Risk Factors and a Prediction Model of Lateral Lymph Node Metastasis in CN0 Papillary Thyroid Carcinoma Patients With 1–2 Central Lymph Node Metastases
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ABSTRACT: Background
Papillary 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). Methods
We 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. Results
In 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. Conclusions
For 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.
SUBMITTER: Wang Y
PROVIDER: S-EPMC8555630 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundWhether prophylactic central lymph node dissection is necessary for cN0 papillary thyroid microcarcinoma (PTMC) patients remains highly debatable. Surgeons desperately need a way to help with surgical decision-making. While traditional predictive models can better explain changes in variables, machine learning (ML) models may have better predictive performance. This study aims to develop models for predicting the risk of central lymph node metastasis (CLNM) by utilizing ML algorithms.MethodsThe clinical records of 1,121 patients with cN0 PTMC who underwent initial thyroid resection at our hospital between January 2014 and December 2018 were retrospectively retrieved. Univariate and multivariate analyses were performed to examine risk factors associated with CLNM. Six ML algorithms for predicting CLNM were established and internally validated. Indices including the area under the receiver operating characteristic (AUROC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to test the performance of the model.ResultsThe results showed 33.5% (376 out of 1,121) of patients had CLNM. In multivariate logistic regression (LR) analyses, gender, age, tumor size, multifocal lesions, and extrathyroidal extension (ETE) were all independent predictors of CLNM. The AUROC predictive values of the six ML algorithms were between 0.664 and 0.794, with the random forest (RF) model performing the best with an AUROC of 0.794. Therefore, we used the RF model and uploaded the results to a web-based risk calculator to predict an individual's probability of CLNM (https://xijing-thyroid.shinyapps.io/ptmc_clnm).ConclusionsDeveloping predictive models of CLNM in cN0 PTMC patients using the ML algorithm is a feasible method. Our online risk calculator based on the RF model may be a useful tool for surgical decisions.
| S-EPMC9469161 | biostudies-literature
Project description:BackgroundCentral lymph node metastasis (CLNM) is common in papillary thyroid carcinoma (PTC). Prophylactic central lymph node dissection (PCLND) for patients with clinically negative central compartment lymph nodes (CN0) remains controversial. The phrase "clinically negative" is used to indicate that patients exhibited no clinical evidence of CLNM by ultrasonography (US) or computerized tomography (CT) preoperatively. In this study, we analyze the risk factors for CLNM in CN0 patients.MethodsThe PUBMED and SCIE databases were systematically searched for works published through January 31, 2015. All of the patients included in this study underwent thyroidectomy+PCLND. Revman 5.3 software was used to analyze the data.ResultsTwenty studies and 9084 patients were included in this meta-analysis. The following variables were associated with an increased risk of CLNM in CN0 patients: age < 45 years (OR = 1.59, 95% CI = 1.42-1.78, p<0.00001), male sex (OR = 1.95, 95% CI = 1.63-2.32, p<0.00001), multifocality (OR = 1.43, 95% CI = 1.22-1.67, p<0.00001), tumor size > 2 cm for PTC patients (OR = 2.98, 95% CI 2.08-4.28, p<0.00001) or tumor size > 0.5 cm for papillary thyroid microcarcinoma (PTMC) patients (OR = 2.30, 95% CI = 1.71-3.09, p<0.00001), location of the primary tumor in the central area and low pole (OR = 1.86, 95% CI = 1.48-2.33, p<0.00001), lymphovascular invasion (OR = 4.35, 95% CI = 2.24-8.46, p<0.0001), extrathyroidal extension (OR = 2.27, 95% CI = 1.76-2.94, p<0.00001), and capsular invasion (OR = 1.72, 95% CI = 1.39-2.41, p<0.00001). PTC (tumor size > 1 cm) exhibited a higher risk factor associated with CLNM than PTMC (tumor size < 1 cm) (OR = 2.83, 95% CI = 2.15-3.72, p<0.00001). Bilateral tumors (OR = 1.21, 95% CI = 0.92-1.58, p = 0.17) and lymphocytic thyroiditis (OR = 0.88, 95% CI = 0.71-1.09, p = 0.25) had no association with CLNM in CN0 patients.ConclusionsOur systematic review identified several clinical features associated with CLNM in CN0 patients, including age, sex, multifocality, size, location, lymphovascular invasion, capsular invasion, and extrathyroidal extension. These factors should guide the application of PCLND in CN0 patients.
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Project description:PURPOSE:Preoperative neck ultrasound (US) for lateral cervical lymph nodes is recommended for all patients undergoing thyroidectomy for thyroid malignancy, but it is operator dependent. We aimed to develop a radiomics signature using US images of the primary tumor to preoperatively predict lateral lymph node metastasis (LNM) in patients with conventional papillary thyroid carcinoma (cPTC). METHODS:Four hundred consecutive cPTC patients from January 2004 to February 2006 were enrolled as the training cohort, and 368 consecutive cPTC patients from March 2006 to February 2007 served as the validation cohort. A radiomics signature, which consisted of 14 selected features, was generated by the least absolute shrinkage and selection operator (LASSO) regression model in the training cohort. The discriminating performance of the radiomics signature was assessed in the validation cohort with the area under the receiver operating characteristic curve (AUC). RESULTS:The radiomics signature was significantly associated with lateral cervical lymph node status (p < 0.001). The AUC of its performance in discriminating metastatic and non-metastatic lateral cervical lymph nodes was 0.710 (95% CI: 0.649-0.770) in the training cohort and was 0.621 (95% CI: 0.560-0.682) in the validation cohort. CONCLUSIONS:The present study showed that US radiomic features of the primary tumor were associated with lateral cervical lymph node status. Although their discriminatory performance was slightly lower in the validation cohort, our study shows that US radiomic features of the primary tumor alone have the potential to predict lateral LNM.
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Project description: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.
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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.
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Project description:Objective To develop a web-based machine learning server to predict lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) patients. Methods Clinical data for PTC patients who underwent primary thyroidectomy at our hospital between January 2015 and December 2020, with pathologically confirmed presence or absence of any LLNM finding, were retrospectively reviewed. We built all models from a training set (80%) and assessed them in a test set (20%), using algorithms including decision tree, XGBoost, random forest, support vector machine, neural network, and K-nearest neighbor algorithm. Their performance was measured against a previously established nomogram using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), precision, recall, accuracy, F1 score, specificity, and sensitivity. Interpretable machine learning was used for identifying potential relationships between variables and LLNM, and a web-based tool was created for use by clinicians. Results A total of 1135 (62.53%) out of 1815 PTC patients enrolled in this study experienced LLNM episodes. In predicting LLNM, the best algorithm was random forest. In determining feature importance, the AUC reached 0.80, with an accuracy of 0.74, sensitivity of 0.89, and F1 score of 0.81. In addition, DCA showed that random forest held a higher clinical net benefit. Random forest identified tumor size, lymph node microcalcification, age, lymph node size, and tumor location as the most influentials in predicting LLNM. And the website tool is freely accessible at http://43.138.62.202/. Conclusion The results showed that machine learning can be used to enable accurate prediction for LLNM in PTC patients, and that the web tool allowed for LLNM risk assessment at the individual level.
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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.
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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
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.
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Project description:PurposeTreatment protocols for occult central lymph node metastasis (LNM) associated with papillary thyroid cancer (PTC) located in the isthmus are debatable. We aimed to analyze the pattern of occult central LNM in isthmic PTC, including risk factors for bilateral paratracheal LNM.Patients and MethodsConsecutive patients with PTC were recruited to this study. All patients underwent total thyroidectomy and prophylactic bilateral central neck dissection. The clinicopathologic features and distribution of central LNM were compared between the two groups, and risk factors for bilateral paratracheal LNM were analyzed.ResultsA total of 174 patients with PTC were enrolled in this study, of whom 87 patients had isthmic PTC (study group) and 87 patients had lobe-originating PTC (control group). The two groups had comparable demographics and tumor features. There were higher frequencies of pretracheal LNM (P =0.001) and bilateral paratracheal LNM (P = 0.002) in the isthmic PTC group. Bilateral paratracheal LNM was significantly associated with age <55 years (P = 0.037), capsular invasion (P = 0.034), tumor location (isthmus) (P < 0.001), BRAF gene mutation (P = 0.013), and pretracheal LNM (P < 0.001). Isthmus location (odds ratio [OR]: 4.116, 95% confidence interval [CI]: 1.264–13.433, P = 0.019) and pretracheal LNM (OR: 3.422, 95% CI: 1.214–9.642, P = 0.020) were independent risk factors for bilateral paratracheal LNM.ConclusionBecause of its unique anatomic location, isthmic PTC differs from PTC in the lobe with respect to pretracheal and bilateral paratracheal LNM, even in patients of comparable age, sex, tumor size, extrathyroidal extension, BRAF mutation, and pathologic TNM staging. The isthmus location was found to be an independent risk factor for bilateral paratracheal LNM. This information may contribute to the development of an appropriate surgical protocol for isthmic PTC.
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