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A Prediction Model for Contralateral Central Neck Lymph Node Metastases in Unilateral Papillary Thyroid Cancer.

ABSTRACT: The health problems caused by the frequent relapse of papillary thyroid carcinoma (PTC) remain a worldwide concern since the morbidity rate of PTC ranks the highest among thyroid cancers. Residues from contralateral central lymph node metastases (con-CLNM) are the key reason for persistence or recurrence of unilateral papillary thyroid carcinoma (uni-PTC); however, the ability to assess the status of con-CLNM in uni-PTC patients is limited. To clarify the risk factors of con-CLNM, a total of 250 patients with uni-PTC who underwent total thyroidectomy and bilateral central lymph node dissection were recruited in this study. We compared the clinical, sonographic, and pathological characteristics of patients with con-CLNM to those without con-CLNM and established a nomogram for con-CLNM in uni-PTC. We found that male sex, without Hashimoto's thyroiditis, present capsular invasion, with ipsilateral lateral lymph node metastases, and the ratio of ipsilateral central lymph node metastases ≥0.16 were independent con-CLNM predictors of uni-PTC (ORs: 2.797, 0.430, 2.538, 2.202, and 26.588; 95% CIs: 1.182-6.617, 0.211-0.876, 1.223-5.267, 1.064-4.557, and 7.596-93.069, respectively). Additionally, a preoperative nomogram for the prediction of con-CLNM based on these risk factors showed good discrimination (C-index 0.881; 95% CI: 0.840-0.923; sensitivity 85.3%; specificity 76.0%) and good agreement via the calibration plot. Our study provided a way to quantitatively and accurately predict whether con-CLNM occurred in patients with uni-PTC, which may guide surgeons to evaluate the nodal status and perform tailored therapeutic central lymph node dissection.

SUBMITTER: Tan HL

PROVIDER: S-EPMC8263281 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Predicting factors of central lymph node metastases in patients with unilateral multifocal papillary thyroid microcarcinoma.

Project description:Background:Unilateral multifocal papillary thyroid microcarcinoma (UM-PTMC) is not rare, and the suitable extent of operation is controversial. Total thyroidectomy is associated with a high risk of hypoparathyroidism and recurrent laryngeal nerve injury. Hemithyroidectomy should be considered in patients without lymph node metastases. This study aimed to identify factors predicting central lymph node metastases (CLNM) in patients with UM-PTMC. Methods:A total of 1,453 patients with thyroid cancer underwent surgery at our hospital from January 2018 to June 2018, and 115 patients with UM-PTMC were selected and assessed retrospectively. The relationship between CLNM and clinicopathologic features of UM-PTMC were analyzed by both univariate and multivariate analyses. Results:Of the 115 patients with UM-PTMC, CLNM were confirmed by pathology in 57 patients. Univariate and multivariate analyses identified the sum diameter of all tumors (?1.0 cm) [odds ratio (OR) =2.295; 95% confidence interval (CI): 1.036-5.086; P=0.041] and positive CLNM via ultrasonography (OR =5.882; 95% CI: 1.562-22.153; P=0.009) as independent predictors of CLNM. Conclusions:CLNM are common in patients with UM-PTMC. The sum diameter of all tumors (?1.0 cm) and positive CLNM via ultrasonography are independent risk factors for CLNM. Our data should be considered in the decision process relating to performing total thyroidectomy or hemithyroidectomy in patients with UM-PTMC.

| S-EPMC7347816 | biostudies-literature

A preoperative nomogram for the prediction of ipsilateral central compartment lymph node metastases in papillary thyroid cancer.

Project description:BACKGROUND: Central compartment lymph node metastases in papillary thyroid carcinoma (PTC) are difficult to detect preoperatively, and the role of routine or prophylactic central compartment lymph node dissection (CLND) in managing PTC remains controversial. The aim of this project was to create a nomogram able to predict the occurrence of central compartment lymph node metastasis using readily available preoperative clinical characteristics. METHODS: Records from patients undergoing total thyroidectomy and lymph node dissection for PTC in the period 1968-2012 were analyzed. Nodal status was based on results of serial hematoxylin and eosin (H&E) examination. Age, sex, tumor size, tumor site, and multifocality were included in a multivariable logistic regression model to predict lymph node metastasis. A coefficient-based nomogram was developed and validated using an external patient cohort. RESULTS: The study population included 914 patients (80% females) with an average central compartment nodal yield of eight per patient. Central compartment lymph node metastases were present in 390 patients (42.7%). The variables with the strongest predictive value were age (p<0.001), male sex (p<0.001), increasing tumor size (p<0.001), and tumor multifocality (p<0.05). The nomogram had good discrimination with a concordance index of 76.4% [95% confidence interval 73.3-79.4], supported by an external validation point estimate of 61.5% [95% confidence interval 49.5-73.6]. An online calculator and smartphone application were developed for point of care use. CONCLUSIONS: A validated nomogram utilizing readily available preoperative variables has been developed to give a predicted probability of central lymph node metastases in patients presenting with PTC. This nomogram may help guide surgical decision making in PTC.

| S-EPMC3993080 | biostudies-other

Risk Factors and a Prediction Model of Lateral Lymph Node Metastasis in CN0 Papillary Thyroid Carcinoma Patients With 1-2 Central Lymph Node Metastases.

Project description:BackgroundPapillary thyroid cancer (PTC) in clinically lymph node-negative (cN0) patients is prone toward lymph node metastasis. As a risk factor for tumor persistence and local recurrence, lateral lymph node metastasis (LLNM) is related to the number of central lymph node metastases (CLNMs).MethodsWe performed LLNM risk stratification based on the number of CLNMs for cN0 PTC patients who underwent thyroidectomy and lymph node dissection between January 2013 and December 2018. A retrospective analysis was applied to the 274 collected patients with 1-2 CLNMs. We examined the clinicopathological characteristics of the patients and constructed a LASSO model.ResultsIn the 1-2 CLNM group, tumors >10 mm located in the upper region and nodular goiters were independent risk factors for LLNM. Specifically, tumors >20 mm and located in the upper region contributed to metastasis risk at level II. Hashimoto's thyroiditis reduced this risk (p = 0.045, OR = 0.280). Age ≤ 30 years and calcification (microcalcification within thyroid nodules) correlated with LLNM. The LASSO model divided the population into low- (25.74%) and high-risk (57.25%) groups for LLNM, with an AUC of 0.715.ConclusionsFor patients with 1-2 CLNMs, young age, calcification, nodular goiter, tumor >10 mm, and tumor in the upper region should alert clinicians to considering a higher occult LLNM burden. Close follow-up and therapy adjustment may be warranted for high-risk patients.

| S-EPMC8555630 | biostudies-literature

Clinicopathologic predictors of central lymph node metastases in clinical node-negative papillary thyroid microcarcinoma: a systematic review and meta-analysis.

Project description:BackgroundThe presence of central lymph node metastases (CLNM) has been suggested as a risk factor for poorer prognosis and recurrence in papillary thyroid microcarcinoma (PTMC). However, the clinicopathologic factors for CLNM in clinical node-negative (CN0) PTMC were not well defined. This study aimed to perform a systematic review and meta-analysis to investigate the significant clinicopathologic predictors of CLNM in CN0 PTMC.MethodsA systematic literature search was performed in PubMed, Embase, Cochrane Library, and Web of Science. Case-control studies on the association of clinicopathologic risk factors with CLNM in CN0 PTMC were included.ResultsThirteen eligible studies involving 6068 patients with CN0 PTMC were included. From the pooled analyses, male (odds ratio [OR]: 2.07, 95% CI: 1.49-2.87, P < 0.001), multifocality (OR: 1.88, 95% CI: 1.54-2.29, P < 0.001), tumor size > 5 mm (OR: 1.84, 95% CI: 1.55-2.18, P < 0.001), and extrathyroidal extension (OR: 1.96, 95% CI: 1.30-2.95, P = 0.001) are significantly associated with increased risk of CLNM in CN0 PTMC. A sample size with a cutoff point of 200 was identified as the source of heterogeneity for sex according to meta-regression (t = 3.18, P = 0.033). Then, the subgroup analysis of male was performed, which illustrated that male increased the risk of CLNM in the small sample group (SG) and the large sample group (LG) by 6.11-folds and 2.01-folds, respectively (SG: OR, 6.11, 95% CI, 3.16-11.81, P < 0.001; LG: OR, 2.01, 95% CI, 1.65-2.46, P < 0.001).ConclusionsMale, multifocality, tumor size > 5 mm, and extrathyroidal extension may be reliable clinical predictors of CLNM in CN0 PTMC. Moreover, prophylactic central lymph node dissection should be considered in surgical decision-making for CN0 PTMC patients, who are male, multifocal, with tumor size > 5 mm, and with extrathyroidal extension.Trial registrationCRD42021242211 (PROSPERO).

| S-EPMC8976349 | biostudies-literature

Establishing a prediction model for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma.

Project description:This study aimed to establish a model for predicting lateral neck lymph node metastasis in patients with papillary thyroid carcinoma. A total of 106 patients with papillary thyroid carcinoma undergoing initial treatment of neck lymph node dissection (including central and lateral neck lymph nodes) at the thyroid surgery department were enrolled from January 2009 to April 2017. Logistic regression analysis was used to screen the factors influencing lateral neck lymph node metastasis and develop a prediction model. The receiver operating characteristic curve was used to evaluate the predictive power and boundary value of the model for lateral neck lymph node metastasis. Prediction model: Logistic(P) = -5.699 + 0.681 × (TSH) + 0.342 × (Metastatic rate of central lymph nodes) + 1.463 × (Combined with Hashimoto's disease) + 1.525 × (Number of tumors). When logistic (P) was ≥ 0.821, it was predicted that lateral neck lymph node metastasis occurred in patients with papillary thyroid carcinoma. When logistic (P) was <0.821, it was predicted that no metastasis was found in the lateral neck lymph node. The prediction accuracy was 78.3%. The model helped in evaluating lateral neck lymph node metastasis in patients with papillary thyroid carcinoma. Also, it had significance in determining reasonable surgical range, reducing unnecessary lateral neck lymph node dissection, and further improving the quality of life of patients.

| S-EPMC6255803 | biostudies-literature

The Number of Central Lymph Nodes on Preoperative Ultrasound Predicts Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma: A Prospective Cohort Study.

Project description:To evaluate the effectiveness of the number of central compartment lymph nodes (CLNs) on ultrasound (US) in predicting CLN metastasis (CLNM). We prospectively studied 309 papillary thyroid cancer (PTC) patients who underwent thyroidectomy with CLN dissection at our center from May 2017 to July 2017. The number and features of CLNs were evaluated preoperatively via US. All US examinations were performed using a Philips iU 22 or a GE Logiq 9 machine. Correlations between CLNs observed via preoperative US and amount of CLNM were evaluated. We found that ?2 CLNs on the preoperative US was associated with CLNM (P < 0.01). For this feature, the sensitivity, specificity, and area under the curve (AUC) were 54.3%, 66.1%, and 0.61, respectively. The presence of both suspected metastasis and ?2 CLNs on US had a specificity of 86.5%. In addition, ?3 CLNs on preoperative US was associated with large-volume CLNM (>5 metastatic CLNs) (P < 0.01). For this feature, the sensitivity, specificity and AUC were 54.8%, 74.5% and 0.65, respectively. The presence of both suspected metastasis and ?3 CLNs on US had a specificity of 84.9%. The presence of suspected metastasis and/or ?3 CLNs had a sensitivity of 80.6%. Our results suggest that ?2 and???3 CLNs on preoperative US may serve as ancillary preoperative markers for predicting CLNM.

| S-EPMC7178523 | biostudies-literature

Conventional US, elastography, and contrast enhanced US features of papillary thyroid microcarcinoma predict central compartment lymph node metastases.

Project description:Lymph node metastases at the time of diagnosis have a major impact on both therapeutic strategy and tumor recurrence for patients with papillary thyroid microcarcinoma (PTMC). Our objective was to evaluate the usefulness of PTMC characteristics on ultrasonography for predicting central compartment lymph node metastases (CCLNM) of PTMC. One hundred twenty seven patients who underwent surgery for PTMC were enrolled in this study. The relationship between the CCLNM and the characteristics on conventional US, elastographic, and contrast enhanced ultrasound (CEUS) were investigated. Univariate analysis indicated that PTMCs with CCLNM were more often nodule irregular shape, microcalcifications, hyperenhancing or isoenhancing parametric maps, and peak index ≥1 at preoperative US and CEUS than those without CCLNM (P< 0.01, 0.05, 0.01 and 0.05 respectively). Multivariate analysis showed that microcalcification (OR:2.378, 95% CI: 1.096-5.158) and hyperenhancement or isoenhancement (OR:2.8, 95% CI: 1.287-6.094) were predictive for the presence of CCLNM. Elastography score was not significantly different between the groups. Our study indicated that preoperative thyroid nodule characteristics on conventional US and CEUS may serve as a useful tool to predict central compartment lymph node metastases in PTMC.

| S-EPMC4291568 | biostudies-other

Genomic and Transcriptomic Characterization of Papillary Microcarcinomas With Lateral Neck Lymph Node Metastases.

Project description:CONTEXT:Most papillary microcarcinomas (PMCs) are indolent and subclinical. However, as many as 10% can present with clinically significant nodal metastases. OBJECTIVE AND DESIGN:Characterization of the genomic and transcriptomic landscape of PMCs presenting with or without clinically important lymph node metastases. SUBJECTS AND SAMPLES:Formalin-fixed paraffin-embedded PMC samples from 40 patients with lateral neck nodal metastases (pN1b) and 71 patients with PMC with documented absence of nodal disease (pN0). OUTCOME MEASURES:To interrogate DNA alterations in 410 genes commonly mutated in cancer and test for differential gene expression using a custom NanoString panel of 248 genes selected primarily based on their association with tumor size and nodal disease in the papillary thyroid cancer TCGA project. RESULTS:The genomic landscapes of PMC with or without pN1b were similar. Mutations in TERT promoter (3%) and TP53 (1%) were exclusive to N1b cases. Transcriptomic analysis revealed differential expression of 43 genes in PMCs with pN1b compared with pN0. A random forest machine learning-based molecular classifier developed to predict regional lymph node metastasis demonstrated a negative predictive value of 0.98 and a positive predictive value of 0.72 at a prevalence of 10% pN1b disease. CONCLUSIONS:The genomic landscape of tumors with pN1b and pN0 disease was similar, whereas 43 genes selected primarily by mining the TCGA RNAseq data were differentially expressed. This bioinformatics-driven approach to the development of a custom transcriptomic assay provides a basis for a molecular classifier for pN1b risk stratification in PMC.

| S-EPMC6733494 | biostudies-literature

Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer.

Project description:BackgroundCentral lymph node metastasis (CLNM) occurs frequently in patients with papillary thyroid cancer (PTC), but performing prophylactic central lymph node dissection is still controversial. There are no reliable models for predicting CLNM. This study aimed to develop predictive models for CLNM by machine learning (ML) algorithms.MethodsPatients with PTC who underwent initial thyroid resection at our hospital between January 2018 and December 2019 were enrolled. A total of 22 variables, including clinical characteristics and ultrasonography (US) features, were used for conventional univariate and multivariate analysis and to construct ML-based models. A 5-fold cross validation strategy was used for validation and a feature selection approach was applied to identify risk factors.ResultsThe areas under the receiver operating characteristic curve (AUC) of 7 models ranged from 0.680 to 0.731. All models performed significantly better than US (AUC=0.623) in predicting CLNM (P<0.05). In decision curve, most of the models also performed better than US. The gradient boosting decision tree model with 7 variables was identified as the best model because of its best performance in both ROC (AUC=0.731) and decision curves. Based on multivariate analysis and feature selection, young age, male sex, low serum thyroid peroxidase antibody and US features such as suspected lymph nodes, microcalcification and tumor size > 1.1 cm were the most contributing predictors for CLNM.ConclusionsIt is feasible to develop predictive models of CLNM in PTC patients by incorporating clinical characteristics and US features. The ML algorithm may be a useful tool for the prediction of lymph node metastasis in thyroid cancer.

| S-EPMC7609926 | biostudies-literature

A Preoperative Nomogram for the Prediction of High-Volume Central Lymph Node Metastasis in Papillary Thyroid Carcinoma.

Project description:BackgroundHigh-volume lymph node metastasis (HVLNM, equal to or more than 5 lymph nodes) is one of the adverse features indicating high recurrence risk in papillary thyroid carcinoma (PTC) and is recommended as one of the indications of completion thyroidectomy for patients undergoing thyroid lobectomy at first. In this study, we aim to develop a preoperative nomogram for the prediction of HVLNMs in the central compartment in PTC (cT1-2N0M0), where preoperative imaging techniques perform poor.MethodsFrom October 2016 to April 2021, 423 patients were included, who were diagnosed as PTC (cT1-2N0M0) and underwent total thyroidectomy and prophylactic central compartment neck dissection in our center. Demographic and clinicopathological features were recorded and analyzed using univariate and multivariate logistic regression analysis. A nomogram was developed based on multivariate logistic regression analysis.ResultsAmong the included patients, 13.4% (57 cases) were found to have HVLNMs in the central compartment. Univariate and multivariate logistic regression analysis showed that age (</=35 years vs. >35 years), BRAF with V600E mutated, nodule diameter, and calcification independently predicted HVLNMs in the central compartment. The nomogram showed good discrimination with an AUC of 0.821 (95% CI, 0.768-0.875).ConclusionThe preoperative nomogram can be used to quantify the probability of HVLNMs in the central compartment and may reduce the reoperation rate after thyroid lobectomy.

| S-EPMC8729159 | biostudies-literature

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