A nomogram for predicting high-volume central lymph node metastasis in unilateral clinically node-negative papillary thyroid microcarcinoma

Introduction

Papillary thyroid carcinoma (PTC) is the most prevalent type of endocrine cancer (1). The worldwide occurrence of PTC has been on the rise in recent years, with papillary thyroid microcarcinoma (PTMC), characterized by a tumor size of 10 mm or less, making up an increasing share of new diagnoses (1,2). PTC typically has a very good prognosis, with more than 90% of patients surviving for 20 years, and up to 95% for PTMC (2,3). Accordingly, it has been suggested that PTMC patients may initially be managed with active surveillance or minimally invasive interventions as alternatives to immediate surgery (4-6). Furthermore, several guidelines recommend that for clinically node-negative (cN0) PTMC, a unilateral lobectomy is adequate and prophylactic central neck dissection is not routinely indicated (7).

However, PTMC should not be viewed as equivalent to low-risk thyroid cancer (TC), as some PTMC patients exhibit aggressive characteristics, including high-volume central lymph node metastasis (CLNM) (8). High-volume CLNM was defined as more than five metastatic lymph nodes, in line with previous studies and the 2015 American Thyroid Association (ATA) risk stratification guidelines (7,8). The 2015 guidelines from the ATA highlight that a large number of CLNM is crucial for assessing PTMC risk, as it is linked to a higher chance of local recurrence and influences decisions regarding postoperative radioactive iodine treatment (7). Nevertheless, the sensitivity of preoperative neck ultrasonography, the first-line imaging modality, is limited by anatomical constraints and operator’s experience. Consequently, evaluating lymph node status before surgery in PTMC patients remains suboptimal, and a notable proportion of cN0 patients are subsequently found to harbor lymph node metastases, including some with high-volume CLNM (9,10). Therefore, identifying high-volume CLNM in unilateral cN0 PTMC patients carries significant clinical implications for guiding treatment strategies.

Previous studies have rarely explored the risk factors related to high-volume CLNM in patients with PTMC. This study focused on analyzing the clinicopathological characteristics of unilateral cN0 PTMC patients to pinpoint risk factors associated with high-volume CLNM and to develop a risk prediction model aimed at facilitating personalized treatment strategies. We present this article in accordance with the TRIPOD reporting checklist (available at https://gs.amegroups.com/article/view/10.21037/gs-2026-1-0071/rc).

Methods

Patient selection

This retrospective study was carried out on the clinical and pathological information of TC patients treated surgically at the Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, from January 2013 to December 2018. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The First Affiliated Hospital of Chongqing Medical University (No. 2020-487) and informed consent was taken from all the patients.

The criteria for including patients were: individuals who are 18 years old or above; preoperative evaluation indicating cN0; first-time TC surgery; postoperative pathological confirmation of unilateral PTMC; surgical procedure involving at least ipsilateral thyroid lobectomy combined with ipsilateral central neck lymph node dissection; availability of complete clinical and pathological data; absence of distant metastasis; and no prior instances of other head and neck cancers. The criteria for cN0 evaluation were defined according to the standards proposed by Shou et al. (9). The ipsilateral central lymph nodes were defined as including the prelaryngeal, pretracheal, and ipsilateral paratracheal lymph nodes. The anatomical boundaries of the central compartment were delineated with reference to the consensus statement on central neck dissection for thyroid carcinoma issued by the ATA surgical affairs committee in 2009 (11). All surgeries were performed by a single highly experienced surgeon (X.S.), with strict adherence to guideline-based dissection of the ipsilateral central compartment. Preoperative evaluation refers to a comprehensive assessment including clinical examination, thyroid function tests, neck ultrasonography, and selective cervical CT imaging when indicated.

Clinicopathological data were collected, including patient gender, age, tumor size, tumor location, coexistence of Hashimoto’s thyroiditis (HT), presence of extrathyroidal extension (ETE), and multifocality. Tumor size was recorded as an integer, rounded to the nearest whole number. A diagnostic receiver operating characteristic (ROC) analysis was conducted for tumor size, and the area under the curve (AUC) reached its peak at 8 mm. Hence, the cut-off for the maximum tumor size was set at 8 mm. TC can occur across all age groups, but the average age of diagnosis is approximately 50 years (12). As a result, the age of 50 was selected as the dividing line in this study to enable effective comparisons between younger and older patient groups. Multifocality was defined as the presence of multiple tumor foci within the same thyroid lobe, based on final postoperative pathological findings. In cases of multifocal disease, the maximal diameter of the largest tumor focus was used to represent tumor size. The location of this dominant lesion was subsequently defined as the tumor location. ETE was defined to include both gross (macroscopic) extrathyroidal invasion and microscopic ETE, based on postoperative pathological reports. The number of ipsilateral central lymph node metastases was obtained from postoperative paraffin-embedded pathological reports.

Statistical analysis

Statistical analyses were carried out using IBM SPSS Statistics (v27.0; RRID: SCR_002865). Continuous data were presented as the mean ± standard deviation. Using R Project for Statistical Computing (v4.5.1, RRID:SCR_001905), all patients were randomly assigned to a training cohort (70%) and a validation cohort (30%). Within the training cohort, univariate logistic regression analysis was initially conducted, followed by multivariate logistic regression analysis to identify independent risk factors for high-volume CLNM in patients with PTMC. Statistical significance was assigned to P values under 0.05. Using R Project for Statistical Computing (v4.5.1, RRID:SCR_001905), a nomogram was developed from the multivariate logistic regression analysis results in the training cohort. ROC curves and the concordance index (C-index) were utilized to evaluate the discriminative capacity of the nomogram, while decision curve analysis (DCA) and calibration curves were used to assess its predictive accuracy.

Results

Patient characteristics in the training and validation cohort

This study included a total of 1,500 patients diagnosed with unilateral cN0 PTMC. Among them, 623 patients (41.5%) had CLNM, and 60 patients (4.0%) had high-volume CLNM. There were 392 male patients (26.1%) and 1,108 female patients (73.9%), ages spanned from 18 to 78 years, with an average age of 43.43±11.35 years. The mean tumor size was 7.30±2.00 mm. Tumor locations were the lower, middle, and upper portions of the thyroid in 426 (28.4%), 745 (49.7%), and 329 (21.9%) patients, respectively. In 171 patients, multifocality was noted, accounting for 11.4%. Coexistent HT was present in 242 patients (16.1%). ETE was detected in 127 patients (8.5%). Patient characteristics did not significantly differ between the training and validation cohorts (Table 1).

Univariate and multivariate logistic regression analyses of factors associated with high-volume CLNM in the training cohort

Gender, age, tumor size, and multifocality were clinicopathological factors related to high-volume CLNM in the training cohort, with respective P values of 0.01, 0.02, 0.03, and 0.02. Conversely, HT, ETE, and tumor location were not significantly associated with high-volume CLNM (P=0.82, 0.39, and 0.97, respectively). Multivariate analysis demonstrated that male patients, age ≤50 years, a tumor diameter greater than 8 mm, and multifocal disease were significantly associated with a higher likelihood of high-volume CLNM, with corresponding odds ratios of 2.408, 3.049, 2.225, and 3.098, respectively (Table 2).

Nomogram building and validation

Analysis of the training cohort revealed that male patients, younger age (≤50 years), larger tumor size (>8 mm), and the presence of multifocal tumors constituted significant independent risk factors for high-volume CLNM (Table 2). A nomogram integrating these variables was developed to estimate the individualized risk of high-volume CLNM (Figure 1). Each variable in the nomogram was weighted and assigned a corresponding point value. The cumulative score, obtained by adding the points for all predictors, was used to quantify the likelihood of high-volume CLNM. By entering individual clinicopathological parameters, the nomogram allowed rapid and intuitive estimation of patient-specific risk. In the training cohort, the ROC analysis yielded an AUC of 0.73 (95% CI: 0.687–0.742) and a C-index of 0.714, indicating good discriminatory ability (Figure 2A). Calibration curves based on 1,000 bootstrap resamples indicated close agreement between the predicted risks and actual outcomes in the training cohort (Figure 2B). When applied to the validation cohort, the model maintained stable performance, achieving an AUC of 0.70 (95% CI: 0.662–0.746) and a C-index of 0.704 (Figure 3A). In the validation cohort, the calibration chart, also derived from 1,000 bootstrap resamples, showed that predicted probabilities were close to the actual occurrence rates, confirming the model’s reliability and generalizability (Figure 3B). According to DCA, the nomogram achieved superior net benefit over the “treat-all” and “treat-none” approaches when the threshold probability ranged from 0.1 to 0.8, suggesting its potential clinical utility in assessing high-volume CLNM risk and guiding individualized treatment decisions (Figure 4).

Figure 1 Clinicopathological nomogram for predicting high-volume CLNM in unilateral clinically node-negative papillary thyroid microcarcinoma. CLNM, central lymph node metastasis.

Figure 2 Performance evaluation of the training cohort. ROC curve and AUC evaluating the predictive performance of the nomogram (A). Calibration curve of the nomogram (B). AUC, area under the curve; ROC, receiver operating characteristic.

Figure 3 Performance evaluation of the validation cohort. ROC curve and AUC evaluating the predictive performance of the nomogram (A). Calibration curve of the nomogram (B). AUC, area under the curve; ROC, receiver operating characteristic.

Figure 4 Decision curve analysis illustrating the clinical net benefit of the nomogram across a range of threshold probabilities in the training cohort.

Discussion

Epidemiological data indicate a sustained upward trend in the incidence of PTMC in recent years (2). A 2024 retrospective cohort study conducted in Korea, involving over 430,000 patients newly diagnosed with TC, revealed an increase in disease-specific mortality beginning in 2015. The study further demonstrated that patients diagnosed during the periods 2005–2011 and 2015–2018 experienced higher mortality rates attributable to advanced-stage disease at diagnosis, likely due to delays in detection (13). These findings collectively highlight the critical importance of accurate diagnosis and risk-adapted treatment strategies for TC patients at high risk of disease progression, particularly in light of the growing trend toward more conservative clinical management approaches for PTMC (5,6,14). PTMC typically exhibits an excellent prognosis, primarily due to standardized and evidence-based therapeutic approaches. Nevertheless, accurate identification of patients at higher risk for disease progression remains crucial to ensure appropriate risk stratification and the implementation of guideline-concordant management strategies. In our study, clinicopathological variables were analyzed to identify high-risk individuals exhibiting the aggressive feature of high-volume CLNM. Using these findings, we constructed a visually accessible predictive model designed to assist clinicians, particularly surgeons, in refining patient risk assessment, optimizing treatment decisions, and determining the potential need for more extensive therapeutic interventions. Surgical intervention provides effective disease control and is generally followed by low recurrence rates; nevertheless, it may entail potential complications, including transient vocal cord paralysis, postoperative infection, and hypoparathyroidism (15). According to published reports, temporary impairment of the recurrent laryngeal nerve, clinically manifested as transient vocal cord paralysis, occurs in about 0.2% of surgically treated cases of low-risk PTMC. The rate of permanent hypoparathyroidism, typically resulting from inadvertent damage to or loss of blood supply to the parathyroid glands during surgery, is estimated at around 1.5% (16,17). These complication rates underscore the importance of meticulous surgical technique and careful patient counseling regarding perioperative risks.

In this study, among 1,500 patients with unilateral cN0 PTMC, CLNM was identified in 623 cases (41.5%). Of these, 60 patients (4.0% of the total cohort) exhibited high-volume CLNM. This observed rate of high-volume metastasis is lower than those reported in previous studies by Wei et al. (9.9%, 237 out of 2,395 patients) and Shou et al. (7.9%, 33 out of 420 patients) (8,9). The discrepancies may be attributable to variations in patient selection criteria, demographic characteristics of the study populations, sample sizes, and potential differences in pathological assessment protocols.

TC occurs significantly more often in women than in men, a disparity that may be attributed, at least in part, to the expression and activity of estrogen receptor-α (ERα). Accumulating evidence suggests that estrogen signaling through ERα is critically involved in modulating thyroid cellular growth and tumorigenesis, potentially contributing to sex-related differences in TC prevalence (18). However, male patients appear to have a higher susceptibility to lymph node metastasis. Existing evidence consistently identifies male sex as an important risk factor for lymph node involvement in PTMC (8,9). In this study, the cohort comprised 1,500 patients, including 392 (26.1%) males and 1,108 (73.9%) females. After adjustment for potential confounders, male patients showed a significantly higher risk of harboring over five metastatic central compartment lymph nodes [odds ratio (OR) =2.408, 95% CI: 1.255–4.622; P=0.008]. These findings underscore the potential influence of sex-related biological factors on the aggressiveness of PTMC. Age is commonly acknowledged as an important predictor of outcomes in PTC patients. However, its link to lymph node metastasis remains controversial and incompletely defined. Our study found that patients who were 50 years old or younger showed a notably higher rate of CLNM than older patients. Further analysis using multivariate logistic regression identified age ≤50 years as an independent predictor of high-volume CLNM, with an OR of 3.049 and a 95% CI of 1.177–7.900 (P<0.05). In contrast, findings from Kaliszewski et al. suggest that patients aged ≥55 years are more likely to experience lymph node metastasis, highlighting potential discrepancies in risk stratification across different age thresholds (19). Nevertheless, several other clinical studies support our observations, consistently reporting that younger age is correlated with an elevated probability of lymph node involvement in PTC (8,9). This apparent paradox may reflect differences in tumor biology, detection bias due to more aggressive surveillance in younger patients, or variations in study design and population characteristics. The size of the tumor is commonly acknowledged as an important indicator of CLNM in PTMC patients. Nonetheless, there is still debate over the ideal cutoff value for tumor size, with various studies proposing different thresholds. Wei et al. found that when a tumor is larger than 5 mm in diameter, there is a higher likelihood of lymph node metastasis in PTMC (8). According to Qiu et al., larger tumor size was associated with a greater occurrence of CLNM, suggesting a continuous relationship between tumor diameter and metastatic potential (20). Our multivariate analysis identified a maximal tumor diameter greater than 8 mm as a significant independent risk factor for extensive central compartment lymph node metastasis (>5 nodes) (OR =2.225, 95% CI: 1.164–4.255; P<0.05). These results emphasize the value of precise tumor measurement when formulating risk-adapted surgical strategies in PTMC. Wada et al. were the first to propose that intrathyroidal tumor location may influence the incidence of CLNM in individuals with PTMC; however, their findings did not achieve statistical significance (21). Conversely, this study did not find a statistically significant link between the location of the tumor within the thyroid and CLNM (P=0.972), a result that aligns with the findings reported by Jin et al. (10), suggesting that tumor localization within the thyroid gland may not be a reliable predictor of CLNM in PTMC. To date, a substantial body of evidence has demonstrated that multifocal tumors are related to a greater chance of lymph node metastasis (8,9). Consistent with these results, this study found a notable link between tumor multifocality and CLNM. After adjustment for confounding variables, the presence of multifocal disease emerged as a significant independent risk factor for high-volume CLNM (95% CI: 1.400–6.856; P<0.01). HT is an autoimmune thyroid disorder characterized by extensive lymphocytic infiltration and is also referred to as chronic lymphocytic thyroiditis. The relationship between HT and lymph node metastasis remains controversial and incompletely understood. Previous investigations have suggested a possible protective association between HT and lymph node metastasis at diagnosis; the underlying biological mechanisms have yet to be fully elucidated (22). In contrast, other investigations have reported no statistically significant association between HT and the presence of lymph node metastasis (23). In accordance with these latter findings, our study found no significant correlation between HT and CLNM (P=0.82), further supporting the notion that HT does not independently influence the risk of regional nodal involvement in this context. Previous studies have demonstrated that patients with ETE in PTC generally have poorer prognosis and a higher risk of lymph node metastasis compared to those without ETE (24). However, lymph node involvement does not necessarily indicate high-volume metastasis, and ETE may not be directly associated with high-volume CLNM. Consistent with this, our study did not find a significant association between ETE and high-volume CLNM (P=0.39). This finding may reflect intrinsic biological heterogeneity in PTC, where some tumors preferentially spread to lymph nodes while others exhibit extrathyroidal invasion.

Using a cohort of 1,500 patients with unilateral PTMC, we constructed and validated a nomogram to estimate the risk of high-volume CLNM. Each patient’s individualized risk score is derived from specific clinicopathological variables, enabling personalized risk stratification. Previous studies involving 2,395 patients with PTMC have reported a C-index of 0.702 for similar predictive models (8). In comparison, the nomogram developed in the present study achieved a C-index exceeding 0.7, demonstrating acceptable discriminative performance. In our study, the predictive efficacy of the nomogram was assessed through split-sample validation and bootstrapping methodologies. Model calibration was evaluated using 1,000 bootstrap resamples, which indicated a strong concordance between predicted and observed probabilities of high-volume CLNM. The application of bootstrapping offers a more robust strategy for internal validation, especially in scenarios characterized by a low event rate, as it mitigates overfitting and provides more reliable estimates of model performance. Furthermore, the favorable performance on DCA suggests that the nomogram may assist clinicians in balancing risks and benefits when making individualized management decisions. These findings suggest that the integration of key clinicopathological predictors into a quantitative model may assist clinicians in identifying patients with unilateral low-risk PTMC who are at increased risk of extensive nodal involvement. As such, the nomogram may guide more tailored management strategies, including the consideration of prophylactic central neck dissection or more aggressive surgical approaches, thereby optimizing oncologic outcomes while minimizing overtreatment.

Several limitations of this study should be acknowledged. First, this study is a single-center, retrospective analysis, which may introduce selection bias. Second, the model was validated solely through internal validation methods, and the absence of external validation may limit its generalizability. Third, tumor size and age were dichotomized in our analyses to facilitate clinical interpretation; however, this simplification may not fully capture the continuous nature of these variables and could lead to potential discrepancies in risk stratification. Fourth, multifocality is primarily determined postoperatively, which may limit the model’s applicability for preoperative risk stratification. Despite these limitations, large-scale prospective multicenter studies are warranted to further evaluate the accuracy, robustness, and generalizability of the model, which will be essential for establishing its broader utility and reliability in real-world clinical practice.

Conclusions

We identified male sex, younger age (≤50 years), larger tumor size (>8 mm), and multifocality as independent risk factors for high-volume CLNM in unilateral cN0 PTMC. An individualized nomogram incorporating these factors was developed to support preoperative risk stratification. This model may aid in identifying patients at increased risk of aggressive disease and inform personalized surgical strategies in the era of conservative management.

Acknowledgments

The authors would like to thank all the patients who participated in this study and the clinical staff of the Department of Thyroid and Breast Surgery at The First Affiliated Hospital of Chongqing Medical University for their support in data collection and clinical management. We also acknowledge the contributions of the pathology department for their assistance in pathological evaluation.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://gs.amegroups.com/article/view/10.21037/gs-2026-1-0071/rc

Data Sharing Statement: Available at https://gs.amegroups.com/article/view/10.21037/gs-2026-1-0071/dss

Peer Review File: Available at https://gs.amegroups.com/article/view/10.21037/gs-2026-1-0071/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-2026-1-0071/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The First Affiliated Hospital of Chongqing Medical University (No. 2020-487) and informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Hedinger C, Williams ED, Sobin LH. The WHO histological classification of thyroid tumors: a commentary on the second edition. Cancer 1989;63:908-11.
2. Boucai L, Zafereo M, Cabanillas ME. Thyroid Cancer: A Review. JAMA 2024;331:425-35. [Crossref] [PubMed]
3. Lyu Z, Zhang Y, Sheng C, et al. Global burden of thyroid cancer in 2022: Incidence and mortality estimates from GLOBOCAN. Chin Med J (Engl) 2024;137:2567-76. [Crossref] [PubMed]
4. Chou R, Dana T, Haymart M, et al. Active Surveillance Versus Thyroid Surgery for Differentiated Thyroid Cancer: A Systematic Review. Thyroid 2022;32:351-67. [Crossref] [PubMed]
5. Jasim S, Patel KN, Randolph G, et al. American Association of Clinical Endocrinology Disease State Clinical Review: The Clinical Utility of Minimally Invasive Interventional Procedures in the Management of Benign and Malignant Thyroid Lesions. Endocr Pract 2022;28:433-48. [Crossref] [PubMed]
6. Kim HJ, Chung SM, Kim H, et al. Long-Term Efficacy of Ultrasound-Guided Laser Ablation for Papillary Thyroid Microcarcinoma: Results of a 10-Year Retrospective Study. Thyroid 2021;31:1723-9. [Crossref] [PubMed]
7. Haugen BR, Alexander EK, Bible KC, et al. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid 2016;26:1-133. [Crossref] [PubMed]
8. Wei X, Min Y, Feng Y, et al. Development and validation of an individualized nomogram for predicting the high-volume (> 5) central lymph node metastasis in papillary thyroid microcarcinoma. J Endocrinol Invest 2022;45:507-15. [Crossref] [PubMed]
9. Shou JD, Li FB, Shi LH, et al. Predicting non-small-volume central lymph node metastases (>5 or ≥2 mm) preoperatively in cN0 papillary thyroid microcarcinoma without extrathyroidal extension. Medicine (Baltimore) 2020;99:e22338. [Crossref] [PubMed]
10. Jin L, Sun HL, Zhou L, et al. Prediction mode of more than 5 central lymph nodes metastases in clinically node-negative ipsilateral papillary thyroid carcinoma with tumor size 1 to 4 cm. Medicine (Baltimore) 2020;99:e19809. [Crossref] [PubMed]
11. American Thyroid Association Surgery Working Group. Consensus statement on the terminology and classification of central neck dissection for thyroid cancer. Thyroid 2009;19:1153-8.
12. Chen DW, Lang BHH, McLeod DSA, et al. Thyroid cancer. Lancet 2023;401:1531-44. [Crossref] [PubMed]
13. Kim KJ, Choi J, Park SK, et al. Thyroid cancer-specific mortality during 2005-2018 in Korea, aftermath of the overdiagnosis issue: a nationwide population-based cohort study. Int J Surg 2024;110:5489-95. [Crossref] [PubMed]
14. Walgama E, Sacks WL, Ho AS. Papillary thyroid microcarcinoma: optimal management versus overtreatment. Curr Opin Oncol 2020;32:1-6. [Crossref] [PubMed]
15. Deo S, Bansal B, Bhoriwal S, et al. Re-operative surgery for differentiated thyroid cancer: A single institutional experience of 182 cases. Eur J Surg Oncol 2023;49:107042. [Crossref] [PubMed]
16. Alkaf H, Abuduruk S, Bayounos S, et al. Incidence of recurrent laryngeal nerve injury and associated risk factors after thyroidectomy: a retrospective study. Ann Saudi Med 2025;45:295-303. [Crossref] [PubMed]
17. Wang X, Wang SL, Cao Y, et al. Postoperative hypoparathyroidism after thyroid operation and exploration of permanent hypoparathyroidism evaluation. Front Endocrinol (Lausanne) 2023;14:1182062. [Crossref] [PubMed]
18. Song ZM, Wang YD, Chai F, et al. Estrogen enhances the proliferation, migration, and invasion of papillary thyroid carcinoma via the ERα/KRT19 signaling axis. J Endocrinol Invest 2025;48:653-70. [Crossref] [PubMed]
19. Kaliszewski K, Diakowska D, Rzeszutko M, et al. Risk factors of papillary thyroid microcarcinoma that predispose patients to local recurrence. PLoS One 2020;15:e0244930. [Crossref] [PubMed]
20. Qiu P, Guo Q, Pan K, et al. Development of a nomogram for prediction of central lymph node metastasis of papillary thyroid microcarcinoma. BMC Cancer 2024;24:235. [Crossref] [PubMed]
21. Wada N, Duh QY, Sugino K, et al. Lymph node metastasis from 259 papillary thyroid microcarcinomas: frequency, pattern of occurrence and recurrence, and optimal strategy for neck dissection. Ann Surg 2003;237:399-407. [Crossref] [PubMed]
22. Zhong L, Xie C, Gan X, et al. Hashimoto's thyroiditis reduces central lymph node metastasis risk in papillary thyroid microcarcinoma: an integrated meta-analysis. Front Endocrinol (Lausanne) 2025;16:1695508. [Crossref] [PubMed]
23. Shu X, Tang L, Hu D, et al. Prediction Model of Pathologic Central Lymph Node Negativity in cN0 Papillary Thyroid Carcinoma. Front Oncol 2021;11:727984. [Crossref] [PubMed]
24. Mao J, Zhang Q, Zhang H, et al. Risk Factors for Lymph Node Metastasis in Papillary Thyroid Carcinoma: A Systematic Review and Meta-Analysis. Front Endocrinol (Lausanne) 2020;11:265. [Crossref] [PubMed]