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

The role of intraoperative central lymph node biopsy in the treatment of clinically low-risk PTMC

PURPOSE

To evaluate the role of intraoperative frozen biopsy of central lymph nodes in central neck dissection and thyroidectomy in patients of unilateral, clinically negative nodes (cN0) papillary thyroid microcarcinoma (PTMC) without extra-glandular invasion.

METHODS

The clinical data of 465 patients were collected retrospectively. Part of prelaryngeal, pretracheal and ipsilateral paratracheal lymph nodes were taken for frozen pathological examination during the operation. Then the thyroid lobe on the tumor side and isthmus were excised, and central neck dissection of the affected side was performed in all patients. The number of metastases in entire central lymph nodes of the affected side can be obtained by postoperative paraffin pathology. If the number of positive lymph nodes during surgery is ≥3, contralateral gland resection was performed.

RESULTS

In this group of 465 patients, there were 186 cases with central lymph node metastasis. The Kappa coefficient of consistency between frozen pathology and paraffin pathology in central lymph nodes was 0.605. The ROC curve for the number of intraoperative frozen metastases-postoperative pathological metastases over 5 showed that the AUC of the curve was 0.793, while the maximum Youden index was 0.5259, whose corresponding number of positive lymph nodes was 3.

CONCLUSION

Intraoperative central lymph nodes biopsy can be used as an important indicator for the status of central lymph node metastasis in unilateral cN0 PTMC patients without extra-glandular invasion and a determinant for central lymph node dissection. While the number of positive lymph nodes intraoperatively is ≥3, total thyroidectomy should be considered.

Accurate preoperative prediction of nodal metastasis in papillary thyroid microcarcinoma: Towards optimal management of patients

OBJECTIVE

To enhance the accuracy in predicting lymph node metastasis (LNM) preoperatively in patients with papillary thyroid microcarcinoma (PTMC), refining the "low-risk" classification for tailored treatment strategies.

METHODS

This study involves the development and validation of a predictive model using a cohort of 1004 patients with PTMC undergoing thyroidectomy along with central neck dissection. The data was divided into a training cohort (n = 702) and a validation cohort (n = 302). Multivariate logistic regression identified independent LNM predictors in PTMC, leading to the construction of a predictive nomogram model. The model's performance was assessed through ROC analysis, calibration curve analysis, and decision curve analysis.

RESULTS

Identified LNM predictors in PTMC included age, tumor maximum diameter, nodule-capsule distance, capsular contact length, bilateral suspicious lesions, absence of the lymphatic hilum, microcalcification, and sex. Especially, tumors larger than 7 mm, nodules closer to the capsule (less than 3 mm), and longer capsular contact lengths (more than 1 mm) showed higher LNM rates. The model exhibited AUCs of 0.733 and 0.771 in the training and validation cohorts respectively, alongside superior calibration and clinical utility.

CONCLUSION

This study proposes and substantiates a preoperative predictive model for LNM in patients with PTMC, honing the precision of "low-risk" categorization. This model furnishes clinicians with an invaluable tool for individualized treatment approach, ensuring better management of patients who might be proposed observation or ablative options in the absence of such predictive information.

Prediction of central lymph node metastasis in patients with papillary thyroid microcarcinoma by gradient-boosting decision tree model based on ultrasound radiomics and clinical features

Background

In recent years, the study of radiomics in thyroid diseases has developed rapidly. This study aimed to establish a preoperative radiomics prediction model for central compartment lymph node metastases (CLNMs) in papillary thyroid microcarcinoma (PTMC) patients using gradient-boosting decision tree (GBDT) model and evaluate the performance of the model.

Methods

A total of 274 patients with PTMC admitted for thyroid surgery at China-Japan Union Hospital of Jilin University from January 2020 to July 2022 were retrospectively analyzed. Patients were randomized into training and validation cohorts according to a ratio of 8:2. Radiomics features were extracted from the ultrasound (US) images of PTMC lesions. The open-source software Pyradiomics was used to extract radiomics features, and WEKA software was used to select CLNM-related radiomics features. Clinical risk factors for CLNM were screened by statistical methods. The GBDT model was constructed by combining radiomics features and clinical risk factors, and compared with the diagnostic efficacy of US-reported cervical lymph node status. Shapley Additive exPlanations (SHAP) was applied to visualize and analyze the GBDT model globally and locally.

Results

A total of seven radiomics features were significantly correlated with central lymph node status in the training and validation cohorts. The predictors in the GBDT model included the radiomics features, sex, age, and body mass index (BMI). The area under the curve (AUC) values of the GBDT model in the training and validation cohorts were 0.946 [95% confidence interval (CI): 0.920-0.972] and 0.845 (95% CI: 0.714-0.976), respectively, compared with 0.583 (95% CI: 0.508-0.659) and 0.582 (95% CI: 0.430-0.736) for US-reported lymph node status alone. The Delong test showed a significant difference between AUS in the training and validation cohorts (P<0.001, respectively). SHAP visual analysis showed the effect of each parameter on the GBDT model globally and locally. Decision curve analysis demonstrated the clinical utility of the GBDT model.

Conclusions

The prediction of CLNM by the GBDT model, based on US radiomics features and clinical factors, can be better than that by using US alone in patients with PTMC. Furthermore, the GBDT model may serve a guidance of clinical decision for patient's treatment strategy.

Prediction of papillary thyroid metastases to the central compartment: proposal of a model taking into consideration other thyroid conditions

Objective

To construct risk prediction models for cervical lymph node metastasis (CLNM) of papillary thyroid carcinoma (PTC) under different thyroid disease backgrounds and to analyze and compare risk factors among different groups.

Methods

This retrospective study included 518 patients with PTC that was pathologically confirmed post-operatively from January 2021 to November 2021. Demographic, ultrasound and pathological data were recorded. Univariate and multivariate logistic regression analyses were performed to identify factors associated with CLNM in the whole patient cohort and in patients grouped according to diagnoses of Hashimoto's thyroiditis (HT), nodular goiter (NG), and no background disease. Prediction models were constructed for each group, and their performances were compared.

Results

Analysis of the whole PTC patient cohort identified NG as independently associated with CLNM. The independent risk factors for patients with no background disease were the maximum thyroid nodule diameter and American College of Radiology Thyroid Imaging Reporting & Data System score; those for patients with HT were the maximum thyroid nodule diameter, ACR TI-RADS score, and multifocality; and those for patients with NG were the maximum thyroid nodule diameter, ACR TI-RADS score, multifocality and gender.

Conclusion

Background thyroid disease impacts CLNM in PTC patients, and risk factors for CLNM vary among PTC patients with different background diseases. Ultrasound is useful for diagnosing background thyroid disease, which can inform treatment planning. Different prediction models are recommended for PTC cases with different thyroid diseases.

Risk factors associated with high-risk nodal disease in patients considered for active surveillance of papillary thyroid microcarcinoma without extrathyroidal extension

Background

Active surveillance (AS) has become an alternative treatment approach for papillary thyroid microcarcinoma (PTMC). The purpose of this study is to uncover the clinicopathological factors associated with high-risk nodal disease in order to select proper candidates for AS of PTMC.

Methods

We retrospectively reviewed 5,329 patients with PTMC without extrathyroidal extension (ETE) who underwent thyroidectomy with central compartment neck dissection (CCND) between 2007 and 2021 at Seoul St. Mary's Hospital. Patients with more than five metastatic lymph nodes (MLNs) (higher-risk N1 disease) and/or lateral neck node metastases (N1b disease) were defined as having high-risk nodal disease. The clinicopathological factors associated with high-risk nodal disease were analyzed.

Results

A total of 415 (7.8%) patients had higher-risk N1 disease. These patients were younger on average, included a higher proportion of males, and had a larger tumor size and more frequent capsular invasion and multifocality compared with other patients. For the tumor size, a cutoff value of 0.65 cm was the best predictor of nodal risk groups. In a multivariate analysis, the independent risk factors associated with higher-risk N1 disease were younger age, male sex, tumor size >0.65 cm, and the presence of capsular invasion and/or multifocality. A total of 246 (4.6%) patients had N1b disease at initial diagnosis. In a multivariate analysis, the independent risk factors associated with N1b disease were younger age, male sex, tumor size >0.65 cm, and the presence of capsular invasion and/or multifocality.

Conclusions

Young age, male sex, tumor size >0.65 cm, and presence of capsular invasion and/or multifocality can be considered risk factors for high-risk nodal disease in PTMC. Therefore, cautious observation is necessary for AS of patients with these characteristics.

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

BACKGROUND

The 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.

METHODS

A 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.

RESULTS

Multivariate 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.

CONCLUSION

CLNM 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.