLNM was 0.672 (95% CI: 0.656–0.688) (P < 0.01). COX regression model showed that Tumor size >0.5 cm, Lobe dissemination (+), Multifocality and CLNM were significant risk factors associated with poor outcomes. The research suggested that prophylactic CLN dissection could be performed in patients with total score ≥4 according to the risk-scoring model, and more aggressive treatment and more frequent follow-up should be considered for patients with Tumor size >0.5 cm, Lobe dissemination (+), Multifocality and CLNM.

The Clinicopathological Features of Papillary Thyroid Carcinoma Patients with Positive Hepatitis B Surface Antigen

Background: Hepatitis B virus infection has been reported to be associated with some kinds of cancer. The aim of this study was to investigate clinicopathological features of papillary thyroid carcinoma (PTC) patients with positive hepatitis B surface antigen (HBsAg). Materials and Methods: A total of 569 PTC patients were analyzed retrospectively in this study. The relationships of HBsAg with clinicopathologic features of PTC were analyzed by univariate analysis. The relationships of central lymph node metastasis (CLNM) with clinicopathological features of PTC were analyzed by univariate and multivariate logistic regression analysis. Results: The incidence of CLNM in PTC with positive HBsAg was higher than that in PTC with negative HBsAg (71.4 vs. 60.0%, p = 0.047). Univariate analysis showed that positive HBsAg was significantly associated with bilateral tumors (p = 0.043) and lymph node metastasis (LNM) (p = 0.047) in PTC patients. In addition, the incidence of CLNM in patients with PTC was 61.7% (351 of 569 cases). Univariate analysis showed that CLNM of patients with PTC was significantly associated with positive HBsAg (p = 0.047). Multivariate logistic regression analysis revealed that positive HBsAg (p = 0.038) was an independent predictor of CLNM in patients with PTC. Conclusions: The incidence of CLNM in PTC patients with positive HBsAg was significantly higher than that of patients with negative HBsAg. Positive HBsAg was correlated with LNM and bilateral tumors. In addition to gender, age <45 years, tumor size >2 cm, and lateral LNM, positive HBsAg was also an independent predictor of CLNM in PTC patients.