&lt;p&gt;Analysis of Risk Factors for Lymph Node Metastases in Elderly Patients with Papillary Thyroid Micro-Carcinoma&lt;/p&gt;

Fu, Guiming; Wang, Zhaohui; Chen, Yibo; Li, Chunhua; Zhang, Yue-Jia; Li, Xiaojing; Wan, Quan-Xin

doi:10.2147/cmar.s248374

Cited by 5 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, age was an important predictive factor, especially in patients below 45 years. Previous studies have determined age (< 45 years) as an independent risk factor [ 34 , 36 ], and we found similar results in this study. Jiwang et al [ 8 ] found that CLNM within PTMC patients was higher among males and younger individuals.…”

Section: Discussionsupporting

confidence: 91%

“…In this study, age (< 45 years), sex, tumor size, multifocality, capsular invasion, and HT to be the risk factors that independently predicted CLNM among PTMC cases, which was similar to the risk factors determined in previous studies [ 8 , 34 – 36 ]. However, the significant CLNM predictive risk factors among PTMC were dissimilar partially.…”

Section: Discussionsupporting

confidence: 87%

“…Some authors reported multifocality as the the risk factor to independently predict CLNM among cN0 PTMC cases [ 8 , 35 , 43 ]. Additionally, Xue et al [ 36 ] proposed that multiple cancer foci were independent risk factor for lateral lymph node metastasis (LLNM) in patients with PTMC.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

A novel tool for predicting the risk of central lymph node metastasis in patients with papillary thyroid microcarcinoma: a retrospective cohort study

Luo

Gao

et al. 2022

BMC Cancer

View full text Add to dashboard Cite

Introduction Central lymph node status in papillary thyroid microcarcinoma (PTMC) plays an important role in treatment decision-making clinically, however, it is not easy to predict central lymph node metastasis (CLNM). The present work focused on finding the more rational alternative for evaluating central lymph node status while identifying influencing factors to construct a model to predict CLNM incidence. Methods In this study, we retrospectively analyzed the typical sonographic and clinicopathologic features of 546 PTMC patients who underwent surgery, among which, the data of 382 patients were recruited in the training cohort and that of 164 patients in the validation cohort. Based on the outcome of the training cohort, significant influencing factors were further identified through univariate analysis and were considered as independent variables in multivariable logistic regression analysis and incorporated in and presented with a nomogram. Results In total, six independent predictors, including the age, sex, tumor size, multifocality, capsular invasion, Hashimotos thyroiditis were entered into the nomogram. Both internal validation and external validation revealed the favorable discrimination of our as-constructed nomogram. Calibration curves exhibited high consistency. As suggested by decision-curve analyses, the as-constructed nomogram might be applied in clinic. Besides, the model also distinguished patients according to risk stratification. Conclusions The novel nomogram containing remarkable influencing factors for CLNM cases was established in the present work. The nomogram can assist clinicians in clinical decision-making.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 87%

See 1 more Smart Citation

A novel tool for predicting the risk of central lymph node metastasis in patients with papillary thyroid microcarcinoma: a retrospective cohort study

Luo

Gao

et al. 2022

BMC Cancer

View full text Add to dashboard Cite

show abstract

“…Our data demonstrate that up to 51% of patients with T1-T2, non-invasive, cN0 PTC harbored central lymph node metastases. Such a high incidence of regional lymph node involvement is similar to other findings (18)(19)(20) and indicates that thyroid cancer is predisposed to LNM and that preoperative ultrasound currently fails to detect a massive number of patients with clinically significant lymph nodal disease (21,22). Therefore, an accurate diagnosis of lymph node status carries much weight in helping clinicians determine the precise treatment for patients as well as informing the patients of prognoses and we advocate a selective approach to pCLND, particularly for cases with a high risk of CLNM.…”

Section: Discussionsupporting

confidence: 91%

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Zhu

Zheng

et al. 2021

Front. Med.

View full text Add to dashboard Cite

Purpose: While there are no clear indications of whether central lymph node dissection is necessary in patients with T1-T2, non-invasive, clinically uninvolved central neck lymph nodes papillary thyroid carcinoma (PTC), this study seeks to develop and validate models for predicting the risk of central lymph node metastasis (CLNM) in these patients based on machine learning algorithms.Methods: This is a retrospective study comprising 1,271 patients with T1-T2 stage, non-invasive, and clinically node negative (cN0) PTC who underwent surgery at the Department of Endocrine and Breast Surgery of The First Affiliated Hospital of Chongqing Medical University from February 1, 2016, to December 31, 2018. We applied six machine learning (ML) algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Neural Network (NNET), coupled with preoperative clinical characteristics and intraoperative information to develop prediction models for CLNM. Among all the samples, 70% were randomly selected to train the models while the remaining 30% were used for validation. Indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance.Results: The results showed that ~51.3% (652 out of 1,271) of the patients had pN1 disease. In multivariate logistic regression analyses, gender, tumor size and location, multifocality, age, and Delphian lymph node status were all independent predictors of CLNM. In predicting CLNM, six ML algorithms posted AUROC of 0.70–0.75, with the extreme gradient boosting (XGBoost) model standing out, registering 0.75. Thus, we employed the best-performing ML algorithm model and uploaded the results to a self-made online risk calculator to estimate an individual's probability of CLNM (https://jin63.shinyapps.io/ML_CLNM/).Conclusions: With the incorporation of preoperative and intraoperative risk factors, ML algorithms can achieve acceptable prediction of CLNM with Xgboost model performing the best. Our online risk calculator based on ML algorithm may help determine the optimal extent of initial surgical treatment for patients with T1-T2 stage, non-invasive, and clinically node negative PTC.

show abstract

“…Most patients have a good prognosis, especially PTMC patients, whose cancer-related mortality rate is merely 0.34% after treatment ( 11 , 12 ). Whereas PTMC patients have a high CLNM rate, studies have pointed out that 28% of PTMC patients are diagnosed with CLNM ( 13 , 14 ). Thyroid cancer diagnosis and treatment guidelines (2022) published by the National Health Commission of the People’s Republic of China recommended prophylactic CLND should be performed for PTC patients as long as the parathyroid gland and recurrent laryngeal nerve can be protected ( 15 ).…”

Section: Discussionmentioning

confidence: 99%

A clinical and molecular pathology prediction model for central lymph node metastasis in cN0 papillary thyroid microcarcinoma

Wang

Zhang

et al. 2023

Front. Endocrinol.

View full text Add to dashboard Cite

BackgroundThe frequency of thyroid cancer has rapidly increased in recent years globally. Thus, more papillary thyroid microcarcinoma (PTMC) patients are being diagnosed, including clinical lymph node-negative (cN0) patients. Our study attempted to develop a prediction model for assessing the probability of central lymph node metastasis (CLNM) in cN0 PTMC patients.MethodsA total of 595 patients from the Affiliated Hospital of Qingdao University (training cohort: 456 patients) and the Affiliated Hospital of Jining Medical University (verification cohort: 139 patients) who underwent thyroid surgery between January 2020 and May 2022 were enrolled in this study. Their clinical and molecular pathology data were analyzed with multivariate logistic regression to identify independent factors, and then we established a prediction model to assess the risk of CLNM in cN0 PTMC patients.ResultsMultivariate logistic regression analysis revealed that sex, Hashimoto’s thyroiditis (HT), tumor size, extrathyroidal extension, TERT promoter mutations and NRAS mutation were independent factors of CLNM. The prediction model demonstrated good discrimination ability (C-index: 0.757 and 0.753 in the derivation and validation cohorts, respectively). The calibration curve of the model was near the optimum diagonal line, and decision curve analysis (DCA) showed a noticeably better benefit.ConclusionCLNM in cN0 PTMC patients is associated with male sex, tumor size, extrathyroidal extension, HT, TERT promoter mutations and NRAS mutation. The prediction model exhibits good discrimination, calibration and clinical usefulness. This model will help to assess CLNM risk and make clinical decisions in cN0 PTMC patients.

show abstract

<p>Analysis of Risk Factors for Lymph Node Metastases in Elderly Patients with Papillary Thyroid Micro-Carcinoma</p>

Cited by 5 publications

References 17 publications

A novel tool for predicting the risk of central lymph node metastasis in patients with papillary thyroid microcarcinoma: a retrospective cohort study

A novel tool for predicting the risk of central lymph node metastasis in patients with papillary thyroid microcarcinoma: a retrospective cohort study

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

A clinical and molecular pathology prediction model for central lymph node metastasis in cN0 papillary thyroid microcarcinoma

Contact Info

Product

Resources

About