Predictive nomogram for occult metastasis in central lymph nodes of papillary thyroid microcarcinoma based on clinical and ultrasound features

Introduction

Thyroid cancer (TC) is the most common endocrine malignancy, with papillary thyroid carcinoma (PTC) accounting for nearly 90% of cases (1). With an increasing emphasis on health checkups and the widespread availability of high-resolution ultrasound, the incidence of PTC continues to escalate annually. According to China’s National Cancer Center report on cancer incidence and mortality in 2022, TC ranked third in terms of new cases in China, following lung and colorectal cancers, with approximately 466,000 new cases reported (2). If the maximum size of PTC is less than 1cm, it is defined as papillary thyroid microcarcinoma (PTMC), which comprises roughly 30% of PTC cases (3).

PTMC is generally considered a low-risk tumor with a favorable prognosis, boasting a 10-year disease-free survival rate exceeding 90% after standardized management (4). However, PTMC is prone to lymph node metastasis (LNM), particularly central lymph node metastasis (CLNM), with reported rates ranging from 18% to 80% (5,6). CLNM is associated with distant metastasis and tumor recurrence, significantly impacting patient prognosis (7). Hence, accurately assessing CLNM preoperatively is critical. Yet, due to imaging technique limitations and operator expertise, ultrasound often fails to detect metastases in regional lymph nodes, with reported sensitivities of only 25% to 60% for abnormal central and lateral cervical lymph nodes (8). Consequently, the implementation of prophylactic central lymph node dissection (pCLND) in clinically lymph node-negative (cN0) PTMC patients remains controversial. Some experts argue that pCLND can reduce the risks of tumor recurrence and reoperation, thereby improving patients’ disease-free survival rates rate (9,10). However, opposing viewpoints suggest that pCLND fails to enhance survival rates while increasing the likelihood of post-thyroid surgery complications such as nerve damage, hypoparathyroidism, and chyle leak (11-13). The Chinese Society of Clinical Oncology guidelines (version: 2018) recommend a comprehensive evaluation of patient surgical risks and benefits before considering pCLND (14).

Therefore, the objective of this study is to develop a predictive model utilizing clinical data and ultrasound features. This model aims to offer a preoperative means of objectively quantifying the CLNM status in cN0 PTMC patients. Such an approach can aid clinicians in making informed treatment decisions and determining the most suitable surgical strategy. We present this article in accordance with the TRIPOD reporting checklist (available at https://gs.amegroups.com/article/view/10.21037/gs-2025-159/rc).

Methods

Population selection

Retrospective analysis of 884 patients with pathologically confirmed diagnosis of PTMC admitted to The First Affiliated Hospital of Nanchang University from January 2022 to December 2023 was conducted. The inclusion criteria were: (I) postoperative pathological confirmation of PTC; (II) maximum tumor diameter ≤1 cm; and (III) primary tumor located in the thyroid gland. The exclusion criteria were (I) combination of other types of TC; (II) history of previous neck surgery (thyroidectomy or thermal ablation) or radiation; (III) CLNM seen on ultrasound examination; (IV) incomplete ultrasonographic or pathologic data. The flowchart for our study was shown in Figure 1. This study was approved by the Ethics Committee of The First Affiliated Hospital of Jiangxi Medical College, Nanchang University (approval code: 2024-202). Individual consent for this retrospective analysis was waived, and the study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Figure 1 Flowchart of the patient selection. CLNM, central lymph node metastasis; PTC, papillary thyroid carcinoma; PTMC, papillary thyroid microcarcinoma.

Data collection

Relevant clinical indicators were obtained from hospital electronic medical record systems, including: age, gender, body mass index (BMI), ultrasound characteristics of the nodule [echogenicity, margins, shape, calcification, length/width rate, extrathyroidal extension (ETE), blood signal], and tumor size (maximum size of the primary tumor), focal infection (unifocal/multifocal) and location (isthmus/not). Hashimoto’s thyroiditis (HT) was diagnosed based on pathological data. All patients underwent subtotal thyroidectomy or lobectomy with pCLND.

Statistical analysis

The patients included in our study were randomly divided into training and validation sets in a 7:3 ratio. Continuous variables following a normal distribution were expressed as mean ± standard deviation (x¯±s), while categorical variables were presented as frequencies and percentages. Independent samples t-test was used for measurement data, and the Chi-squared test or Fisher’s exact probability method was used for count data. Multiple logistic regression was utilized to identify independent risk factors associated with CLNM, represented by odds ratios (ORs) and 95% confidence intervals (CIs). Constructed a nomogram model for predicting CLNM and validated the model’s performance by used validation set. Calculated the area under the curve (AUC) of receiver operating characteristic (ROC) to assess the discriminatory ability of the model. Assessing the calibration capability of models by using calibration curves. Clinical utility was assessed by using decision curve analysis (DCA). Statistical analyses were performed using R version 4.3.0, Ρ<0.05 was considered statistically significant.

Results

Baseline characteristics

In the training set, a total of 590 patients (126 males and 464 females) were included, with 205 patients (34.7%) had CLNM. The validation set included 254 patients (42 males and 212 females), with 98 patients (38.6%) had CLNM. Finally, a total of 844 patients were included in our study, of whom 303 (35.9%) developed CLNM. The patients’ detailed clinical and ultrasound characteristics were shown in Table S1.

Nomogram variable screening

In the training set, univariate analysis revealed that male, age <55 years, multifocal, isthmus tumor, microcalcification, ETE and tumor size were significantly associated with the occurrence of CLNM in cN0 PTMC patients (P<0.05) (Table 1). These factors were then included in a multivariate logistic regression analysis, and we ultimately identified six independent risk factors, including male (OR =2.96, 95% CI: 1.90–4.61, Ρ<0.001), age <55 years (OR =1.91, 95% CI: 1.20–3.04, P=0.006), multifocal (OR =2.10, 95% CI: 1.43–3.09, Ρ<0.001), isthmus (OR =3.37, 95% CI: 1.42–8.03, Ρ=0.006), microcalcification (OR =2.02, 95% CI: 1.38–2.96, Ρ<0.001), and tumor size (OR =2.27, 95% CI: 1.47–3.49, Ρ<0.001) (Table 2).

Nomogram establishment and validation

Based on the independent risk factors (gender, age, focal infection, location, calcification, and tumor size) identified by multifactorial logistic regression analysis, we constructed a nomogram to predict the occurrence of CLNM in cN0 PTMC patients (Figure 2). Each variable has a corresponding score on the upper axis of the chart, and the total score was obtained by adding the scores of each variable, which corresponds to the probability of CLNM occurring on the lower axis. The nomogram model was subsequently further evaluated by drawing ROC curves, and internal validation was performed by using the validation set. The AUC of the training and validation sets were 0.746 (95% CI: 0.704–0.789) (Figure 3A) and 0.726 (95% CI: 0.663–0.790) (Figure 3B), respectively, demonstrating a good ability to distinguish CLNM in cN0 PTMC patients. In addition, both the calibration curves for the training set (Figure 4A) and the validation set (Figure 4B) demonstrate that the predicted values of the model were good consistency with the actual results, which indicates the good calibration ability of the model. Finally, in order to assess the net benefit of the nomogram, DCA curves were plotted for both the training and validation sets. The results showed that utilizing the nomogram to predict the occurrence of CLNM in cN0 PTMC patients and giving the corresponding treatment strategy could obtain a large net clinical benefit (Figure 5A,5B).

Figure 2 Nomograms for predicting CLNM in cN0 PTMC patients. CLNM, central lymph node metastasis; cN0, clinically lymph node-negative; PTMC, papillary thyroid microcarcinoma.

Figure 3 ROC curves of the nomogram models in training set (A) and validation set (B). AUC, area under the curve; ROC, receiver operating characteristic.

Figure 4 Calibration curves for training set (A) and validation set (B).

Figure 5 DCA of the nomogram model in training set (A) and validation set (B). DCA, decision curve analysis.

Discussion

In recent years, with the improvement of people’s awareness of physical examination and the development and application of high-resolution ultrasound and fine needle aspiration biopsy, the incidence of asymptomatic PTMC has been increasing year by year (15). However, its mortality rate has remained essentially stable, with a 10-year survival rate of more than 90% after standardized management (4). Consequently, some scholars believe that PTMC has been over-diagnosed and over-treated, and propose that active surveillance (AS) is the effective management approach for PTMC (16). But the problems that come with are anxiety and depression. In a cross-sectional study by Nakamura et al., it was found that patients in the AS group had a significantly increased risk of anxiety and depression due to the extensive repetitive ultrasound, tissue biopsy, and blood tests during long-term follow-up, as well as the emotional and psychological effects of known cancer (17). Therefore, in China, patients diagnosed with PTMC are still more likely to receive surgical treatment. However, due to the sensitivity of ultrasonography to preoperative diagnosis of CLNM was poor. Kim et al. found that the preoperative ultrasound assessment has a missed diagnosis rate of approximately 30% for CLNM (18). This is consistent with our findings that we included a total of 844 cases of cN0 PTMC patients, and postoperative pathology showed that approximately 303 cases (35.9%) had CLNM. This means that a significant proportion of cN0 PTMC patients should actually to be pN1. Therefore, there is still controversy regarding whether pCLND should be performed for cN0 PTMC patients. Some scholars believe that pCLND not only fails to improve patient survival rates but also increases the risk of postoperative complications such as recurrent laryngeal nerve injury and hypoparathyroidism (12,13,19). However, other scholars argue that pCLND can reduce the risk of local recurrence and the need for reoperation (9,10). Therefore, accurately identifying the risk factors associated with CLNM is crucial for guiding patients in adopting appropriate surgical approaches and reducing the occurrence of complications.

Our study included a total of 844 cN0 PTMC patients and established a nomogram based on clinical data and ultrasound features to predict the risk of CLNM occurrence. Through univariate and multivariate logistic regression analysis, it was ultimately determined that male, age <55 years, multifocal, isthmus location, microcalcification, and tumor size were independent risk factors for CLNM occurrence. According to the nomogram model, we can know that the AUC of the training set is 0.746 (95% CI: 0.704–0.789), and the AUC of the validation set is 0.726 (95% CI: 0.663–0.790), indicating that the model has moderate prediction efficiency. Similarly, Qiu et al. found that age, gender, multifocal, tumor size, and tumor boundaries were independent risk factors for CLNM in cN0 PTMC patients (20). In addition, Wang et al. also found that male, younger age, larger tumor size, ETE, microcalcification, and multifocal were independent risk predictors of CLNM (21). Some of the risk factors in these studies that were not confirmed in this study may be related to the different ultrasound devices and the clinical experience of the sonographer and the insufficient sample size in this study.

A plethora of prior studies has consistently indicated a higher incidence of PTMC in females compared to males, while conversely, the incidence of CLNM tends to be lower (22,23). Our study corroborates these trends. Within our analysis, the incidence of CLNM among males and females in the training set stood at 57.14% and 28.67%, respectively. Notably, male gender was identified as the significant risk factor for CLNM (OR =2.96, 95% CI: 1.90–4.61, Ρ<0.001), and it ranked as the second most important variable in the nomogram.

Age has long played an important role in influencing the prognosis of PTMC. Studies have shown that different age groups have varying effects on PTMC: older patients have a lower rate of PTMC progression, while younger patients show a higher progression rate (24,25). In this study, younger age was found to be an independent risk factor for CLNM (OR =1.91, 95% CI: 1.20–3.04, P=0.006). Therefore, in clinical practice, elderly PTMC patients may be more suitable candidates for AS, while younger patients may require more aggressive intervention.

Multifocal papillary thyroid microcarcinoma (MPTMC) is currently understood to arise from intraglandular metastases originating from a single primary tumor or multiple synchronous primary tumors (26), constituting approximately 20–40% of PTMC cases (27). Previous research has consistently demonstrated that MPTMC is significantly associated with an elevated risk of LNM, distant metastasis, and tumor recurrence (28-30). In our study, multifocality (OR =2.10, 95% CI: 1.43–3.09, Ρ<0.001) emerged as an independent risk factor for the development of CLNM in cN0 PTMC patients.

The isthmus of the thyroid gland spans across the trachea, linking the lower portions of the lobes of both glands. According to pertinent literature, papillary thyroid microcarcinoma of the isthmus (PTMCI) constitutes approximately 2.5% to 12.3% of all PTMC cases (31). The thinning and narrowing of the gland in the isthmus contribute to a more invasive biological behavior of malignant tumors (32,33). A study conducted by Zheng et al. (23), revealed that among patients with single primary tumors, those with PTMCI were more predisposed to developing CLNM. Additionally, research by Ma indicated that PTMCI had a higher propensity for metastasizing to the anterior laryngeal lymph nodes and anterior tracheal lymph nodes (34). In this study, the isthmus emerged as the most significant variable in the nomogram (OR =3.37, 95% CI: 1.42–8.03, Ρ=0.006), consistent with findings from previous study.

Microcalcification is caused by the deposition of calcium salts due to the expansion of blood vessels and fibers caused by the rapid proliferation of cancer cells (21). Its ultrasonographic feature manifests as punctate strong echoes, which is one of the highly characteristic signs of PTC (19). In this study, microcalcification (OR =2.02, 95% CI: 1.38–2.96, Ρ<0.001) is an independent risk factor for the development of CLNM, consistent with the results of other study (35).

Tumor size correlates directly with tumor stage, with conventional studies suggesting that larger tumor diameters indicate greater aggressiveness (36). For instance, Ye et al. demonstrated that a tumor size >0.5 cm could serve as a preoperative predictor for CLNM occurrence in PTMC patients (37). The same conclusion is reached in the present study, where the OR for tumor size >0.5 cm to predict the occurrence of CLNM was (OR =2.27, 95% CI: 1.47–3.49, Ρ<0.001).

There are several limitations in this study. Firstly, it is a retrospective study, so selection bias inevitably occurs; secondly, although we included over 800 cases, the sample size is still insufficient, and all data come from a single center, lacking external validation; thirdly, the genotypes of the tumors were not included in the model, such as BRAF-V600E, TERT, RET, KRAS, NRAS, HRAS and so on. In the future, prospective multicenter studies can be conducted, and tumor genetic monitoring can be improved before surgery to enhance the predictive effectiveness of the model and guide clinical decision-making.

Conclusions

In our study revealed significant correlations between gender, age, multifocal, isthmic, microcalcification, and tumor size with CLNM among cN0 PTMC patients. Additionally, we developed a prediction model utilizing these six variables to offer clinicians guidance in making individualized treatment decisions and facilitating precise interventions.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-159/rc

Data Sharing Statement: Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-159/dss

Peer Review File: Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-159/prf

Funding: This work was supported by the Jiangxi Provincial Science and Technology Innovation Base Construction—Clinical Medical Research Center (No. 20221ZDG02011); Jiangxi Provincial Science and Technology Innovation Base Plan—Provincial Clinical Medical Research Center (No. 2020BCG74001); and Jiangxi Provincial Natural Science Foundation (No. 20224ACB206010).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-2025-159/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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study protocol received approval from the Research Committee of The First Affiliated Hospital of Jiangxi Medical College, Nanchang University (approval code: 2024-202). The ethics committee granted permission for the use of anonymized historical data in the study and waived the requirement for informed patient consent.

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/.

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