Prophylactic central neck dissection for cN0 papillary thyroid carcinoma: is there any difference between western countries and China? A systematic review and meta-analysis

The necessity of prophylactic central lymph node dissection in clinically n0 papillary thyroid carcinoma: perspective from the endemic region

BACKGROUND

Prophylactic central lymph node dissection (pCND) in papillary thyroid carcinoma (PTC) is still a matter of debate. Therefore, we aimed to identify the factors affecting central lymph node metastasis (CLNM) in patients with clinically node-negative (cN0) PTC.

METHODS

This retrospective study included 248 patients with cN0 PTC who underwent total thyroidectomy (TT) or TT + pCND. Clinicopathological associations among CLNM, complication rates and the effect of pCND on staging were assessed. Risk factors (RFs) and the pattern of lymph node metastasis (LNM) in PTC patients were studied via multivariate analysis.

RESULTS

A total of 216 patients underwent pCND, and 58.8% (127/216) had positive CLNM. Male patients, aged < 41 years, and those with lymphatic invasion were identified as RFs for CLNM, with odds ratios of 2.59, 2.26, and 4.09, respectively. Among the 216 patients, 65 (30%) had transient hypoparathyroidism (HPT), and 20 (9.3%) had permanent HPT. Transient recurrent laryngeal nerve (RLN) palsy occurred in 15 (6.9%) patients, and permanent RLN palsy occurred in 3 (1.4%) patients. Over 55 years of age, 46.7% of patients were upstaged according to the American Joint Committee on Cancer (AJCC) TNM staging system, and 14.2% (n = 18) of the 127 patients with CLNM were upgraded according to the American Thyroid Association (ATA) risk stratification system (RSS).

CONCLUSION

Taken together, in terms of the high incidence rate of CLNM in cN0 PTC patients; We believe that routine pCND, which can be performed with low morbidity rates, is optimal for cN0 PTC patients during their first treatment, especially for those with RFs for CLNM.

CLINICAL TRIALS NUMBER

NCT05873283.

Hypoparathyroidism: Similarities and differences between Western and Eastern countries

BACKGROUD

Hypoparathyroidism (hypoPT) is characterized by acute and chronic complications due to insufficient parathyroid hormone (PTH) production or action. Several management guidelines have been developed, but mostly based on evidence from Western countries. Data from Eastern countries have not been systematically compared with those from Western countries.

METHODS

Literatures regarding to the epidemiology, genetics, risk factors, clinical manifestations and therapies for hypoPT in Easten and Western countries, including China, South Korea, Japan, India, and USA, Canada, Italy, and etc., were searched through PubMed and CNKI. This review was officially endorsed by European Calcified Tissue Society (ECTS) board.

RESULTS

Postoperative hypoPT is the major form of hypoPT in both Western and Eastern countries. The genetic profiles and clinical features of hypoPT are similar in Eastern and Western countries. The most commonly used medications in Eastern countries are calcium and native vitamin D or active vitamin D analogues, similar to their Western counterparts. While PTH replacement therapy is not available and approved to use in most Eastern countries.

CONCLUSION

Physicians and surgeons should follow the guidelines on the management of thyroid nodules, taking more care of protecting parathyroid glands during surgery. The cross-talk between East and West in the management of hypoPT should be continued. Direct comparisons of the management strategies in patients with hypoPT between Eastern and Wester countries regarding to the morbidity, mortality, quality of life, optimal dosage, efficacies and side-effects of conventional therapies or newer medications, as well as pharmacogenetics and pharmacoeconomics, would be valuable.

Development and validation of a dynamic nomogram for predicting central lymph node metastasis in papillary thyroid carcinoma patients based on clinical and ultrasound features

Background

Prophylactic cervical lymph node dissection (CLND) for patients with papillary thyroid carcinoma (PTC) has long been a subject of controversy. To accurately perform preoperative staging and risk stratification of PTC patients, this study developed and validated a preoperative nomogram model for predicting central lymph node metastasis (CLNM) based on clinical and ultrasound features, thereby guiding surgical resection and postoperative adjuvant therapy.

Methods

Patients with PTC (n=409), as confirmed by surgery and histopathology combined with CLND, were divided into training and validation groups. Clinical information, ultrasound features, American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) scores and Chinese version of the Thyroid Imaging Reporting and Data System (C TI-RADS) scores were collected. The features in the training group were selected by least absolute shrinkage and selection operator (LASSO) regression. These potential features were included in a multivariate logistic regression analysis to identify independent risk factors for CLNM and to develop a dynamic nomogram. In both the training and validation groups, the nomogram was evaluated for discrimination, calibration and clinical utility.

Results

It was found that sex, age, multifocality, capsule contact, margin, micro-calcification, and ultrasound-based CLNM status were independent risk factors of CLNM, and a dynamic nomogram was used to develop a prediction model. The prediction model showed good discriminability, with an area under the receiver operating characteristic curve of 0.905 (95% confidence interval: 0.870-0.940) in the training group and 0.865 (95% confidence interval: 0.799-0.932) in the validation group. Based on the calibration curve and Hosmer-Lemeshow test, the prediction model was found to have good concordance in both the training group (P=0.6259) and validation group (P=0.1182). Decision curve analysis and clinical impact curve analysis demonstrated that the prediction model had good net clinical benefit.

Conclusions

Dynamic nomograms developed using clinical and ultrasound characteristics can predict CLNM risk in PTC patients, thereby providing valuable support to clinicians for making personalized treatment decisions.

Predicting central cervical lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis: a practical nomogram based on retrospective study

Background

In papillary thyroid carcinoma (PTC) patients with Hashimoto's thyroiditis (HT), preoperative ultrasonography frequently reveals the presence of enlarged lymph nodes in the central neck region. These nodes pose a diagnostic challenge due to their potential resemblance to metastatic lymph nodes, thereby impacting the surgical decision-making process for clinicians in terms of determining the appropriate surgical extent.

Methods

Logistic regression analysis was conducted to identify independent risk factors associated with central lymph node metastasis (CLNM) in PTC patients with HT. Then a prediction model was developed and visualized using a nomogram. The stability of the model was assessed using ten-fold cross-validation. The performance of the model was further evaluated through the use of ROC curve, calibration curve, and decision curve analysis.

Results

A total of 376 HT PTC patients were included in this study, comprising 162 patients with CLNM and 214 patients without CLNM. The results of the multivariate logistic regression analysis revealed that age, Tg-Ab level, tumor size, punctate echogenic foci, and blood flow grade were identified as independent risk factors associated with the development of CLNM in HT PTC. The area under the curve (AUC) of this model was 0.76 (95% CI [0.71-0.80]). The sensitivity, specificity, accuracy, and positive predictive value of the model were determined to be 88%, 51%, 67%, and 57%, respectively.

Conclusions

The proposed clinic-ultrasound-based nomogram in this study demonstrated a favorable performance in predicting CLNM in HT PTCs. This predictive tool has the potential to assist clinicians in making well-informed decisions regarding the appropriate extent of surgical intervention for patients.

Interpretable machine learning model based on the systemic inflammation response index and ultrasound features can predict central lymph node metastasis in cN0T1-T2 papillary thyroid carcinoma

Background

It is arguable whether individuals with T1-T2 papillary thyroid cancer (PTC) who have a clinically negative (cN0) diagnosis should undergo prophylactic central lymph node dissection (pCLND) on a routine basis. Many inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammatory index (SII), have been reported in PTC. However, the associations between the systemic inflammation response index (SIRI) and the risk of central lymph node metastasis (CLNM) remain unclear.

Methods

Retrospective research involving 1,394 individuals with cN0T1-T2 PTC was carried out, and the included patients were randomly allocated into training (70%) and testing (30%) subgroups. The preoperative inflammatory indices and ultrasound (US) features were used to train the models. To assess the forecasting factors as well as drawing nomograms, the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were utilized. Then eight interpretable models based on machine learning (ML) algorithms were constructed, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The performance of the models was evaluated by incorporating the area under the precision-recall curve (auPR) and the area under the receiver operating characteristic curve (auROC), as well as other conventional metrics. The interpretability of the optimum model was illustrated via the shapley additive explanations (SHAP) approach.

Results

Younger age, larger tumor size, capsular invasion, location (lower and isthmus), unclear margin, microcalcifications, color Doppler flow imaging (CDFI) blood flow, and higher SIRI (≥0.77) were independent positive predictors of CLNM, whereas female sex and Hashimoto thyroiditis were independent negative predictors, and nomograms were subsequently constructed. Taking into account both the auROC and auPR, the RF algorithm showed the best performance, and superiority to XGBoost, CatBoost and ANN. In addition, the role of key variables was visualized in the SHAP plot.

Conclusions

An interpretable ML model based on the SIRI and US features can be used to predict CLNM in individuals with cN0T1-T2 PTC.