Predicting Central Lymph Node Metastasis for cN0 Papillary Thyroid Microcarcinoma: a Nomogram Model Based on Blood Copper and Clinical Characteristics

Predicting Central Lymph Node Metastasis for cN0 Papillary Thyroid Microcarcinoma: a Nomogram Model Based on Blood Copper and Clinical Characteristics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting Central Lymph Node Metastasis for cN0 Papillary Thyroid Microcarcinoma: a Nomogram Model Based on Blood Copper and Clinical Characteristics Yi-Wei Zhang, Heng-Feng Zhang, Rui-Fang Guo, Huan Wang, Cui-Cui Ma, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9292047/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract To develop and validate a clinical prediction model for central lymph node metastasis (CLNM) in patients with clinically lymph node-negative papillary thyroid microcarcinoma (cN0 PTMC). This retrospective study (2021-2024) enrolled 333 cN0 PTMC patients. Participants were randomly allocated at a 6:4 ratio to a training cohort (n=199) or a validation cohort (n=134). Based on postoperative pathological assessment of CLNM, the training cohort was stratified into non-CLNM (NCLNM, n = 132) and CLNM (n = 67) groups. Blood copper levels and clinical characteristics were systematically recorded. Logistic regression identified factors associated with CLNM, and Lasso regression selected key predictors to develop a nomogram for CLNM risk estimation. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) to assess discrimination, calibration, and clinical utility in both cohorts. The incidence of CLNM in patients with cN0 PTMC was 33.7%. The predictive model incorporated blood copper levels, age, smoking, drinking, height, maximum tumor diameter, Hashimoto's thyroiditis, free triiodothyronine, free thyroxine, thyroid-stimulating hormone (TSH), and thyroglobulin antibody (TgAb). It demonstrated an AUC of 0.784 (sensitivity 0.881, specificity 0.553) with excellent calibration. The calibration curve exhibited a slope close to 1, indicating excellent calibration. DCA showed consistently positive net benefits across risk thresholds of 0.34–0.94. Validation yielded an AUC of 0.783 (sensitivity 0.750, specificity 0.459). Exclusion of copper metabolism-related factors reduced the AUC to 0.716. The nomogram incorporating blood copper and clinical characteristics provided an effective tool for predicting CLNM in cN0 PTMC patients. Trial Registration ChiCTR2100054781.Registered 27 December 2021, for retrospectively registered trials. Nomogram Copper cN0 Papillary thyroid microcarcinoma Central lymph node metastasis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Papillary Thyroid Microcarcinoma (PTMC) is defined as papillary thyroid carcinoma with a maximum tumor diameter of ≤ 1 cm. As a major subtype of thyroid cancer (TC), PTMC currently comprises over 50% of newly diagnosed TC cases[ 1 ]. However, PTMC should not be equated with early-stage or low-risk cancer. It shares the same biological behavior as larger papillary thyroid carcinomas, including the potential for extrathyroidal extension and cervical lymph node metastasis—among which central lymph node metastasis (CLNM) is the most common. Even in clinically lymph node-negative (cN0) PTMC patients with no preoperative evidence of lymph node involvement, approximately 15–64% are found to have CLNM upon postoperative histopathology. CLNM is closely associated with postoperative recurrence and poorer prognosis[ 2 ]. Currently, the necessity of prophylactic central lymph node dissection (PCLND) in cN0 PTMC remains controversial. Thus, accurate, non-invasive preoperative assessment of CLNM represents a critical challenge in clinical management. Current imaging assessment methods exhibit significant limitations. While high-resolution ultrasound (US) serves as the preferred screening modality, its diagnostic accuracy for CLNM remains suboptimal, with literature reporting sensitivities of 23–38% and specificities of 90–97%[ 3 , 4 ]. Ultrasound-guided fine-needle aspiration cytology (FNAC) offers an accessible, cost-effective, and minimally invasive approach for evaluating cervical lymph node lesions, and is consequently widely utilized in clinical practice. Nevertheless, sole reliance on FNAC presents inherent limitations: diagnostic outcomes may be compromised by operator experience, sampling site selection, and tumor heterogeneity, resulting in variable accuracy. Furthermore, as an invasive procedure, FNAC frequently encounters patient reluctance. Trace element profiling—particularly for copper—demonstrates considerable potential as a novel cancer biomarker. Copper acts as an essential cofactor for metastatic regulators (including lysyl oxidase (LOX) and vascular endothelial growth factor (VEGF)), mediating tumor microenvironment remodeling through activation of signaling pathways involving collagen crosslinking and angiogenesis. This process critically influences tumor recurrence and metastasis[ 5 , 6 ]. Elevated copper levels are well-documented in both tumor tissue and serum across multiple malignancies (e.g., breast, lung, gastric, thyroid, and prostate cancers)[ 7 ]. A 2023 prospective cohort study specifically confirmed that elevated blood copper significantly increases gastric cancer lymph node metastasis risk (OR = 2.4, 95% CI 1.6–3.6)[ 8 ]. In addition, copper, as an essential constituent of superoxide dismutase, participates in the synthesis, activation, and metabolism of thyroid hormones. Abnormal copper metabolism may interact with thyroid function indicators (free triiodothyronine, free thyroxine, Thyroid stimulating hormone) and autoimmune markers (thyroglobulin antibody), collectively influencing the risk of lymph node metastasis[ 9 ]. Zinc and iron are involved in regulating oxidative stress and DNA repair pathways, and their dysregulation has been associated with tumor aggressiveness[ 10 , 11 ]. However, the integration of trace elements into clinical prediction models remains unexplored. Addressing the clinical challenge of identifying CLNM in cN0 PTMC patients where current diagnostic approaches show significant limitations - our study responds to the paucity of validated predictive models. We therefore developed and validated a novel integrated risk stratification tool using trace elements and established clinicopathological risk factors. By analyzing blood copper levels, clinical data, and pathological characteristics of cN0 PTMC patients, we constructed a clinically applicable visual nomogram for CLNM prediction. This evidence-based model outperforms traditional methods and establishes a preoperative decision-support system to guide personalized surgical management of central lymph node metastasis. Methods Study Design This retrospective study received ethical approval from the Ethics Review Committee of the People's Hospital of Inner Mongolia Autonomous Region (Approval No: 2021003). All participants provided written informed consent authorizing the use of their blood test results and clinicopathological data. Data collection spanned 2020 to 2024. Inclusion criteria: 1.cN0 PTMC diagnosis confirmed; 2.Scheduled for thyroidectomy with central compartment lymph node dissection at our institution. Exclusion criteria: 1.Surgical contraindications; 2.Current trace element supplementation; 3.History of other thyroid disorders; 4.Abnormal hepatic or renal function. All enrolled PTMC patients underwent central compartment lymph node dissection. Central lymph node metastasis (CLNM) was histopathologically confirmed when ≥ 1 metastatic lymph node was identified; otherwise, cases were classified as non-CLNM (NCLNM). A total of 333 patients were randomized into training (n = 199) and validation (n = 134) cohorts at a 6:4 ratio. Univariate logistic regression identified potential predictors in the training cohort, followed by predictive model construction using LASSO regression. Model performance was subsequently validated in the independent cohort. The participant selection process is detailed in Fig. 1 . Sample Collection Patients were instructed to maintain normal dietary habits before blood collection, while avoiding copper, zinc, calcium, magnesium, and iron-rich foods and supplements. Following a 12-hour fasting period, participants abstained from alcohol and medications. Venous blood (2000 µL) was collected, and serum was separated via centrifugation (3000 rpm, 10 min). Aliquots (150 µL) were cryopreserved at − 80°C. Clinicopathological characteristics of cN0 PTMC and patient demographics were extracted from electronic medical records. Test Methods Blood copper, zinc, calcium, magnesium, and iron concentrations were quantified using atomic absorption spectrophotometry. Thyroid function was assessed via chemiluminescence immunoassay, measuring: Triiodothyronine (T3) Free triiodothyronine (FT3) Thyroxine (T4) Free thyroxine (FT4) Thyroid-stimulating hormone (TSH) Thyroglobulin antibody (TgAb) Thyroid peroxidase antibody (TPOAb) Thyroglobulin (TG). Reference ranges were as follows: T3: 0.60 to 1.81 ng/ml, FT3: 1.50 to 4.10 pg/ml, T4: 4.50 to 10.90 ug/dl, FT4: 0.8 to 1.80 ng/dl; TSH: 0.4 to 5.00 uIU/ml, TGAb: 0.00 to 4.50 IU/mL, TPOAb: 0.00 to 60.00 IU/mL, TG: 0.83 to 68.30 ng/mL. The normal range for blood copper concentration is 0.7 to 1.4 mg/L. Statistical Analysis Continuous variables are presented as mean ± standard deviation or median (interquartile range), while categorical variables are summarized using frequencies and percentages. Intergroup differences were assessed using chi-square tests (for categorical variables) and analysis of variance (ANOVA, for continuous variables). Univariate logistic regression identified potential predictors of central lymph node metastasis (CLNM).The LASSO regression method was implemented to select optimal predictors and construct a dynamic nomogram for CLNM risk stratification in cN0 PTMC patients. Significant variables identified through LASSO regression were subsequently incorporated into a multivariate logistic regression model to develop the final nomogram. Model discrimination was evaluated by calculating the area under the receiver operating characteristic curve (AUC-ROC). Calibration performance was assessed using bootstrap validation with 1,000 resamples. Clinical utility was quantified through decision curve analysis (DCA), with both internal and external validation performed. To further investigate the contribution of copper metabolism biomarkers to our predictive models, we compared the area under the curve (AUC) values between models incorporating these indicators versus those excluding them. Stepwise regression was subsequently employed to develop alternative models, with performance evaluated through comparative AUC analysis. Existing models from published literature were benchmarked against our selected configurations, with the DeLong test determining the optimal model. All statistical analyses were performed using R software (v4.2.2; R Foundation) and SPSS Statistics (v26.0; IBM). Statistical significance was defined at P < 0.05. Results Baseline Characteristics A total of 333 cN0 PTMC patients participated in this study. They were randomly divided into a training (199) and validation (134) cohort. No significant differences were found between the two groups. The baseline characteristics of the patients and the binary logistic regression results are shown in Table 1 and Table 2 . The P values for sex, age, height, smoking, FT3, and blood copper were all < 0.05. Table 1 Baseline characteristics of the patients and the results of the univariate logistic regression Characteristics ALL (N = 199) NCLNM (N = 132) CLNM (N = 67) P OR 95%CI lower 95%CI upper Gender Female 159 (79.90%) 111 (84.09%) 48 (71.64%) 0.041 2.092 1.032 4.243 Male 40 (20.10%) 21 (15.91%) 19 (28.36%) Age(years) 50.00 (13.00) 52.00 (12.00) 47.00 (16.00) <0.001 0.943 0.912 0.974 Weight(kg) 66.00 (15.00) 65.50 (15.00) 68.00 (19.25) 0.528 1.006 0.987 1.025 Height(cm) 163.00 (8.00) 161.00 (9.00) 165.00 (8.00) 0.008 1.064 1.016 1.114 Smoking No 174 (87.44%) 121 (91.67%) 53 (79.10%) Yes 25 (12.56%) 11 (8.33%) 14 (20.90%) 0.014 2.906 1.238 6.820 Drinking No 183 (91.96%) 125 (94.70%) 58 (86.57%) Yes 16 (8.04%) 7 (5.30%) 9 (13.43%) 0.054 2.771 0.984 7.806 Unilateral or Bilateral Unilateral 152 (76.38%) 103 (78.03%) 49 (73.13%) Bilateral 47 (23.62%) 29 (21.97%) 18 (26.87%) 0.443 1.305 0.661 2.574 Infringe No 169 (84.92%) 111 (84.09%) 58 (86.57%) Yes 30 (15.08%) 21 (15.91%) 9 (13.43%) 0.645 1.219 0.525 2.833 Multifocality No 128 (64.32%) 86 (65.15%) 42 (62.69%) Yes 71 (35.68%) 46 (34.85%) 25 (37.31%) 0.732 0.899 0.488 1.655 Hashimoto No 131 (65.83%) 82 (62.12%) 49 (73.13%) Yes 68 (34.17%) 50 (37.88%) 18 (26.87%) 0.123 0.602 0.316 1.148 Diameter(cm) 0.50 (0.30) 0.50 (0.30) 0.50 (0.35) 0.343 1.881 0.509 6.953 Table 2 Laboratory of the patients and the results of the univariate logistic regression Laboratory ALL (N = 199) NCLNM (N = 132) CLNM (N = 67) P OR 95%CI lower 95%CI upper T3(ng/ml) 1.11 ± 0.21 1.11 ± 0.20 1.12 ± 0.23 0.793 1.205 0.298 4.879 FT3(pg/ml) 3.23 (0.43) 3.21 (0.39) 3.31 (0.50) 0.034 2.411 1.070 5.429 T4(ug/dl) 3.23 (0.43) 3.21 (0.39) 3.31 (0.50) 0.603 0.960 0.823 1.119 FT4(ng/dl) 1.19 (0.22) 1.17 (0.22) 1.25 (0.25) 0.086 3.874 0.825 18.192 TSH(uIU/ml) 2.15 (1.70) 2.27 (1.82) 2.04 (1.31) 0.136 0.845 0.678 1.054 TgAb(IU/ml) 2.30 (24.65) 2.30 (22.62) 2.10 (34.60) 0.115 1.002 1.000 1.004 TPOAb(IU/ml) 46.40 (61.75) 48.40 (73.80) 45.50 (37.90) 0.764 1.000 0.999 1.001 Tg(ng/ml) 14.61 (19.64) 14.52 (20.76) 15.20 (14.26) 0.676 0.998 0.988 1.008 Copper(mg/L) 0.81 (0.42) 0.78 (0.39) 0.99 (0.52) 0.005 4.235 1.546 11.601 Zinc(mg/L) 5.41 (1.39) 5.33 (1.25) 5.49 (1.78) 0.536 0.948 0.802 1.121 Cadmium(mg/L) 50.49 (14.52) 50.96 (13.01) 49.97 (13.84) 0.726 0.996 0.975 1.018 Magnesium (mg/L) 42.87 (9.68) 42.76 (9.34) 42.87 (10.73) 0.342 1.017 0.982 1.054 Iron(mg/L) 633.98 (161.64) 634.00 (174.47) 627.30 (137.65) 0.887 1.000 0.998 1.002 Risk Factor Selection The LASSO algorithm screened 24 clinically relevant risk factors, identifying 11 predictors with non-zero coefficients: age, smoking status, alcohol consumption, height, maximum tumor diameter, Hashimoto thyroiditis (HT), FT3, FT4, TSH, TgAb, and blood copper (Fig. 2 ). These predictors were incorporated into a multivariate logistic regression model, with detailed results presented in Table 3 . Age demonstrated a protective effect against CLNM (OR: 0.945; 95% CI: 0.911–0.979), with each additional year corresponding to a 5.5% decreased risk. Conversely, elevated TgAb (OR: 1.005; 95% CI: 1.002–1.007) and blood copper (OR: 5.882; 95% CI: 1.865–18.550) significantly increased CLNM risk. Each unit increase in TgAb level was associated with a 0.5% risk elevation, while elevated blood copper conferred a 5.88-fold increased risk. Table 3 Introduction of 11 risk factors into the results of the multivariate logistic regression model Characteristics β P OR 95%CI lower 95%CI upper Age -0.057 0.002 0.945 0.911 0.979 Smoking No Yes 0.542 0.369 1.719 0.527 5.609 Drinking No Yes 0.352 0.636 1.422 0.331 6.106 Height 0..032 0.221 1.033 0.981 1.087 Diameter 0.983 0.213 2.671 0.569 12.534 Hashimoto No Yes -0.799 0.058 0.450 0.197 1.027 FT3 0.391 0.455 1.479 0.530 4.130 FT4 0.492 0.626 1.635 0.227 11.807 TSH -0.081 0.526 0.922 0.718 1.184 TgAb 0.005 0.001 1.005 1.002 1.007 Copper 1.772 0.003 5.882 1.865 18.550 Prediction Nomogram for CLNM in cN0 PTMC Patients The 11 risk factors selected by Lasso regression form a column diagram (Fig. 3 ). A vertical line is drawn from the value of each variable to the line of the upper point to obtain the score for each variable. The scores of all variables are summed to get the total score. This total score is located on the total score line. A vertical line is drawn down from that point to intersect with the diagnostic probability line, obtaining the corresponding CLNM probability. Assuming a patient is 45 years old, has no smoking history, no drinking history, is 160 cm tall, has a maximum tumour diameter of 0.4 cm, has HT, with FT3 value of 3.5 pg/ml, FT4 value of 1.2 ng/dl, TSH value of 3.0 uIU/ml, TgAb value of 600 IU/ml, and blood copper content of 0.8 mg/L, the total score for this patient is 267. The CLNM diagnostic probability for this patient is 0.81. Nomogram Validation and Clinical Utility The ROC curve of the internal validation test is shown in Fig. 4 (A) and 4(B). The AUC of the training cohort (number of seeds: 11) is 0.784 (cutoff value: 0.229, sensitivity: 0.881, and specificity: 0.553), and the AUC of the validation cohort (number of seeds: 11) is 0.783 (cutoff value: 0.339, sensitivity: 0.750, and specificity: 0.709). This indicates good discrimination. Internal validation was performed using Bootstrap resampling, and in both the training and validation sets, the predicted values on the calibration curve were highly consistent with the actual values (Fig. 5 ), indicating good model calibration. The Hosmer–Lemeshow test showed good goodness of fit ( P = 0.5325), indicating good calibration ability. The clinical decision curves for the training and validation cohorts are shown in Fig. 6 . The nomogram demonstrates good clinical utility in both the training and validation cohorts, indicating that PTMC patients can benefit from the nomogram to predict the risk of CLNM. Model Comprision Remove the copper-related indicators from the 11 risk factors in the final model, refit it using the remaining risk factors, calculate the predicted values and plot the ROC curve. The AUC of the model after excluding copper-related indicators is 0.716, lower than the original model's 0.784 (Fig. 7 (A)). The P value is 0.041, indicating a statistically significant difference. Stepwise regression was used to select variables, including age, TgAb, and blood copper. The model was fitted and the predicted values were calculated, and the ROC curve was plotted and compared with the lasso model (Fig. 7 (B)). The AUC of the stepwise regression model was 0.716, lower than the AUC of the lasso model (0.784) ( P = 0.022). Most previous articles predicted the results of CLNM based on single indicators, so we plotted a Figure(Fig. 8 ) showing the ROC curves of the original model and multiple single factors combined to predict CLNM, which shows that the AUC of our model is greater than the AUC of other single risk factors. Discussion Existing guidelines and consensus documents showed persistent disagreement regarding the indication for PCLND in patients who present with radiologically negative central lymph nodes. Some scholars contended that PCLND in PTMC patients increased the risks of recurrent laryngeal nerve and parathyroid gland injury without significantly improving long-term survival[ 12 ]. Conversely, other scholars maintained that omitting PCLND increased recurrence risk and necessitated reoperation – which could further elevate parathyroid injury rates, thereby hindering patient recovery[ 13 ]. In this study, PCLND was performed on 333 cN0 PTMC patients. Histopathology confirmed a CLNM rate of 34.5% (115/333), which aligned with previous research[ 2 ]. The study employed Lasso regression for variable selection, which mitigated the overfitting risk inherent in traditional stepwise regression methods and significantly improved predictive performance (AUC = 0.784). Among the 11 risk factors, blood copper emerged as a significantly independent predictor of CLNM (OR: 5.882, 95% CI: 1.865–18.550), where elevated levels were associated with a markedly increased risk. This finding aligned with contemporary research on copper's role in the thyroid tumor microenvironment. In a 2012 study, Kosova et al observed elevated serum copper levels in thyroid cancer patients (131.61 ± 33.9 µg/dL) versus those with nodular goiter (84.75 ± 12.1 µg/dL)[ 14 ]. In 2015, a meta-analysis demonstrated elevated serum copper levels in the serum of thyroid cancer patients relative to healthy controls(95%CI = (0.945, 3.799), P = 0.001)[ 15 ].This work represented the first incorporation of blood copper into a CLNM prediction model for cN0 PTMC, providing clinical evidence for investigating the link between copper metabolism and thyroid cancer behavior. The negative association between age and CLNM risk (OR = 0.945, 95% CI: 0.911–0.979) indicated that younger patients should receive heightened vigilance for CLNM. Both cigarette smoking and drinking showed positive associations with CLNM occurrence, which was consistent with findings from a UK Biobank prospective cohort study[ 16 ]. That cohort study reported that maintaining healthy lifestyle practices (e.g., smoking abstinence and limited alcohol intake) reduces thyroid cancer risk, even in individuals with high genetic predisposition.The inclusion of height—a factor rarely examined in previous CLNM studies—potentially reflected underlying differences in growth factor signaling pathways and warranted further validation. Notably, our data revealed that HT exhibited a negative association with CLNM (OR = 0.450), whereas thyroglobulin antibody (TgAb) levels showed a positive association (OR = 1.005). This observed pattern corresponded with established literature. Research has shown that patients with thyroid carcinoma concomitant with HT tend to have a more favorable prognosis, which may be attributed to the heightened immune response present in HT, potentially suppressing tumor growth[ 17 , 18 ]. In a retrospective analysis of 1,171 patients with differentiated thyroid carcinoma (DTC), Jo et al. reported a positive correlation between elevated TgAb levels and an increased rate of lymph node metastasis[ 19 ]. Previous studies have predominantly investigated HT and TgAb in isolation, with limited discussion integrating both factors within a single analysis. This incomplete correspondence between TgAb and HT reflected the heterogeneity of thyroid autoimmune diseases. These findings underscored the necessity of integrating multidimensional data in clinical practice to avoid overreliance on a single antibody marker. We performed comparative analyses across multiple models. The model incorporating copper metabolism indicators achieved an AUC of 0.784, whereas the model constructed without copper-related indicators (including blood copper, FT3, FT4, TSH, and TgAb) yielded an AUC of 0.712. This result indicated that the inclusion of copper metabolism-related indicators significantly enhanced the model's discriminative ability. The ROC curve, which included both individual risk factors and our multivariate model, further illustrated the superior predictive performance of our model. Additionally, we compared our model with existing predictive models for CLNM in PTMC. Using the PubMed search query: ((papillary thyroid microcarcinoma) AND (model) AND (central) AND (lymph node metastasis)) NOT (BRAF), we identified 71 relevant articles. As BRAF V600E testing typically requires invasive biopsy, models incorporating this marker were excluded from comparison. After excluding studies that involved papillary thyroid carcinoma (rather than PTMC), those without clinical prediction models, and other non-compliant literature, 31 articles presenting clinical prediction models remained. Among these, 13 studies reported models with AUC values exceeding 0.784[ 20 – 32 ]. Notably, nine of these articles employed machine learning algorithms or artificial neural networks, which often lack simplicity and clinical feasibility[ 20 , 22 , 23 , 25 , 26 , 29 – 32 ]. This comparison underscored the significant advantages of our model. However, this study had several limitations. Its retrospective design employed strict inclusion/exclusion criteria screening, yet potential selection bias remains. Additionally, the limited sample size may constrain generalizability to broader populations. To address these issues, our research team plans to validate, optimize, and refine the prediction model through multi-center, large-sample prospective studies, thereby enhancing its clinical applicability and accuracy. Conclusion We established a nomogram model incorporating blood copper levels and clinical characteristics to predict CLNM risk in cN0 PTMC patients. We recommend that clinicians carefully evaluate the risk factors identified in the model to guide prophylactic central lymph node dissection (PCLND) decisions. During surgery, meticulous central compartment dissection should be performed to minimize recurrence/metastasis risk and improve long-term prognosis. Declarations Ethical Approval This retrospective study received ethical approval from the Ethics Review Committee of Inner Mongolia People’s Hospital (Approval No: 2021003). Consent to Participate Informed consent was obtained from all individual participants included in the study. Consent to Publish The authors afrm that human research participants provided informed consent for the publication of the data in Figs. 1, 2, 3, 4, 5, 6,7 and 8. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was supported by the Project of the Health Commission of Inner Mongolia Autonomous. Author Ya-Hang Liu has received research support from Project of the Health Commission of Inner Mongolia Autonomous.Region (ID: 202202020) and the Major Science and Technology Project of Inner Mongolia Autonomous Region (ID: 2021ZD0014). Author Contribution All authors contributed to the study conception and design. Designed the study, developed the nomogram model, and wrote the main manuscript text, Yi-Wei Zhang and Heng-Feng Zhang.Collected and annotated clinical data, NA An. Evaluated pathological characteristics, Yan Li. Performed statistical analysis and model validation, Huan Wang.Processed imaging features, Cui-Cui Ma.Supervised methodology, Rui-Fang Guo. Secured funding and critically revised the manuscript, Ya-Hang Liu.All authors reviewed and approved the final version. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. References Siegel RL, Miller KD, Jemal A, Cancer statistics (2018) CA: a cancer journal for clinicians. 2018;68:7–30 Liu L-S, Liang J, Li J-H, Liu X, Jiang L, Long J-X et al (2017) The incidence and risk factors for central lymph node metastasis in cN0 papillary thyroid microcarcinoma: a meta-analysis. European archives of oto-rhino-laryngology: official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology -. 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J Clin Med. ;11 Zhou W, Li L, Hao X, Wu L, Liu L, Zheng B et al (2025) Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning. Front Endocrinol 16:1537386 Wu L, Zhou Y, Li L, Ma W, Deng H, Ye X (2024) Application of ultrasound elastography and radiomic for predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma. Front Oncol 14:1354288 Gao L, Wen X, Yue G, Wang H, Lu Z, Wu B et al (2025) The Predictive Value of a Nomogram Based on Ultrasound Radiomics, Clinical Factors, and Enhanced Ultrasound Features for Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma. Ultrason Imaging 47:93–103 Liu J, Yu J, Wei Y, Li W, Lu J, Chen Y et al (2024) Ultrasound radiomics signature for predicting central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma. Thyroid Res 17:4 Huang X, Zhang Y, He D, Lai L, Chen J, Zhang T et al (2022) Machine Learning-Based Shear Wave Elastography Elastic Index (SWEEI) in Predicting Cervical Lymph Node Metastasis of Papillary Thyroid Microcarcinoma: A Comparative Analysis of Five Practical Prediction Models. Cancer Manage Res 14:2847–2858 Liu X, Li H, Zhang L, Gao Q, Wang Y (2025) Development and validation of a multidimensional machine learning-based nomogram for predicting central lymph node metastasis in papillary thyroid microcarcinoma. Gland Surg 14:344–357 Ozden S, Er S, Saylam B, Yildiz BD, Senol K, Tez M (2018) A comparison of logistic regression and artificial neural networks in predicting central lymph node metastases in papillary thyroid microcarcinoma. Ann Ital Chir 89:193–198 Zhao Y, Fu J, Liu Y, Sun H, Fu Q, Zhang S et al (2023) 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. Gland Surg 12:1722–1734 Additional Declarations No competing interests reported. Supplementary Files EthicsStatement.jpg ResponseLetter.docx 1670615SNConsentformforpublication.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 28 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers invited by journal 17 Apr, 2026 Editor assigned by journal 14 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 01 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9292047","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628984818,"identity":"aa9de935-2256-4b06-800b-5828ed7e3684","order_by":0,"name":"Yi-Wei Zhang","email":"","orcid":"","institution":"Inner Mongolia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi-Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":628984819,"identity":"aa20e2ec-084e-4429-b7b8-33128f9952ad","order_by":1,"name":"Heng-Feng Zhang","email":"","orcid":"","institution":"Inner Mongolia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Heng-Feng","middleName":"","lastName":"Zhang","suffix":""},{"id":628984820,"identity":"a5ad9a7b-6ca2-42cc-a18d-5c224c56813c","order_by":2,"name":"Rui-Fang Guo","email":"","orcid":"","institution":"Inner Mongolia People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rui-Fang","middleName":"","lastName":"Guo","suffix":""},{"id":628984821,"identity":"39d174ac-ad58-4734-a629-56293c969c29","order_by":3,"name":"Huan Wang","email":"","orcid":"","institution":"Inner Mongolia People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Wang","suffix":""},{"id":628984822,"identity":"4a81cf4b-1d02-4b8b-9c49-0176c073adf1","order_by":4,"name":"Cui-Cui Ma","email":"","orcid":"","institution":"Inner Mongolia People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cui-Cui","middleName":"","lastName":"Ma","suffix":""},{"id":628984823,"identity":"1a7ac1ec-c541-4f89-8370-66a94755e307","order_by":5,"name":"Yan Li","email":"","orcid":"","institution":"Inner Mongolia People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""},{"id":628984824,"identity":"88718d0a-7df5-4b89-95f9-66236db1a8a2","order_by":6,"name":"Na An","email":"","orcid":"","institution":"Inner Mongolia People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"An","suffix":""},{"id":628984825,"identity":"1a7f305a-ab64-4c33-aae7-bc170068745e","order_by":7,"name":"Ya-Hang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACNvb+xw8+VPznsb9/+ABxWvh4zrAZzjjDLMNwgy2BOC1yEjkM0pxtzDYMN3gMiHQYQ+4BY8Y2Nh7G2T0fb7xhsJPTbSCo5VzC44JzPDzMMmc3W85hSDY2O0BIC2ODgfGMMgkeoHXbpHkYDiRuI6iFmcFAmofNgIeHIecZkVrYeIBa2hJ4JCRy2IjUwsOWBgzkAzwGPMeMLecYEOEX+fmPDwOj8oC9AXvzwxtvKuzkCGpBARLERg2yFlJ1jIJRMApGwYgAAANNPbZkcdfqAAAAAElFTkSuQmCC","orcid":"","institution":"Inner Mongolia Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ya-Hang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-04-01 12:40:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9292047/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9292047/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107916220,"identity":"fe7f8e1c-a34c-49d1-9e95-389a8cf4b129","added_by":"auto","created_at":"2026-04-27 14:13:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":862814,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient inclusion and exclusion criteria for cN0 PTMC. Independent risk factors were identified from the training cohort and used to construct a nomogram. The model’s predictive accuracy was subsequently validated in the testing cohort\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/33fbcfc873710c35811b3363.jpg"},{"id":108006711,"identity":"e0a0b27f-5dec-4d2e-858d-f371e89078c4","added_by":"auto","created_at":"2026-04-28 12:56:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":763065,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics selection using the LASSO logistic regression model. (A) Selection of the optimal parameter (logλ) in the lasso model. (B) The x-axis is the tuning parameter logλ, and the y-axis is the binomial deviance\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/fd1add7a3ff16d1397365771.jpg"},{"id":108006508,"identity":"81944303-b09a-4713-ab0e-28df76927f99","added_by":"auto","created_at":"2026-04-28 12:55:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":940821,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram predicting cNo PTMC patients’ CLNM\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/34af84a711a6c058da6f2150.jpg"},{"id":107916223,"identity":"d3a41fff-a449-4a71-a3d6-f43ad49024fe","added_by":"auto","created_at":"2026-04-27 14:13:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":669749,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve and optimal cutoff value between the training and testing cohorts. (A) The ROC curve of the training cohort. (B) The ROC curve of the testing cohort\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/bd6701c7acfbdd040efbccac.jpg"},{"id":107916221,"identity":"87334f82-7ef4-459b-ad04-dc2eb9a7af74","added_by":"auto","created_at":"2026-04-27 14:13:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":891379,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve between training and testing cohorts. (A) Training cohort calibration curve. (B) Testing cohort calibration curve\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/1f09965003907f5da2f009e4.jpg"},{"id":108007286,"identity":"4ed99da0-e3cb-421b-b5f9-9229d34dab2d","added_by":"auto","created_at":"2026-04-28 12:59:22","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":687655,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curve between training and testing cohorts. (A) Training cohort DCA curve. (B) Testing cohort DCA curve\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/2379f4e2b0dcbae379a19c78.jpg"},{"id":108006509,"identity":"ffa7b42f-c519-4892-b707-93828c9e705c","added_by":"auto","created_at":"2026-04-28 12:55:48","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":593214,"visible":true,"origin":"","legend":"\u003cp\u003eModel comparison.(A) Lasso model vs. copper metabolism deleted model.(B) Lasso model vs. stepwise model\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/1de9d4745641e76341145c90.jpg"},{"id":107916224,"identity":"2bd306c5-dba2-4144-af2f-73eaa107df5d","added_by":"auto","created_at":"2026-04-27 14:13:18","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1493296,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves of the original model and multiple single factors\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/b8dd907f054c757b5db07db5.jpg"},{"id":108803469,"identity":"cc55daef-834f-4cba-9442-b48ab9339fa9","added_by":"auto","created_at":"2026-05-08 14:55:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7345111,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/5b1fac6f-f26e-4dba-8a92-e70d534e54e6.pdf"},{"id":107916219,"identity":"f5164f21-ccaf-4692-9324-510a9d661ceb","added_by":"auto","created_at":"2026-04-27 14:13:18","extension":"jpg","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":44866,"visible":true,"origin":"","legend":"","description":"","filename":"EthicsStatement.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/02754862317c23ef7a9f74ff.jpg"},{"id":108007530,"identity":"77ab7f8f-2355-443b-9895-1ccd0b8d69e1","added_by":"auto","created_at":"2026-04-28 13:00:23","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15486,"visible":true,"origin":"","legend":"","description":"","filename":"ResponseLetter.docx","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/22360d349412b0146e761495.docx"},{"id":108006798,"identity":"dbb5815c-5b77-4996-9e2a-95e818599b74","added_by":"auto","created_at":"2026-04-28 12:57:21","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":766150,"visible":true,"origin":"","legend":"","description":"","filename":"1670615SNConsentformforpublication.docx","url":"https://assets-eu.researchsquare.com/files/rs-9292047/v1/09974687d285d2b33ee4baf6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Central Lymph Node Metastasis for cN0 Papillary Thyroid Microcarcinoma: a Nomogram Model Based on Blood Copper and Clinical Characteristics","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePapillary Thyroid Microcarcinoma (PTMC) is defined as papillary thyroid carcinoma with a maximum tumor diameter of \u0026le;\u0026thinsp;1 cm. As a major subtype of thyroid cancer (TC), PTMC currently comprises over 50% of newly diagnosed TC cases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, PTMC should not be equated with early-stage or low-risk cancer. It shares the same biological behavior as larger papillary thyroid carcinomas, including the potential for extrathyroidal extension and cervical lymph node metastasis\u0026mdash;among which central lymph node metastasis (CLNM) is the most common. Even in clinically lymph node-negative (cN0) PTMC patients with no preoperative evidence of lymph node involvement, approximately 15\u0026ndash;64% are found to have CLNM upon postoperative histopathology. CLNM is closely associated with postoperative recurrence and poorer prognosis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Currently, the necessity of prophylactic central lymph node dissection (PCLND) in cN0 PTMC remains controversial. Thus, accurate, non-invasive preoperative assessment of CLNM represents a critical challenge in clinical management.\u003c/p\u003e \u003cp\u003eCurrent imaging assessment methods exhibit significant limitations. While high-resolution ultrasound (US) serves as the preferred screening modality, its diagnostic accuracy for CLNM remains suboptimal, with literature reporting sensitivities of 23\u0026ndash;38% and specificities of 90\u0026ndash;97%[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Ultrasound-guided fine-needle aspiration cytology (FNAC) offers an accessible, cost-effective, and minimally invasive approach for evaluating cervical lymph node lesions, and is consequently widely utilized in clinical practice. Nevertheless, sole reliance on FNAC presents inherent limitations: diagnostic outcomes may be compromised by operator experience, sampling site selection, and tumor heterogeneity, resulting in variable accuracy. Furthermore, as an invasive procedure, FNAC frequently encounters patient reluctance.\u003c/p\u003e \u003cp\u003eTrace element profiling\u0026mdash;particularly for copper\u0026mdash;demonstrates considerable potential as a novel cancer biomarker. Copper acts as an essential cofactor for metastatic regulators (including lysyl oxidase (LOX) and vascular endothelial growth factor (VEGF)), mediating tumor microenvironment remodeling through activation of signaling pathways involving collagen crosslinking and angiogenesis. This process critically influences tumor recurrence and metastasis[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Elevated copper levels are well-documented in both tumor tissue and serum across multiple malignancies (e.g., breast, lung, gastric, thyroid, and prostate cancers)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A 2023 prospective cohort study specifically confirmed that elevated blood copper significantly increases gastric cancer lymph node metastasis risk (OR\u0026thinsp;=\u0026thinsp;2.4, 95% CI 1.6\u0026ndash;3.6)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, copper, as an essential constituent of superoxide dismutase, participates in the synthesis, activation, and metabolism of thyroid hormones. Abnormal copper metabolism may interact with thyroid function indicators (free triiodothyronine, free thyroxine, Thyroid stimulating hormone) and autoimmune markers (thyroglobulin antibody), collectively influencing the risk of lymph node metastasis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Zinc and iron are involved in regulating oxidative stress and DNA repair pathways, and their dysregulation has been associated with tumor aggressiveness[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the integration of trace elements into clinical prediction models remains unexplored.\u003c/p\u003e \u003cp\u003eAddressing the clinical challenge of identifying CLNM in cN0 PTMC patients where current diagnostic approaches show significant limitations - our study responds to the paucity of validated predictive models. We therefore developed and validated a novel integrated risk stratification tool using trace elements and established clinicopathological risk factors. By analyzing blood copper levels, clinical data, and pathological characteristics of cN0 PTMC patients, we constructed a clinically applicable visual nomogram for CLNM prediction. This evidence-based model outperforms traditional methods and establishes a preoperative decision-support system to guide personalized surgical management of central lymph node metastasis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003e This retrospective study received ethical approval from the Ethics Review Committee of the People's Hospital of Inner Mongolia Autonomous Region (Approval No: 2021003). All participants provided written informed consent authorizing the use of their blood test results and clinicopathological data. Data collection spanned 2020 to 2024. Inclusion criteria: 1.cN0 PTMC diagnosis confirmed; 2.Scheduled for thyroidectomy with central compartment lymph node dissection at our institution.\u003c/p\u003e \u003cp\u003eExclusion criteria: 1.Surgical contraindications; 2.Current trace element supplementation; 3.History of other thyroid disorders; 4.Abnormal hepatic or renal function. All enrolled PTMC patients underwent central compartment lymph node dissection. Central lymph node metastasis (CLNM) was histopathologically confirmed when \u0026ge;\u0026thinsp;1 metastatic lymph node was identified; otherwise, cases were classified as non-CLNM (NCLNM). A total of 333 patients were randomized into training (n\u0026thinsp;=\u0026thinsp;199) and validation (n\u0026thinsp;=\u0026thinsp;134) cohorts at a 6:4 ratio. Univariate logistic regression identified potential predictors in the training cohort, followed by predictive model construction using LASSO regression. Model performance was subsequently validated in the independent cohort. The participant selection process is detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Collection\u003c/h3\u003e\n\u003cp\u003ePatients were instructed to maintain normal dietary habits before blood collection, while avoiding copper, zinc, calcium, magnesium, and iron-rich foods and supplements. Following a 12-hour fasting period, participants abstained from alcohol and medications. Venous blood (2000 \u0026micro;L) was collected, and serum was separated via centrifugation (3000 rpm, 10 min). Aliquots (150 \u0026micro;L) were cryopreserved at \u0026minus;\u0026thinsp;80\u0026deg;C. Clinicopathological characteristics of cN0 PTMC and patient demographics were extracted from electronic medical records.\u003c/p\u003e\n\u003ch3\u003eTest Methods\u003c/h3\u003e\n\u003cp\u003eBlood copper, zinc, calcium, magnesium, and iron concentrations were quantified using atomic absorption spectrophotometry. Thyroid function was assessed via chemiluminescence immunoassay, measuring: Triiodothyronine (T3) Free triiodothyronine (FT3) Thyroxine (T4) Free thyroxine (FT4) Thyroid-stimulating hormone (TSH) Thyroglobulin antibody (TgAb) Thyroid peroxidase antibody (TPOAb) Thyroglobulin (TG).\u003c/p\u003e \u003cp\u003eReference ranges were as follows: T3: 0.60 to 1.81 ng/ml, FT3: 1.50 to 4.10 pg/ml, T4: 4.50 to 10.90 ug/dl, FT4: 0.8 to 1.80 ng/dl; TSH: 0.4 to 5.00 uIU/ml, TGAb: 0.00 to 4.50 IU/mL, TPOAb: 0.00 to 60.00 IU/mL, TG: 0.83 to 68.30 ng/mL. The normal range for blood copper concentration is 0.7 to 1.4 mg/L.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range), while categorical variables are summarized using frequencies and percentages. Intergroup differences were assessed using chi-square tests (for categorical variables) and analysis of variance (ANOVA, for continuous variables). Univariate logistic regression identified potential predictors of central lymph node metastasis (CLNM).The LASSO regression method was implemented to select optimal predictors and construct a dynamic nomogram for CLNM risk stratification in cN0 PTMC patients. Significant variables identified through LASSO regression were subsequently incorporated into a multivariate logistic regression model to develop the final nomogram. Model discrimination was evaluated by calculating the area under the receiver operating characteristic curve (AUC-ROC). Calibration performance was assessed using bootstrap validation with 1,000 resamples. Clinical utility was quantified through decision curve analysis (DCA), with both internal and external validation performed.\u003c/p\u003e \u003cp\u003eTo further investigate the contribution of copper metabolism biomarkers to our predictive models, we compared the area under the curve (AUC) values between models incorporating these indicators versus those excluding them. Stepwise regression was subsequently employed to develop alternative models, with performance evaluated through comparative AUC analysis. Existing models from published literature were benchmarked against our selected configurations, with the DeLong test determining the optimal model. All statistical analyses were performed using R software (v4.2.2; R Foundation) and SPSS Statistics (v26.0; IBM). Statistical significance was defined at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eA total of 333 cN0 PTMC patients participated in this study. They were randomly divided into a training (199) and validation (134) cohort. No significant differences were found between the two groups. The baseline characteristics of the patients and the binary logistic regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The \u003cem\u003eP\u003c/em\u003e values for sex, age, height, smoking, FT3, and blood copper were all \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the patients and the results of the univariate logistic regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;199)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNCLNM\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCLNM\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003cp\u003elower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003cp\u003eupper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159 (79.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111 (84.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48 (71.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (20.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (15.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (28.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.00 (13.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.00 (12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.00 (16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.00 (15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.50 (15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.00 (19.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163.00 (8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161.00 (9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165.00 (8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174 (87.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121 (91.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53 (79.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (12.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (8.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (20.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183 (91.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125 (94.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58 (86.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (8.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (5.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (13.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnilateral or Bilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e152 (76.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103 (78.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49 (73.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (23.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (21.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (26.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfringe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169 (84.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111 (84.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58 (86.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (15.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (15.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (13.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultifocality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128 (64.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86 (65.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (62.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (35.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (34.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (37.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHashimoto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131 (65.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82 (62.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49 (73.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68 (34.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (37.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (26.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiameter(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLaboratory of the patients and the results of the univariate logistic regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;199)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNCLNM\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCLNM\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003cp\u003elower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003cp\u003eupper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT3(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.23 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.21 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31 (0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4(ug/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.23 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.21 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31 (0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT4(ng/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH(uIU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15 (1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27 (1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.04 (1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTgAb(IU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.30 (24.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.30 (22.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.10 (34.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPOAb(IU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.40 (61.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.40 (73.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.50 (37.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTg(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.61 (19.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.52 (20.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.20 (14.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopper(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.41 (1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.33 (1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.49 (1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadmium(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.49 (14.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.96 (13.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.97 (13.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.87 (9.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.76 (9.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.87 (10.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e633.98 (161.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e634.00 (174.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e627.30 (137.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk Factor Selection\u003c/h3\u003e\n\u003cp\u003eThe LASSO algorithm screened 24 clinically relevant risk factors, identifying 11 predictors with non-zero coefficients: age, smoking status, alcohol consumption, height, maximum tumor diameter, Hashimoto thyroiditis (HT), FT3, FT4, TSH, TgAb, and blood copper (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These predictors were incorporated into a multivariate logistic regression model, with detailed results presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Age demonstrated a protective effect against CLNM (OR: 0.945; 95% CI: 0.911\u0026ndash;0.979), with each additional year corresponding to a 5.5% decreased risk. Conversely, elevated TgAb (OR: 1.005; 95% CI: 1.002\u0026ndash;1.007) and blood copper (OR: 5.882; 95% CI: 1.865\u0026ndash;18.550) significantly increased CLNM risk. Each unit increase in TgAb level was associated with a 0.5% risk elevation, while elevated blood copper conferred a 5.88-fold increased risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIntroduction of 11 risk factors into the results of the multivariate logistic regression model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003cp\u003elower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003cp\u003eupper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0..032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHashimoto\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTgAb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePrediction Nomogram for CLNM in cN0 PTMC Patients\u003c/h3\u003e\n\u003cp\u003eThe 11 risk factors selected by Lasso regression form a column diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A vertical line is drawn from the value of each variable to the line of the upper point to obtain the score for each variable. The scores of all variables are summed to get the total score. This total score is located on the total score line. A vertical line is drawn down from that point to intersect with the diagnostic probability line, obtaining the corresponding CLNM probability. Assuming a patient is 45 years old, has no smoking history, no drinking history, is 160 cm tall, has a maximum tumour diameter of 0.4 cm, has HT, with FT3 value of 3.5 pg/ml, FT4 value of 1.2 ng/dl, TSH value of 3.0 uIU/ml, TgAb value of 600 IU/ml, and blood copper content of 0.8 mg/L, the total score for this patient is 267. The CLNM diagnostic probability for this patient is 0.81.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNomogram Validation and Clinical Utility\u003c/h2\u003e \u003cp\u003eThe ROC curve of the internal validation test is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(A) and 4(B). The AUC of the training cohort (number of seeds: 11) is 0.784 (cutoff value: 0.229, sensitivity: 0.881, and specificity: 0.553), and the AUC of the validation cohort (number of seeds: 11) is 0.783 (cutoff value: 0.339, sensitivity: 0.750, and specificity: 0.709). This indicates good discrimination. Internal validation was performed using Bootstrap resampling, and in both the training and validation sets, the predicted values on the calibration curve were highly consistent with the actual values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), indicating good model calibration. The Hosmer\u0026ndash;Lemeshow test showed good goodness of fit (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5325), indicating good calibration ability. The clinical decision curves for the training and validation cohorts are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The nomogram demonstrates good clinical utility in both the training and validation cohorts, indicating that PTMC patients can benefit from the nomogram to predict the risk of CLNM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModel Comprision\u003c/h2\u003e \u003cp\u003eRemove the copper-related indicators from the 11 risk factors in the final model, refit it using the remaining risk factors, calculate the predicted values and plot the ROC curve. The AUC of the model after excluding copper-related indicators is 0.716, lower than the original model's 0.784 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(A)). The \u003cem\u003eP\u003c/em\u003e value is 0.041, indicating a statistically significant difference.\u003c/p\u003e \u003cp\u003eStepwise regression was used to select variables, including age, TgAb, and blood copper. The model was fitted and the predicted values were calculated, and the ROC curve was plotted and compared with the lasso model (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(B)). The AUC of the stepwise regression model was 0.716, lower than the AUC of the lasso model (0.784) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022). Most previous articles predicted the results of CLNM based on single indicators, so we plotted a Figure(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) showing the ROC curves of the original model and multiple single factors combined to predict CLNM, which shows that the AUC of our model is greater than the AUC of other single risk factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e Existing guidelines and consensus documents showed persistent disagreement regarding the indication for PCLND in patients who present with radiologically negative central lymph nodes. Some scholars contended that PCLND in PTMC patients increased the risks of recurrent laryngeal nerve and parathyroid gland injury without significantly improving long-term survival[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conversely, other scholars maintained that omitting PCLND increased recurrence risk and necessitated reoperation \u0026ndash; which could further elevate parathyroid injury rates, thereby hindering patient recovery[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this study, PCLND was performed on 333 cN0 PTMC patients. Histopathology confirmed a CLNM rate of 34.5% (115/333), which aligned with previous research[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study employed Lasso regression for variable selection, which mitigated the overfitting risk inherent in traditional stepwise regression methods and significantly improved predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.784). Among the 11 risk factors, blood copper emerged as a significantly independent predictor of CLNM (OR: 5.882, 95% CI: 1.865\u0026ndash;18.550), where elevated levels were associated with a markedly increased risk. This finding aligned with contemporary research on copper's role in the thyroid tumor microenvironment. In a 2012 study, Kosova et al observed elevated serum copper levels in thyroid cancer patients (131.61\u0026thinsp;\u0026plusmn;\u0026thinsp;33.9 \u0026micro;g/dL) versus those with nodular goiter (84.75\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1 \u0026micro;g/dL)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In 2015, a meta-analysis demonstrated elevated serum copper levels in the serum of thyroid cancer patients relative to healthy controls(95%CI = (0.945, 3.799), P\u0026thinsp;=\u0026thinsp;0.001)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].This work represented the first incorporation of blood copper into a CLNM prediction model for cN0 PTMC, providing clinical evidence for investigating the link between copper metabolism and thyroid cancer behavior.\u003c/p\u003e \u003cp\u003eThe negative association between age and CLNM risk (OR\u0026thinsp;=\u0026thinsp;0.945, 95% CI: 0.911\u0026ndash;0.979) indicated that younger patients should receive heightened vigilance for CLNM. Both cigarette smoking and drinking showed positive associations with CLNM occurrence, which was consistent with findings from a UK Biobank prospective cohort study[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. That cohort study reported that maintaining healthy lifestyle practices (e.g., smoking abstinence and limited alcohol intake) reduces thyroid cancer risk, even in individuals with high genetic predisposition.The inclusion of height\u0026mdash;a factor rarely examined in previous CLNM studies\u0026mdash;potentially reflected underlying differences in growth factor signaling pathways and warranted further validation. Notably, our data revealed that HT exhibited a negative association with CLNM (OR\u0026thinsp;=\u0026thinsp;0.450), whereas thyroglobulin antibody (TgAb) levels showed a positive association (OR\u0026thinsp;=\u0026thinsp;1.005). This observed pattern corresponded with established literature. Research has shown that patients with thyroid carcinoma concomitant with HT tend to have a more favorable prognosis, which may be attributed to the heightened immune response present in HT, potentially suppressing tumor growth[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In a retrospective analysis of 1,171 patients with differentiated thyroid carcinoma (DTC), Jo et al. reported a positive correlation between elevated TgAb levels and an increased rate of lymph node metastasis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Previous studies have predominantly investigated HT and TgAb in isolation, with limited discussion integrating both factors within a single analysis. This incomplete correspondence between TgAb and HT reflected the heterogeneity of thyroid autoimmune diseases. These findings underscored the necessity of integrating multidimensional data in clinical practice to avoid overreliance on a single antibody marker.\u003c/p\u003e \u003cp\u003eWe performed comparative analyses across multiple models. The model incorporating copper metabolism indicators achieved an AUC of 0.784, whereas the model constructed without copper-related indicators (including blood copper, FT3, FT4, TSH, and TgAb) yielded an AUC of 0.712. This result indicated that the inclusion of copper metabolism-related indicators significantly enhanced the model's discriminative ability. The ROC curve, which included both individual risk factors and our multivariate model, further illustrated the superior predictive performance of our model. Additionally, we compared our model with existing predictive models for CLNM in PTMC. Using the PubMed search query: ((papillary thyroid microcarcinoma) AND (model) AND (central) AND (lymph node metastasis)) NOT (BRAF), we identified 71 relevant articles. As BRAF V600E testing typically requires invasive biopsy, models incorporating this marker were excluded from comparison. After excluding studies that involved papillary thyroid carcinoma (rather than PTMC), those without clinical prediction models, and other non-compliant literature, 31 articles presenting clinical prediction models remained. Among these, 13 studies reported models with AUC values exceeding 0.784[\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Notably, nine of these articles employed machine learning algorithms or artificial neural networks, which often lack simplicity and clinical feasibility[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This comparison underscored the significant advantages of our model.\u003c/p\u003e \u003cp\u003eHowever, this study had several limitations. Its retrospective design employed strict inclusion/exclusion criteria screening, yet potential selection bias remains. Additionally, the limited sample size may constrain generalizability to broader populations. To address these issues, our research team plans to validate, optimize, and refine the prediction model through multi-center, large-sample prospective studies, thereby enhancing its clinical applicability and accuracy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe established a nomogram model incorporating blood copper levels and clinical characteristics to predict CLNM risk in cN0 PTMC patients. We recommend that clinicians carefully evaluate the risk factors identified in the model to guide prophylactic central lymph node dissection (PCLND) decisions. During surgery, meticulous central compartment dissection should be performed to minimize recurrence/metastasis risk and improve long-term prognosis.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003e This retrospective study received ethical approval from the Ethics Review Committee of Inner Mongolia People\u0026rsquo;s Hospital (Approval No: 2021003).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003eThe authors afrm that human research participants provided informed consent for the publication of the data in Figs.\u0026nbsp;1, 2, 3, 4, 5, 6,7 and 8.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Project of the Health Commission of Inner Mongolia Autonomous. Author Ya-Hang Liu has received research support from Project of the Health Commission of Inner Mongolia Autonomous.Region (ID: 202202020) and the Major Science and Technology Project of Inner Mongolia Autonomous Region (ID: 2021ZD0014).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Designed the study, developed the nomogram model, and wrote the main manuscript text, Yi-Wei Zhang and Heng-Feng Zhang.Collected and annotated clinical data, NA An. Evaluated pathological characteristics, Yan Li. Performed statistical analysis and model validation, Huan Wang.Processed imaging features, Cui-Cui Ma.Supervised methodology, Rui-Fang Guo. Secured funding and critically revised the manuscript, Ya-Hang Liu.All authors reviewed and approved the final version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A, Cancer statistics (2018) CA: a cancer journal for clinicians. 2018;68:7\u0026ndash;30\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu L-S, Liang J, Li J-H, Liu X, Jiang L, Long J-X et al (2017) The incidence and risk factors for central lymph node metastasis in cN0 papillary thyroid microcarcinoma: a meta-analysis. European archives of oto-rhino-laryngology: official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology -. Head Neck Surg 274:1327\u0026ndash;1338\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu LM, Gu HY, Qu XH, Zheng J, Zhang W, Yin Y et al (2012) The accuracy of ultrasonography in the preoperative diagnosis of cervical lymph node metastasis in patients with papillary thyroid carcinoma: A meta-analysis. Eur J Radiol 81:1798\u0026ndash;1805\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhokhar MT, Day KM, Sangal RB, Ahmedli NN, Pisharodi LR, Beland MD et al (2015) Preoperative High-Resolution Ultrasound for the Assessment of Malignant Central Compartment Lymph Nodes in Papillary Thyroid Cancer. Thyroid: official J Am Thyroid Association 25:1351\u0026ndash;1354\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing T, Zhang J, Xu H, Zhang X, Yang F, Shi Y et al (2023) In-depth understanding of higher-order genome architecture in orphan cancer. 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Nat Commun 14:6523\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLossow K, Renko K, Schwarz M, Schomburg L, Schwerdtle T, Kipp AP (2021) The Nutritional Supply of Iodine and Selenium Affects Thyroid Hormone Axis Related Endpoints in Mice. Nutrients. ;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Zhao H, Xu Z, Cheng X (2020) Zinc dysregulation in cancers and its potential as a therapeutic target. Cancer biology Med 17:612\u0026ndash;625\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRu Q, Li Y, Chen L, Wu Y, Min J, Wang F (2024) Iron homeostasis and ferroptosis in human diseases: mechanisms and therapeutic prospects. Signal Transduct Target therapy 9:271\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoque-Velasquez J, Resendiz-Nieves J, Jahromi BR, Baluszek S, Muhammad S, Colasanti R et al (2020) Long-term survival outcomes of pineal region gliomas. J Neurooncol 148:651\u0026ndash;658\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLib\u0026aacute;nsk\u0026yacute; P, Ad\u0026aacute;mek S, Broul\u0026iacute;k P, Fialov\u0026aacute; M, Kubinyi J, Lischke R et al (2017) Parathyroid Carcinoma in Patients that Have Undergone Surgery for Primary Hyperparathyroidism. In vivo (Athens, Greece). ;31:925\u0026ndash;930\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosova F, Cetin B, Akinci M, Aslan S, Seki A, Pirhan Y et al (2012) Serum copper levels in benign and malignant thyroid diseases. Bratisl Lek Listy 113:718\u0026ndash;720\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen F, Cai W-S, Li J-L, Feng Z, Cao J, Xu B (2015) The Association Between Serum Levels of Selenium, Copper, and Magnesium with Thyroid Cancer: a Meta-analysis. 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Gland Surg 12:1722\u0026ndash;1734\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biological-trace-element-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bter","sideBox":"Learn more about [Biological Trace Element Research](https://www.springer.com/journal/12011)","snPcode":"12011","submissionUrl":"https://submission.nature.com/new-submission/12011/3","title":"Biological Trace Element Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Nomogram Copper, cN0 Papillary thyroid microcarcinoma, Central lymph node metastasis","lastPublishedDoi":"10.21203/rs.3.rs-9292047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9292047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo develop and validate a clinical prediction model for central lymph node metastasis (CLNM) in patients with clinically lymph node-negative papillary thyroid microcarcinoma (cN0 PTMC). This retrospective study (2021-2024) enrolled 333 cN0 PTMC patients. Participants were randomly allocated at a 6:4 ratio to a training cohort (n=199) or a validation cohort (n=134). Based on postoperative pathological assessment of CLNM, the training cohort was stratified into non-CLNM (NCLNM, n = 132) and CLNM (n = 67) groups. Blood copper levels and clinical characteristics were systematically recorded. Logistic regression identified factors associated with CLNM, and Lasso regression selected key predictors to develop a nomogram for CLNM risk estimation. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) to assess discrimination, calibration, and clinical utility in both cohorts. The incidence of CLNM in patients with cN0 PTMC was 33.7%. The predictive model incorporated blood copper levels, age, smoking, drinking, height, maximum tumor diameter, Hashimoto's thyroiditis, free triiodothyronine, free thyroxine, thyroid-stimulating hormone (TSH), and thyroglobulin antibody (TgAb). It demonstrated an AUC of 0.784 (sensitivity 0.881, specificity 0.553) with excellent calibration. The calibration curve exhibited a slope close to 1, indicating excellent calibration. DCA showed consistently positive net benefits across risk thresholds of 0.34–0.94. Validation yielded an AUC of 0.783 (sensitivity 0.750, specificity 0.459). Exclusion of copper metabolism-related factors reduced the AUC to 0.716. The nomogram incorporating blood copper and clinical characteristics provided an effective tool for predicting CLNM in cN0 PTMC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration\u003c/strong\u003e ChiCTR2100054781.Registered 27 December 2021, for retrospectively registered trials.\u003c/p\u003e","manuscriptTitle":"Predicting Central Lymph Node Metastasis for cN0 Papillary Thyroid Microcarcinoma: a Nomogram Model Based on Blood Copper and Clinical Characteristics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 14:13:03","doi":"10.21203/rs.3.rs-9292047/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"262445544866233580662586055740156936169","date":"2026-04-28T08:29:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T12:07:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117089940234619211337014862007904728560","date":"2026-04-19T00:57:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T17:31:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-15T02:23:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T01:56:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biological Trace Element Research","date":"2026-04-01T12:33:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biological-trace-element-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bter","sideBox":"Learn more about [Biological Trace Element Research](https://www.springer.com/journal/12011)","snPcode":"12011","submissionUrl":"https://submission.nature.com/new-submission/12011/3","title":"Biological Trace Element Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"262e5885-ca4a-4163-ac8d-ee1ad75f167d","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T14:13:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 14:13:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9292047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9292047","identity":"rs-9292047","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}