Thyroid cancer has now ranked as the 7th most common cancer globally, with an age-standardized incidence rates of 9.1 per 100 000 people in 2022 (1). Papillary thyroid cancer (PTC) constitutes the majority of thyroid cancer cases and significantly contributes to its growth (2). Surgery, including thyroidectomy and cervical lymph nodes dissection, TSH suppression therapy and radioiodine therapy are the core treatments of PTC. After comprehensive treatment, the majority of patients experience an ideal prognosis, while there are still more than 10% patients suffering relapse (3, 4).

Lymph node metastasis has been regarded as a prognostic factor in PTC, serving as a predictor for higher mortality and recurrence rates (5–7). Currently, the extent of lymph node dissection is mainly decided by the tumor size and clinical lymph node metastasis. However, some lymph nodes metastases are undetected using existing methods, especially the central lymph node metastasis (CLNM) due to their deep location and small size (8). It is reported that the rate of occult lymph node metastasis is around 24% to 82% (9). Occult lymph node metastasis may progress and lead to completion thyroidectomy, potentially impacting the quality of life of patients.

The incidence of contralateral cervical lymph node metastasis (CCLNM) in clinically negative lymph nodes (cN0) PTC patients is reported to range from 3.9% to 30.6%. Male gender, age < 45 years, lymphovascular invasion, extrathyroidal invasion, ipsilateral CLNM, multifocality, tumor size and tumor location are predictors for CCLNM (10–12). For cN0 patients, the necessity and extent of prophylactic central lymph node dissection (pCLND) are subjects of ongoing debate. Selective pCLND (13) and full extent pCLND (14) were recommended by different studies. Besides, other researchers suggested that ipsilateral CLND should be routinely performed, the decision to perform contralateral CLND should be based on intraoperative frozen-section pathology (15, 16), given the higher prevalence of complications such as permanent hypoparathyroidism in patients receiving bilateral CLND compared to those undergoing only ipsilateral CLND (17–21). Also, there are studies attempted to identify high-risk patients and proposed a “tailored” treatment (18), which means only performing pCLND in high-risk patients but not in others. However, not all factors have the same weight in predicting CCLNM, and a simple “yes or no” judgement is insufficient for clinical decision. Consequently, we attempted to constructed a more precise model to predict CCLNM.

Machine learning (ML), a rapidly evolving field in big data analysis, offers a more sophisticated approach to establish associations between input data and outcomes based on various data types, thereby providing more accurate predictions compared to traditional methods (22). Shortly after its appearance, researchers have been exploring the potential of machine learning to revolutionize medicine (23). Up to now, its application in medical research and practice is extensive. Many studies have explored using classification task to assist in diagnosis and predict prognosis (24, 25).

Here, we developed a machine learning model to predict CCLNM in unilateral cN0 PTC patients, evaluated its performance, and created an online calculator for easy assessment of CCLNM probability.

In this study, we developed and compared 6 popular machine learning algorithms-based models to predict CCLNM in cN0 unilateral PTC patients using multicenter clinical data, utilizing demographic and clinicopathologic features. RF algorithm was selected for further validation and web calculator construction due to its outstanding performance in terms of accuracy, AUC and DCA. Both internal and external validation of the model were performed and showed satisfying results, indicating its potential for widespread application. A web calculator was constructed to facilitate the estimation of the probability of CCLNM in cN0 unilateral PTC patients.

This model helps in identifying CCLNM patients using demographic and clinicopathologic features which are easy to obtained before and during operation. Our study had large population (1780 patients in training cohort, 445 in internal validation cohort, and 409 in external validation cohort), which provides more precise prediction. Besides, to reduce selection bias, our study conducted the validation in external cohort using multicenter clinical data from nine different hospitals. With promising performance, our model showed great robustness and extensive application in CCLNM prediction. Most studies only identified some risk factors of CCLNM in cN0 PTC patients, which were not applicable enough in clinical decision. We not only analyzed the predictive value of several factors, but also constructed a prediction model, and the utility of machine learning makes the prediction more accurate.

The prevalence of CCLNM in our study was 21.1%, which was consistent with previous studies (8.13%-34.3%) (11, 28, 29). The discrepancy may be attributed to variations in patient populations and surgical criteria. We identified that <55 years, male, tumor size > 1cm, multifocality, and ICLNM were risk factors of CCLNM in cN0 PTC patients, while ETE showed no significant prediction value, in line with previous studies (10).

We investigated several demographic factors including age, gender and BMI in this work. It is widely accepted that older age is associated with poorer prognosis in thyroid cancer patients (30), but in our study and previous studies younger age were identified as a predictor of CCLNM (10). Furthermore, studies for total CLNM yielded similar results (31). Although PTC is more prevalent in women than men, male has been reported to be a risk factor of poorer prognosis and worse response to treatment. The underlying molecular mechanisms remains unclear but estrogen and androgen may play a role (32). Even though obesity is reported to increase thyroid cancer incidence (33), its role in lymph node metastasis is contradictory (34, 35) and differs depending on the regions (36). The relationship between obesity and lymph node metastasis and the mechanism underlying requires further study.

Some of the clinicopathologic features showed effects in CCLNM prediction in this work. Our results indicated that in cN0 unilateral PTC patients, multifocality was important to predict CCLNM. Multifocality relates to advanced disease and indicates to higher rate of recurrence, and thus patients with more than one lesions should receive more aggressive treatment (37). 2015 ATA guideline took tumor size into consideration when determining whether to perform pCLND (2). As many other studies (11, 29, 38–41), our results indicated the maximum tumor size was significantly larger in patients with CCLNM than those without. The relationship of ETE and thyroid cancer prognosis is controversial and recent studies further divided it into minimal ETE and extensive ETE based on the extent involved, which showed differences in clinicopathological features, like lymph node metastasis and prognosis (42, 43). As for CCLNM, previous study also demonstrated contradictory results (29, 38, 40). Our analysis revealed that ETE had significant value when performing univariate logistic regression, but not in multivariate analysis, indicating it was not an independent risk factor. However, we did not classify ETE into minimal and extensive group, which may lead to different outcomes.

Similar to previous studies (28, 29, 38–40), ICLNM exhibited great predictive value for CCLNM. Despite of the presence of “skip” metastasis, most metastasis occurs ipsilaterally first. It is interesting that when the models were developed using RICLNM instead of ICLNM, their sensitivity and performance in DCA increased dramatically, with only slight decrease of specificity, and in those models RICLNM had the strongest importance among all variables. Most previous study only focused on the existence of ICLNM, while only Zhou and Qin (11) included the amount of ICLNM in their analysis. Due to the different extent of dissection, absolute number of lymph nodes with metastasis may not reflect the true situation. Studies have demonstrated the significance of metastatic lymph node ratio (MLNR) in PTC prognosis (44–46) but it is rarely used in predicting CCLNM up to now. Our study showed that RICLNM was a stronger predictor of CCLNM than the existence of ICLNM. However, when only very few suspicious lymph nodes are dissected, the ratio will be either extremely large or small. The relationship between the extent of lymph node dissection and clinical value of MLNR requires further study.

There are some limitations of our study. First, its retrospective nature introduced unavoidable bias. Prospective studies are required to verify the accuracy and clinical benefit of the model. Second, although we externally validated of the model using DTCC cohort which includes data from nine centers, patients were all from China. Its application in other races needs further validation. Third, all histopathological features, including maximum diameter, multifocality, ETE and ICLNM, in the study were postoperative results, which are difficult to acquire preoperatively with current detection methods, but a rapid frozen pathological examination intraoperatively can offer the characteristics in need. Fourthly, since the clinical classification is operator-dependent, the judgement of cN0 is not absolutely objective and consistent. More accurate imaging methods may solve this problem in the future.

In conclusion, we presented a ML-based model to predict CCLNM probability in cN0 unilateral PTC patients, validated it in internal and external cohort, and developed an easy-to-use web calculator based on it.

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.

Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements.

AR: Conceptualization, Formal analysis, Methodology, Software, Writing – original draft. JZ: Investigation, Visualization, Writing – original draft. ZW: Resources, Writing – original draft. JM: Investigation, Resources, Writing – review & editing. SR: Investigation, Resources, Writing – review & editing. MX: Conceptualization, Funding acquisition, Software, Validation, Writing – review & editing. TH: Project administration, Supervision, Validation, Writing – review & editing.