A predictive model using platelets and neutrophil-to-lymphocyte ratio for the number of lymph node metastases in papillary thyroid carcinoma: a retrospective analysis

Introduction

Papillary thyroid carcinoma (PTC) is one of the most common endocrine tumors, with its incidence surging significantly over the past decades, leading to widespread public concern (1,2). Although the majority of PTC cases are relatively indolent and the 10-year survival rate is high, 20–90% of patients with PTC develop lymph node metastasis (LNM), significantly increasing the risk of recurrence and adversely affecting their prognosis (3). Clinical studies have demonstrated a quantitative association between metastatic lymph node burden and recurrence risk (4-7), with the American Thyroid Association (ATA) guidelines classifying ≥5 metastatic lymph nodes [designated as large-number lymph node metastases (LNLNM)] as intermediate/high-risk stratification (8). Unfortunately, existing conventional methods, such as preoperative cervical ultrasound and computed tomography, cannot accurately detect LNLNM in patients with PTC (9-12). Consequently, there is an urgent need to develop effective methods for predicting the presence of LNLNM in patients with PTC before surgery to enhance clinical decision-making and optimize treatment efficacy.

Risk prediction models integrate clinical characteristics and laboratory parameters to quantify event probabilities. Their core strength lies in transforming complex clinical problems into actionable quantified risk values, thereby advancing precision medicine. Blood immune indicators have been recognized as important factors in the development and prognosis of malignant tumors, including PTC (13-18). Some inflammatory indicators, including neutrophil count (N), lymphocyte count (L), monocyte count (M), platelet count (Plt), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR). and systemic immune-inflammatory index (SII) are considered effective predictors in many malignant tumors (19-22). Despite their cost-effectiveness and technical accessibility of blood immune indicators, their association with the prognosis of PTC, particularly in relation to LNLNM, remains uncertain.

The objective of this study was to investigate the potential risk factors associated with LNLNM in patients with PTC with a particular emphasis on blood immune indicators, and to establish and verify the predictive model. Our findings may help overcome the limitations of conventional imaging models in detecting micrometastases and inform surgeons in preoperatively assessing metastatic risk and formulating 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-2025-119/rc).

Methods

Patient selection

In this single-center study, the relevant data from 1,017 patients with radical thyroidectomy treated at Affiliated Hangzhou First People’s Hospital of Westlake University School of Medicine (Hangzhou, China) from September 2021 to October 2022 were collected from the electronic medical record (EMR) system for retrospective analysis. The inclusion criteria were as follows: (I) initial radical thyroidectomy and therapeutic lymph node dissection performed for PTC; (II) standard surgical procedures including excision of at least one glandular lobe and ipsilateral lymph node dissection; (III) postoperative pathological diagnosis confirming the presence of PTC; (IV) peripheral blood routine examination conducted within 3 days prior to surgery; and (V) no concurrent malignancies detected. Meanwhile, the exclusion criteria were as follows: (I) a history of preoperative infections (white blood cell >10×10⁹/L), inflammatory diseases, or autoimmune disorders (e.g., Hashimoto’s thyroiditis) that could alter hematological inflammatory markers; (II) a history of chemotherapy or exposure to radioactive substances before surgery; (III) incomplete preoperative and postoperative clinical data; and (IV) a history of chronic diseases such as diabetes, hepatitis, tuberculosis, or kidney disease. Based on the aforementioned inclusion and exclusion criteria, we excluded 286 cases, ultimately retaining 731 patients for analysis. RStudio version 4.3.2 (Posit, Boston, MA, USA) was used to randomly allocate the 731 patients with PTC into a model group (n=513, 70%) and a validation group (n=218, 30%) through a random sampling procedure. The flowchart of the patient selection process is shown in Figure 1. According to the relevant guidelines, lymph node dissection, encompassing lymph nodes and soft tissues within regions II to V, is conducted when preoperative examination leads to a high suspicion of LNM, positive results from lymph node biopsy results are obtained during the operation, or positive results are obtained from preoperative lymph node puncture (7). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Hangzhou First People’s Hospital ([2019] KYYLS No. 040-01) and written informed consent was obtained from all patients.

Figure 1 The flowchart of the patient selection process. LNLNM, large-number lymph node metastases; PTC, thyroid papillary carcinoma.

Data collection

Fasting peripheral blood was collected in the morning 3 days before radical thyroidectomy. Collection of blood samples within 3 days prior to surgery could ensure that the blood was obtained at a relatively consistent time point for each patient, minimizing the influence of external factors. The blood samples were processed with the Mindray BC-6800 automatic blood cell analyzer (Shenzhen Mairui Biomedical Electronics Co., Ltd., Shenzhen, China) to obtain the absolute values of N, M, Plt, and L. The NLR, PLR, LMR, and SII were calculated based on the ratios of the aforementioned inflammatory indicators and were analyzed as continuous variables to prevent information loss and enhance the model’s sensitivity to subtle variations. The pathological data included tumor diameter, multifocality, and the number of LNMs. In cases of multifocal tumors, analysis was performed on the largest tumor. LNLNM was considered to be the presence of more than five metastatic lymph nodes. Additionally, general patient information such as gender and age was collected. All clinicopathological data were retrieved from our hospital’s EMR system, which implements rigorous data completeness protocols. To minimize bias in the data extraction and variable assessment, we implemented dual independent data extraction with third-party arbitration to resolve discrepancies. Pathological evaluations were conducted in a blinded manner to ensure the results remained unaffected by clinical information. Cases with any missing data in preoperative or postoperative clinical documentation were systematically identified and excluded as incomplete clinical records during the screening process.

Statistical analysis

Since our data had a nonparametric distribution, continuous variables are expressed as the median and interquartile range (IQR), while categorical variables are presented as frequencies and percentages. Wilcoxon rank-sum tests were used to analyze continuous variables, and the Pearson chi-squared test or Fisher exact test was applied for categorical variables. Variables with a significance level of P<0.05 in the univariate analysis were selected for inclusion in the multivariate analysis. Binary logistic regression analysis was employed to establish the risk prediction model and construct the nomogram. The predicted outcome of the predictive model was the risk probability of LNLNM in patients with PTC. The evaluation timeline was divided into the model construction phase (training set) and the model validation phase (validation set). The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the discrimination of the above model, with a higher AUC value indicating better model performance. The sensitivity and specificity of the models were calculated at the maximum Youden index. Furthermore, the Hosmer-Lemeshow (HL) test was used to forecast the calibration of the model. A P>0.05 in the HL test indicated that the prediction model had a good fit. Additionally, a calibration diagram was constructed to assess the performance of the predictive model. Finally, decision curve analysis (DCA) was used to verify the clinical net benefit rate of the predicted model. All statistical analyses and figures were conducted via RStudio version 4.3.2.

Results

General information

This study included 731 patients with PTC based on inclusion and exclusion criteria, including 200 males (27.36%) and 531 females (72.64%). LNM was confirmed by postoperative pathology in all patients. Based on the number of metastatic lymph nodes, patients were divided into two groups: with LNLNM and without LNLNM. In our study, 176 (24.07%) patients with more than 5 metastatic lymph nodes were assigned to the LNLNM group, while 555 (75.92%) patients with 1 to 5 metastatic lymph nodes were included in the without-LNLNM group. A random distribution of 7:3 was used to divide the patients with PTC into a model group and a validation group. The validation group was consistent with the model group in terms of setting and identical in eligibility criteria and predictors, differing only in the independent allocation of data. The model group consisted of 513 patients, while the validation group consisted of 218 patients. Specifically, there were 124 (24.17%) patients with LNLNM and 389 (75.83%) patients without LNLNM in the model group, while there were 52 patients (23.85%) with LNLNM and 166 patients (76.14%) without LNLNM in the validation group. The general characteristics of the patients are shown in Table 1. The outcome distribution (incidence of LNLNM) in the validation group was highly similar to that in the model group, indicating high consistency in baseline characteristics and outcomes between the two groups. This design ensured the reliability and generalizability of the model validation.

Screening of predictive model variables

The results of the univariate analysis demonstrated that age, gender, tumor diameter, Plt, N, M, NLR, LMR, PLR, and SII were significant predictors of LNLNM in patients with PTC, with a statistically significant correlation (P<0.05). Conversely, there was no statistically significant correlation in multiplicity or L (P>0.05). The corresponding P values can be found in Table 2.

Significant factors in the univariate analysis were then included in the multivariate analysis. Multivariate logistic regression analysis demonstrated that age [odds ratio (OR) =0.959; 95% confidence interval (CI): 0.939–0.978; P<0.001], tumor diameter (OR =1.149; 95% CI: 1.111–1.193; P<0.001), Plt (OR =1.006; 95% CI: 1.002–1.011; P<0.01), and NLR (OR =1.604; 95% CI: 1.166–2.198; P<0.01) were the independent risk factors for LNLNM in patients with PTC. The specific values are shown in Table 2.

Model construction and validation

The logistic multiple regression analysis findings were incorporated into the construction of the prediction model, which identified age, tumor diameter, NLR, and Plt as risk factors for LNLNM. Consequently, these independent predictors were combined to construct a predictive model for LNLNM in patients with PTC (Figure 2). The full prediction model is expressed as follows:

logP1−P=−2.315+(−0.042×age)+(0.139×tumordiameter)+(0.006×plt)+(0.472×NLR)[1]

Figure 2 Clinical factor-based nomogram for preoperatively predicting LNLNM. This nomogram, developed through multivariable logistic regression analysis, identified age, tumor diameter, NLR, and Plt as independent predictors of LNLNM. Each variable’s value is converted to a corresponding score, and the total score corresponds to the predicted risk of LNLNM. LNLNM, large-number lymph node metastases; NLR, neutrophil-to-lymphocyte ratio; Plt, platelet count.

The model parameters include the intercept (–2.315), age (–0.042), tumor diameter (0.139), Plt (0.006), and NLR (0.472). With this model, the probability of LNLNM for individuals can be calculated. The steps for using this prediction model are as follows: First, collect the patient’s age, tumor diameter, Plt, and NLR. Input these data into the model formula to calculate the log-odds, and then convert it to a probability value. A probability value ≥0.5 indicates a high risk of LNLNM. As an example, for a 50-year-old patient with a tumor diameter of 1.5 cm, NLR =3.5, and Plt =250×10⁹/L, the predicted probability of LNLNM calculated by our model is 25.8%. The ROC curve was generated based on the logistic multiple regression outcomes. The AUC of the model was 0.827 (95% CI: 0.784–0.870; P<0.001), which indicated excellent discrimination. When the Youden index was the highest, the specificity and sensitivity were 75.8% (Figure 3A). A total of 218 validation datasets were included to verify the generalization of the model. The results showed that the AUC of the validation group was 0.824 (95% CI: 0.757–0.890; P<0.001). At the maximum Youden index, the specificity was 79.5% and the sensitivity was 76.9%, indicating a good repeatability and wide applicability of the model (Figure 3B). According to the HL test, there was high goodness of fit (P=0.46), indicating that the predictive model had good calibration in predicting the probability of LNLNM. Moreover, the calibration curves showed good agreement between the predicted and observed probabilities of LNLNM, with a mean absolute error of 0.014 (Figure 4A). The HL test demonstrated excellent calibration in the validation group (P=0.22). In addition, the calibration curve and the DCA of the validation group also indicated that the model had good diagnostic value (Figure 4B and Figure 5).

Figure 3 Receiver operator characteristic curve of (A) the model group and (B) validation group. The blue dot indicates the optimal cut-off point (model group: sensitivity =75.8%, specificity =75.8%; validation group: sensitivity =76.9%, specificity =79.5%). The AUC was 0.827 (95% CI: 0.784–0.870; P<0.001) for the model group and 0.824 (95% CI: 0.757–0.890; P<0.001) for the validation group. AUC, area under the curve; CI, confidence interval.

Figure 4 Calibration curve of (A) the model group and (B) validation group in predicting the probabilities of LNLNM. LNLNM, large-number lymph node metastases.

Figure 5 Decision curve analysis for the model group and validation group.

Discussion

PTC is the most prevalent malignant disease in the endocrine system, with 20–90% of cases also having LNM. The occurrence of LNM not only elevates the risk of recurrence but also significantly diminishes the patient’s prognosis and overall quality of life (3,23,24). Patients exhibiting a large number of LNMs tend to experience inferior outcomes compared to those with a lower number, and the recurrence risk in PTC increases by 5 times when there are more than 5 metastatic lymph nodes (7,25,26). Recurrence occurs in up to 37.3% of patients with more than 5 metastatic lymph nodes (27), which can also significantly reduce the prognosis in patients with PTC and is thus of clinical concern. Therefore, in this study, we defined the presence of more than five metastatic lymph nodes as LNLNM with reference to previous studies and identified the risk factors of LNLNM (8). We found that age, tumor diameter, NLR, and Plt were independent risk factors for LNLNM in patients with PTC. Moreover, the prediction model was demonstrated to be capable of accurately predicting LNLNM in patients with PTC, thereby offering enhanced guidance for clinical practice.

Certain blood immune indicators are closely linked to cancer, holding significant research value (28,29). A number of indicators have been demonstrated to be effective predictors for the presence and prognosis of malignancies across various types of cancers (30-32). NLR, as a relatively popular and reliable blood immune indicator, has been extensively used as a long-term predictor of outcomes across various malignancies (33,34). A high preoperative NLR has been identified as a negative prognostic factor for survival in various types of cancers (35-37). In addition, it was discovered that NLR is associated with the clinicopathologic aggressiveness of PTC and could be used as marker for risk assessment in patients with PTC (32). Similarly, we found that higher NLR levels were more likely to be present in patients with LNLNM and to be indicative of a poor prognosis in our study. Interestingly, Popowicz et al.’s study reported that it is the increase in neutrophils rather than the decrease in lymphocytes that drives the predictive value of NLR (38). The mechanism related to this finding is worth exploring in future research. These findings suggest that an NLR may be associated with the metastasis of PTC, supporting it as a potential marker for predicting LNLNM in patients with PTC.

Platelets are a crucial component of the circulatory system that play a significant role in maintaining hemostasis and various pathological processes, such as inflammation, atherosclerosis, and cancer metastasis (39,40). A high Plt has been associated with an unfavorable clinical prognosis in a number of malignancies, including ovarian, lung, gastric, and breast cancers (41,42). Our study revealed that a high Plt level is associated with LNLNM in PTC and indicative of a poor prognosis. Additionally, a previous study reported that Plt count can serve as a useful predictor of thyroid cancer malignancy and LNM (43).

Age is a major prognostic factor for the risk of LNM and recurrence in patients with PTC (44). A systematic review found that in patients with PTC, the incidence of LNM was 40.12% in patients <45 years old and 34.25% in patients ≥45 years old (45). Similarly, Liu et al. found that LNM rates decline with age, especially in women (46), suggesting that age may be an important factor affecting LNM in patients with PTC. Specifically, younger patients may be more likely to have LNM, which is associated with poor prognosis. In our study, we also found age to be an independent risk factor for predicting LNLNM, with younger patients with PTC being more likely to develop LNLNM than older ones. Therefore, age should be taken into account when clinically assessing the LNLNM in patients with PTC, with younger patients perhaps warranting greater scrutiny.

Studies in this area have predominantly relied upon ultrasound characteristics to develop models for predicting the number of LNMs in patients with PTC (8). Notably, ultrasound factors such as nodule size, multifocal disease, and taller-than-wide shape of LNMs have been identified as independent predictors with superior predictive efficacy. However, ultrasound has a low sensitivity in the diagnosis of cervical LNM (9). According to various studies, the false-positive and false-negative rate of ultrasound to palpable LNMs both range from 20% to 30% (47). Conversely, blood inflammation indicators are widely applicable and easy to acquire, demonstrating high clinical value (48). Our study was the first to use preoperative blood inflammation indicators to predict the presence of LNLNM in PTC. The ROC and HL tests showed that the prediction model of LNLNM in patients with PTC had good discrimination and calibration. Calibration curve analysis and DCA, both in the model and validation group, indicated that the model had a good predictive ability. The predictive model developed in this study demonstrates significant potential for clinical translation. By integrating the model formula into hospital EMR systems, it can automatically calculate patients’ LNLNM risk probability. When the risk probability reaches or exceeds 0.5, the system can trigger warning alerts to assist clinicians in formulating personalized surgical plans (such as the extension of the scope of lymph node dissection). Furthermore, mobile-based simplified calculation tools can be developed (e.g., a digital version of the nomogram shown in Figure 2), enabling rapid risk assessment during outpatient consultations or preoperative discussions.

However, our study involved several limitations that should be acknowledged. First, although the ROC curve and DCA indicated that the model has good discriminative ability and clinical utility, the absence of cross-validation or bootstrapping to assess overfitting risk reduces its generalizability. Second, the single-center study design may also limit the generalizability of our findings. In a follow-up study, we will include more centers for large-sample verification to improve the accuracy of the prediction model. Third, blinding was implemented during pathological evaluation and data management, pathologists assessed anonymized specimens without access to clinical data, and outcome variables were extracted by an independent team with knowledge of the predictor variables; however, the retrospective nature of the study might have limited complete blinding due to implicit information in clinical records, and more importantly, could have introduced the possibility of selection bias in the sample selection process. To address this limitation, we recommend the use of prospective studies in the future. Fourth, the diameter of metastatic lymph nodes was not included in the analysis due to it being difficult to measure accurately. In subsequent studies, it is crucial to incorporate both the size and number of lymph nodes to obtain a more comprehensive evaluation of the prognosis. Finally, regarding the feasibility of clinical integration for this predictive model, further validation through prospective multicenter studies is still required to verify its performance in real-world clinical settings and address standardization issues in EMR system data interfaces.

Conclusions

Age, tumor diameter, NLR, and Plt were identified as significant risk factors for LNLNM in patients with PTC, and the application of a prediction model incorporating these aforementioned factors yielded accurate predictions for LNLNM in patients with PTC, thereby offering improved guidance for clinical practice.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-119/rc

Data Sharing Statement: Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-119/dss

Peer Review File: Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-119/prf

Funding: This work was supported by Zhejiang Provincial Basic Public Welfare Research Project (No. LGF22H160082), and Zhejiang Provincial Medical and Health Technology Project (No. 2022KY939).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-2025-119/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 Ethics Committee of Hangzhou First People’s Hospital ([2019] KYYLS No.040-01) and written informed consent was obtained from all 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/.

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