To Develop and Validate a Nomogram Model for Predicting High Volume (＞5) Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma

To Develop and Validate a Nomogram Model for Predicting High Volume (＞5) Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma | 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 To Develop and Validate a Nomogram Model for Predicting High Volume (＞5) Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma Xuan Guo, Fenghua Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5662887/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives To develop a nomogram model for predicting the risk of high volume (>5) of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma and to evaluate the effectiveness of the model in clinical application, in order to achieve the goal of initial risk stratification of patients with PTMC, individualized design of the scope of surgery, and reduction the incidence of secondary surgery in patients with PTMC by the clinicians. Methods Retrospective analysis of clinical characteristics of patients with PTMC in the Surveillance, Epidemiology, and End Results (SEER) database between January 2016 and December 2020 and the clinical case data of patients who presented to the Gland Surgery, Hebei General Hospital, underwent surgical treatment, and were ultimately pathologically diagnosed with PTMC between January 2021 and December 2023.The clinicopathological characteristics included in the training group were screened using univariate and multivariate logistic regression analyses, to determine the independent risk factors for high volume CLNM in patients with PTMC, and to construct a nomogram model for predicting high volume CLNM. Results The male gender, lager tumor size(>5mm), multifocality, and extra-thyroidal invasion were independent risk factors for high volume CLNM in patients with papillary thyroid microcarcinoma. In contrast, elderly age(≥ 55years) at diagnosis was identified as a protective factor.Based on these independent risk factors, a nomogram model was further constructed to predict high volume CLNM. Conclusions 1 Male, large tumor diameter (>5mm), multifocal, and extra-thyroidal invasion were independent risk factors for high volume CLNM of patients with papillary thyroid microcarcinoma. In contrast, age ≥ 55 at the time of diagnosis was identified as a protective factor.2 The clinical prediction model based on the above mentioned factors has good predictive value, and provides a better individualized clinical decision for the management of PTMC patients by surgeons. papillary thyroid microcarcinoma central lymph node metastasis logistic regression analyses Nomogram Figures Figure 1 Figure 2 Figure 3 Introduction In recent years, the number of people suffering from differentiated thyroid carcinoma (DTC) has been increasing steadily, with the incidence rate showing a significant increase globally [ 1 ] .One of the most common pathological types is papillary thyroid carcinoma (PTC). According to epidemiological studies, over 90% of all thyroid cancers are PTC. Of these, approximately 49% are T1a stage tumors with a diameter of less than 1 cm, and approximately 87% are T1 stage tumors with a diameter of less than 2 cm [ 2 – 3 ] .In thyroid cancer, PTC not only has a high incidence, but also tends to have lymph node metastasis in the early stage, and central lymph node metastasis is the most common [ 4 ] .In addition, there is evidence, particularly from developed countries, that thyroidectomy without prophylactic central lymph node dissection (pCLND) is sufficient to treat clinically node-negative (cN0) PTC patients [ 5 – 6 ] .Because of this, the "less is more" consensus is increasingly accepted by many thyroid surgeons, who recommend less extensive surgery, less radioactive iodine, and less surveillance testing [ 7 ] .In addition, the mortality rate of PTC has been stable at about 0.5 per 100,000, which is why some doctors treat low-risk PTC more conservatively [ 8 ] .Treatment of papillary thyroid microcarcinoma is more conservative. Active surveillance is one of the currently recommended strategies [ 9 – 10 ] .However, in clinical practice, some scholars believe that Central lymph node metastasis (CLNM) may occur even in small lesions [ 11 ] .In addition, according to the 2015 edition of the American thyroid association (ATA) management guidelines, a large number (> 5) CLNM is a key risk factor for risk stratification evaluation of PTC patients.For patients with a large number (> 5) of CLNM, a more aggressive treatment strategy is needed, and postoperative radioactive iodine (RAI) therapy is recommended. At the same time, total thyroidectomy is the basis for postoperative RAI therapy. Due to thyroidectomy, it is inevitable to cause tissue adhesion, surgical scars, etc. When performing a second surgery to remove residual thyroid tissue, the difficulty of the operation will greatly increase, especially in the preservation of the recurrent laryngeal nerve and parathyroid function, and the second surgery will bring great negative impact on the patient's life and psychology [ 12 – 14 ] .Therefore, preoperative or intraoperative identification of high-risk subgroups of patients who may have a large number of CLNM may help surgeons better personalize the extent of surgery and guide patients for postoperative radioiodine therapy [ 15 – 16 ] .This study aims to identify clinical risk factors associated with a large number of CLNM in PTMC patients based on a large number of multi-population clinical characteristics in the SEER database, and to establish a nomogram for individualized risk prediction of this subgroup. In addition, a validation cohort from the hospital departments was used to validate the conclusions drawn in the nomogram. Materials and methods Data source Data for the training group were obtained from the SEER database. The data of the validation group were from the electronic medical record system of Hebei General Hospital. The clinical medical records of PTMC patients who were treated with standardized surgery in Hebei General Hospital from January 2021 to December 2023 were collected.Hebei General Hospital approved the protocol for this study. Ethical approval was waived by the local Ethics Committee of the Hebei General Hospital given the study's retrospective nature and all the procedures being performed were part of the routine care. Patient selection We selected patients from the SEER database who were diagnosed with PTC (ICD-O-3 codes 8050/3 and 8260/3) between January 2016 and December 2020. Clinical-pathological features included: age at diagnosis, gender, race, primary tumor size, extrathyroidal invasion, unilaterality, multifocality, histological variations, TNM staging (based on the AJCC 8th edition), number of regional lymph nodes examined, number of positive regional lymph nodes, and distant metastasis at initial diagnosis. Exclusion criteria included: 1) age 85 years; 2) presence of another primary cancer; 3) lack of histologically confirmed nodal involvement; 4) absence of thyroidectomy; 5) examination of < 5 regional lymph nodes; and 6) incomplete or missing medical records. In the medical record system of Hebei General Hospital, we identified patients with PTMC between January 2021 and December 2023. Inclusion criteria: 1) age ≥ 18 years old; 2) underwent thyroidectomy and central lymph node dissection (CLND); 3) PTMC confirmed by postoperative pathology; 4) with complete clinical data. Exclusion criteria: 1) age 85 years old; 2) patients with previous partial thyroidectomy; 3) patients with insufficient regional lymph nodes (< 5 lymph nodes); 4) patients with distant metastasis or concurrent history of other systemic tumors; 5) patients with incomplete or missing medical records. Research Methods According to the inclusion and exclusion criteria, 2085 patients from SEER database and 283 patients from Hebei General Hospital were included in this study. Data from SEER database were used as training group, and data from Hebei General Hospital were used as validation group. The clinicopathological characteristics of the training group and the validation group were compared at baseline. The cases in the training group and the validation group were grouped according to whether the number of central lymph node metastasis was more than 5. Univariate and multivariate analyses were performed on the variables to screen out the independent risk factors affecting a large number of central lymph node metastasis. ROC curve and area under the curve (AUC) were used to evaluate the discrimination of the prediction model. Hosmer-Lemeshow test, calibration curve and decision curve were used to evaluate the prediction model. Statistic analysis IBM SPSS software (version 26.0) and R software (version 4.3.2) were used to complete all data processing, analysis and mapping. The classification was described by the number of cases (constituent ratio, %), and the continuous variables were tested for normality first. The results of data conforming to the normal distribution were described by the mean ± standard deviation, and the results of data not conforming to the normal distribution were described by the median and interquartile range. Chi-square test was used for comparison between groups. Univariate Logistic regression analysis was performed on the variables, and variables with P < 0.05 were included in multivariate Logistic regression analysis (stepwise backward likelihood ratio regression method) to obtain independent risk factors. The "rms package" of R software was used to establish the clinical model and draw the nomogram, the "pROC package" was used to draw the ROC curve and calculate the AUC, the "rms package" was used to draw the calibration curve, and the "rmda package" was used to draw the DCA curve. Results Overall clinicopathological characteristics of the patients According to the inclusion and exclusion criteria, 2085 patients were included in the training group, including 332 males (15.92%) and 1753 females (84.08%), with an average age of 47 (37,59) years. A total of 283 patients were included in the validation group, including 60 males (21.20%) and 223 females (78.80%), with an average age of 46 (37,55) years. CLNM was identified in 34.82% (726 cases) PTMC patients in the training group and 53.71% (152 cases) in the validation group. A large number of CLNM were identified in only 9.88% (206 cases) of PTMC patients in the training group and 11.31% (32 cases) of patients in the validation group. Details of baseline characteristics are provided in Table 1 . Table 1 Clinicopathological characteristics of patients Variables Subgroup Training cohort(n = 2085) External cohort(n = 283) Gender Male 332(15.92%) 60(21.20%) Female 1753(84.08%) 223(78.80%) Age(years) 47(37, 59) 46(37, 55) 5 1495(71.70%) 225(79.51%) Differentiated Grade Well 86(4.12%) Moderately 18(0.86%) Poorly / Undifferentiated / Unknown 1981(95.02%) Laterality One side 166(7.96%) 174(61.48%) Bilateral 27(1.29%) 109(38.52%) Unknown 1892(90.75%) 0(0) b Stage I 1697(81.39%) 250(88.34%) II 381(18.27%) 33(11.66%) III 7(0.34%) / IV / / CLNM Negative 1359(65.18%) 131(46.29%) Positive 726(34.82%) 152(53.71%) The number of lymph node metastasis ≤ 5 1879(90.12%) 251(88.69%) >5 206(9.88%) 32(11.31%) Extrathyroidal invasion Negative 1851(88.78%) 244(86.22%) Positive 234(11.22%) 39(13.78%) Multifocality Negative 1048(50.26%) 151(53.36%) Positive 1037(49.74%) 132(46.64%) a Other: defined as the Asian/Pacific Islander and American Indian/Alaska Native b Stage:according to the 8th version of TNM by the American Joint Committee on Cancer (AJCC); high-volume: the number of positive lymph nodes > 5 Influencing factors in univariate Logistic regression analysis of the training group Univariate Logistic regression analysis of the training group data showed that male sex (P 5mm, P < 0.001), multifocality (P < 0.001), and extrathyroidal extension (P < 0.001) were risk factors for a large number of CLNM in PTMC patients, as detailed in Table 2 . Table 2 Univariate Logistic regression analysis was performed Variables B SE OR (95% CI ) P χ 2 P Gender 14.829 <0.001 Female reference <0.001 Male 0.657 0.173 1.928(1.374 ~ 2.707) Age 10.188 0.001 <55 reference 0.001 ≥ 55 -0.607 0.193 0.601(0.433 ~ 0.836) Race 2.929 0.231 White reference 0.238 Black -0.094 0.143 0.910(0.688 ~ 1.205) 0.511 a Other -0.618 0.366 0.539(0.263 ~ 1.103) 0.091 Tumor size (mm) 15.667 <0.001 ≤ 5 reference 5 0.754 0.194 2.126(1.452 ~ 3.112) b Stage 2.901 0.234 I reference 0.214 II -1.115 0.767 0.328(0.073 ~ 1.475) 0.146 III -0.959 0.775 0.383(0.084 ~ 1.748) 0.215 Extrathyroidal invasion 181.120 <0.001 Negative reference <0.001 Positive 1.989 0.166 7.310(5.283 ~ 10.115) Multifocality 12.979 <0.001 Negative reference Positive 0.538 0.151 1.712(1.274 ~ 2.299) <0.001 Bold values indicate statistical significance (p < 0.05) a Other: defined as the Asian/Pacific Islander and American Indian/Alaska Native b Stage:according to the 8th version of TNM by the American Joint Committee on Cancer (AJCC); high-volume: the number of positive lymph nodes > 5 Influencing factors of multivariate Logistic regression analysis in the training group The statistically significant indicators screened by univariate Logistic regression analysis were included in multivariate Logistic regression analysis, and the stepwise backward likelihood ratio regression method was used to further screen the independent risk factors. Five influencing factors were finally screened out, among which male patients, large tumor size, multifocality and extrathyroidal invasion were independent risk factors for massive CLNM in PTMC patients, while age (≥ 55 years old) was a protective factor. See Table 3 . Table 3 Multivariate Logistic regression analysis was performed Variables B SE OR (95% CI ) P Gender Female reference Male 0.572 0.186 1.772(1.230 ~ 2.554) 0.002 Age 5 0.624 0.203 1.867(1.253 ~ 2.782) 0.002 Extrathyroidal invasion Negative reference Positive 1.928 1.170 6.877(4.931 ~ 9.590) <0.001 Multifocality Negative reference Positive 0.510 0.159 1.666(1.219 ~ 2.277) 0.001 Bold values indicate statistical significance (p < 0.05) Nomogram construction On the basis of multivariate Logistic regression analysis, a nomogram model was established, and the corresponding scores were calculated according to the regression coefficients of the included influencing factors. The risk probability of a large number of CLNM in this patient could be obtained (Fig. 1 ). EI extrathyroidal invasion The model contained a satisfying C-index of 0.763, which was equivalent to the AUC (Fig. 2A). The utility of the nomogram was further validated by an external cohort with a C-index of 0.725 (Fig. 2B). Moreover, a calibration curve for evaluating the accuracy of the predictive ability in high-volume CLNM was also performed via 1000 bootstrap repetitions (Fig. 3A, B). The curves (apparent, ideal, and bias-corrected lines) suggested a relatively good agreement in the training model and external validation cohort, with a mean absolute error of 0.009 and 0.01, respectively. Moreover, the decision curve analyses (DCA) were performed to evaluate the utility of the model in detecting high-volume CLNM for PTMC patients. The DCA curves presented that the prediction ability derived from the nomogram would be more effective than a treat-none or treat-all strategy when the threshold probability ranged from 0.1 to 0.6 in three cohorts (Fig. 3C, D). Discussion In recent years, the incidence of PTC has increased significantly worldwide, but its mortality has remained stable or even decreased [ 17 ] . This is due to the development of ultrasound imaging technology and aggressive neck ultrasound [ 18 ] . Except for some papillary thyroid carcinoma (PTC) with specific gene mutations, most patients with PTC have a relatively good prognosis and a relatively low risk of recurrence. As a result, the clinical management of PTC, especially PTMC, has become more conservative, and even active surveillance has recently been recognized as a major management modality for low-risk populations [ 19 ] . However, lymph node metastasis in PTC occurs earlier, and most patients have cervical lymph node metastasis at the time of diagnosis. Level Ⅵ is the most common metastatic site. As an important risk factor for local recurrence, a large number of (> 5) CLNM also appear in cN0 PTMC patients [ 20 ] . Whether to perform pCLND in these patients remains controversial.At present, the treatment of PTC mainly relies on surgery, which covers the treatment of the primary tumor and the management of regional lymph nodes. Central lymph node (CLN) dissection is divided into prophylactic CLN dissection a nd therapeutic CLN dissection. Prophylactic dissection is usually performed in the presence of high-risk factors to prevent potential lymph node metastasis. In contrast, therapeutic dissection is performed when lymph-node metastasis is established and is intended to remove cancer cells and reduce the risk of recurrence. The current debate focuses on the cost (postoperative complications) and benefit (long-term disease-free survival) of PTMC patients undergoing Total thyroidectomy (TT) combined with pCLND [ 21 – 23 ] . For lymph node management, the 2015 American Thyroid Association (ATA) guidelines recommend that pCLND is usually recommended for patients with papillary thyroid carcinoma in stage T3, T4 or cN1b. However, pCLND is generally not recommended for T1, T2, non-invasive cN0 PTC. According to the Chinese standard of diagnosis and treatment of thyroid cancer, cN1a should be dissected in the central area of the affected side. For cN0 patients, central compartment dissection may be considered if there are high risk factors (such as T3-T4 lesions, multifocal cancer, family history, and childhood ionizing radiation exposure history). For low-risk patients with cN0 (without high-risk factors), individualized treatment can be performed. Previous studies have shown that there is no statistically significant difference in postoperative recurrence rate between patients with and without pCLND [ 24 – 25 ] . However, some studies have shown that the incidence of permanent hypoparathyroidism after total thyroidectomy combined with pCLND is higher in patients with clinically nodenegative papillary thyroid carcinoma (TT = 1.55%vs.TT + pCLND = 3.45%). However, the incidence of permanent recurrent laryngeal nerve dysfunction was similar (TT = 0.89%vs.TT + pCLND = 0.96%) [ 26 ] . If the central lymph nodes are not treated, some cases will have residual lesions, which may lead to recurrence in the future. Secondary surgery will significantly increase the risk of recurrent laryngeal nerve and parathyroid injury [ 27 ] . At present, ultrasound is still the primary examination for evaluating the central lymph node status of thyroid cancer. Due to the anatomical characteristics of the neck, the detection of central lymph nodes by ultrasound is limited, and it is more difficult to determine its nature. Moreover, metastatic lymph nodes are often micrometastases, and may not have any manifestations on ultrasound [ 28 – 29 ] . The ultrasound technique is dependent on the experience of the operator and has certain limitations in its application. In this study, CLNM was identified in 34.82% (726/2085) of PTMC patients from the SEER database, which is consistent with previously published studies from different databases. The 8th edition of American Joint Committee on Cancer (AJCC) guidelines used 55 as the age risk stratification for DTC. In this study, the subjects were analyzed using the age of 55 as the cut-off point in combination with the AJCC guidelines. Shukla et al. showed that the total incidence of lymph node metastasis was 26.11% compared with PTC patients aged 0 to 10 years, 11 to 20 years, 21 to 30 years and older than 30 years, which increased with the decrease of age [ 30 ] . Decreasing age was also associated with increased total positive lymph nodes, increased proportion of lymph nodes, and increased risk of lateral neck disease. Children and young adults with papillary thyroid carcinoma are considered to be at greater risk of lymph node metastasis, greater nodal disease burden, and greater risk of lateral cervical metastasis. In the training group of this study, there were 2085 patients, including 332 males, accounting for 15.92% of the total number. Univariate Logistic regression analysis showed that gender had a statistically significant difference in predicting the risk of massive CLNM (P < 0.05). Further multivariate regression analysis showed that we confirmed that male sex was an independent risk factor for the onset of CLNM, which was consistent with the findings of Shen et al. [ 31 ] . There is no clear cut-off value for tumor diameter. Tumor size is often positively correlated with the risk of CLNM, but the threshold of tumor size is not consistent in various studies [ 32 ] . In this study, 5mm was used as the cut-off value of tumor diameter. After univariate and multivariate regression analysis, tumor diameter greater than 5mm was an independent risk factor for massive CLNM in PTMC patients (P < 0.001, OR = 1.785). A retrospective study found that in PTMC patients, the presence of extrathyroidal extension indicated increased CLN metastasis by multivariate analysis (OR = 1.647, P < 0.001) [ 33 ] . In the present study, the results of univariate and multivariate Logistic regression analysis also showed that extrathyroidal extension of nodules was an independent risk factor for predicting massive CLNM (P < 0.001, OR = 6.877). In this study, through univariate Logistic regression analysis, it was found that multifocality was statistically significant in predicting the risk of massive CLNM (P < 0.05), that is, patients with multifocality had a higher risk of massive CLNM. Further multivariate Logistic regression analysis also confirmed that multifocality was an independent risk factor for massive CLNM in PTMC patients (P = 0.001, OR = 1.666). Therefore, for patients with multifocal PTMC, total thyroidectomy and further frozen section of central lymph nodes can be considered during operation to improve the treatment effect and prognosis of patients, and better guide the subsequent treatment of patients. In this study, five key factors including age, gender, tumor diameter, multifocality and extrathyroidal extension were comprehensively considered. Based on the OR value and Logisitc regression equation of these factors, a nomogram clinical prediction model was successfully constructed. This model aims to establish a risk assessment system for central lymph node massive metastasis in patients with papillary thyroid microcarcinoma. However, the construction of the model is not the ultimate goal, and its real value lies in the embodiment of its clinical significance. In this study model. The discrimination of the model was evaluated by ROC curve, and the area under the curve (AUC) was a specific quantitative index. In this study, the AUC value was 0.763 in the training group and 0.725 in the validation group. These two values validate the ability of the model to distinguish between patients at high and low risk of CLNM. Although the AUC value did not reach the high discrimination standard of 0.8, the consistency performance in the training group and the validation group still suggested that the model had a certain ability to distinguish the risk of CLNM in patients. In the future, we can further optimize the model to improve its discrimination. In this study, the goodness-of-fit test of the data of the training group and the validation group in the two models were all P > 0.05, which proved that the model was accurate in predicting the risk of a large number of CLNM. Among the calibration curves drawn in this study, the actual curve and the calibrated curve have a high degree of overlap, and both curves have a high degree of overlap with the diagonal dashed line representing the ideal state, which proves that the model in this study has good calibration ability and prediction performance and high accuracy for predicting the risk of a large number of CLNM. Reviewing similar work in predicting CLNM in PTC patients, there are partial differences in our study and further studies are also needed. We aimed to identify a high-risk population of patients with PTMC who need to receive a relatively more aggressive treatment modality. This study explored a subgroup of patients with PTMC who have a large (> 5) CLNM, accounting for approximately 10% of all patients with PTMC. These patients (ATA intermediate-risk group) may require more aggressive treatment strategies such as total thyroidectomy and postoperative radioiodine (RAI). At the same time, total thyroidectomy is the basis of postoperative radioiodine therapy. Although ATA guidelines state that patients with ATA intermediate risk DTC should consider adjuvant RAI therapy after total thyroidectomy, this recommendation level is weak and the quality of evidence is low. Furthermore, whether RAI treatment improves overall and disease-specific survival in PTMC remains controversial, even if there is conflicting evidence on the impact on recurrence. Further studies are needed to determine the benefit of RAI in PTMC patients with intermediate ATA risk. However, this study still has some limitations; first, it is a retrospective study from the SEER database, which does introduce some selection bias. Secondly, the currently used external validation cohorts are based on single-center, retrospective studies, and their sample sizes are relatively small, which may have certain biases and limitations. In order to further improve the performance and accuracy of the model, it is necessary to include multi-center, large-sample and prospective research data to refine and optimize the model. This will enable a more comprehensive assessment of the feasibility and practicability of the nomogram we constructed in clinical practice. Therefore, future studies should aim to collect data from more patients to achieve continuous optimization and validation of model performance. In addition, there were only five prognostic factors in the nomogram, suggesting that there may be potential variables waiting to be discovered and validated that could make our nomogram more complete and reliable, including but not limited to body mass index (BMI), ultrasound features, and some laboratory test results, which have been previously identified to be associated with CLNM in PTC patients. Conclusion By univariate and multivariate Logistic regression analysis, male, tumor size (> 5mm), multifocality and extrathyroidal extension were considered to be independent risk factors for massive CLNM in PTMC patients. In contrast, age at diagnosis (≥ 55 years) was identified as a protective factor. Based on the above influencing factors, the Nomogram clinical prediction model established has good predictive value. The nomogram was successfully established and validated with five clinical indicators. This model can help surgeons make better personalized clinical decisions regarding the management of patients with papillary thyroid microcarcinoma. Declarations Acknowledgements We acknowledged Dr. Fenghua Zhang for the substantial contribution to thyroid surgery in our department. Also, we acknowledge the contributions of the Surveillance, Epidemiology, and End Results (SEER) Program registries for creating and updating the SEER database. Author contributions (I) Conception and design: Xuan Guo, Fenghua Zhang. (II) Administrative support: Fenghua Zhang. (III) Provision of study materials or patients: Xuan Guo, Fenghua Zhang. (IV) Collection and assembly of data: Xuan Guo. (V) Data analysis and interpretation: Xuan Guo. (VI) Manuscript writing: all authors. (VII) Final approval of manuscript: all authors. Funding None. Availability of data and material The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Code availability The software application generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Compliance with ethical standards Conflicts of interest The authors declare that they have no conflict of interest. Ethical approval Ethical approval was waived by the local Ethics Committee of the Hebei General Hospital in view of the retrospective nature of the study and all the procedures being performed were part of the routine care. Informed consent Not applicable. Consent for publication Not applicable. References Wei X, Min Y, Feng Y, et al. Development and validation of an individualized nomogram for predicting the high-volume （＞5） central lymph node metastasis in papillary thyroid microcarcinoma[J]. 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Predictive factors for lymph node metastasis in papillary thyroid microcarcinoma[J]. Annals of surgical oncology, 2016, 23: 2866-2873. Shukla N, Osazuwa-Peters N, Megwalu U C. Association between age and nodal metastasis in papillary thyroid carcinoma[J]. Otolaryngology–Head and Neck Surgery, 2021, 165（1）: 43-49. Shen G, Ma H, Huang R, et al. Predicting large-volume lymph node metastasis in the clinically node-negative papillary thyroid microcarcinoma: a retrospective study[J]. Nuclear Medicine Communications, 2020, 41（1）: 5-10. Dou Y, Hu D, Chen Y, et al. PTC located in the upper pole is more prone to lateral lymph node metastasis and skip metastasis[J]. World Journal of Surgical Oncology, 2020, 18（1）: 1-7. Sheng L, Shi J, Han B, et al. Predicting factors for central or lateral lymph node metastasis in conventional papillary thyroid microcarcinoma[J]. The American Journal of Surgery, 2020, 220（2）: 334-340. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5662887","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392579790,"identity":"efcd8a7e-3dc3-4faf-a1a0-3fdbd2d4da01","order_by":0,"name":"Xuan Guo","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Guo","suffix":""},{"id":392579792,"identity":"b1d2b5a3-84fe-486d-828b-862849d21ef5","order_by":1,"name":"Fenghua Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACAxCRUGFT30+algdn0hhnNpCihfFh22HGDQeI1WLO3n5NIrHtMLPx8eQNDD8qthHWYtlzptgg4Vw6m9mZZwWMPWduE+GwGzmJDxLKrHnMbuQYMDO2EaPl/puEAwlszBLGM4jWcoP94IOENmcDAwmitZzJYTZIOJOWIAH0y0Hi/HL8+DPJHxU2CfztyRsf/KggQgsDA48BlJFgcIAY9UDA/gCuhUgdo2AUjIJRMNIAAJkVQmCqphrWAAAAAElFTkSuQmCC","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Fenghua","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-12-17 14:53:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5662887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5662887/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72334381,"identity":"15935d0b-26d2-4d1d-9763-2b6cc772c73a","added_by":"auto","created_at":"2024-12-25 15:37:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19273,"visible":true,"origin":"","legend":"\u003cp\u003eClinicopathological characteristics-based nomogram used for prediction of high-volume central lymph node metastasis in PTMC patients.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5662887/v1/1bb4e684620fd3306557c549.png"},{"id":72336098,"identity":"131bf707-c4ec-49fe-90de-b3e2da34eae1","added_by":"auto","created_at":"2024-12-25 15:45:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54542,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver-operating characteristics (ROC) curve and area under the ROC curve (AUC) in the training cohort (A) and external cohort (B)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5662887/v1/e4a6bb6680c1cde112f51364.png"},{"id":72334385,"identity":"026e6a9d-89f6-4d36-a4e2-1251254a4cd2","added_by":"auto","created_at":"2024-12-25 15:37:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109649,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration curves for evaluating the accuracy of the nomogram and determination of decision point via decision curve analysis (DCA). A The calibration curves in the training cohort; B the calibration curves in the external validation cohort; C the DCA in the training cohort; D the DCA in the external cohort.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5662887/v1/0eafc1b92edf585b4be7e9bb.png"},{"id":73869669,"identity":"3db2e8d2-cc84-4df3-ad4d-edbb5b21309b","added_by":"auto","created_at":"2025-01-15 12:09:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1065088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5662887/v1/677cddd9-6c87-4251-99d6-9fec6dccf144.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"To Develop and Validate a Nomogram Model for Predicting High Volume (＞5) Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the number of people suffering from differentiated thyroid carcinoma (DTC) has been increasing steadily, with the incidence rate showing a significant increase globally\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.One of the most common pathological types is papillary thyroid carcinoma (PTC). According to epidemiological studies, over 90% of all thyroid cancers are PTC. Of these, approximately 49% are T1a stage tumors with a diameter of less than 1 cm, and approximately 87% are T1 stage tumors with a diameter of less than 2 cm\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.In thyroid cancer, PTC not only has a high incidence, but also tends to have lymph node metastasis in the early stage, and central lymph node metastasis is the most common\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.In addition, there is evidence, particularly from developed countries, that thyroidectomy without prophylactic central lymph node dissection (pCLND) is sufficient to treat clinically node-negative (cN0) PTC patients\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.Because of this, the \"less is more\" consensus is increasingly accepted by many thyroid surgeons, who recommend less extensive surgery, less radioactive iodine, and less surveillance testing\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.In addition, the mortality rate of PTC has been stable at about 0.5 per 100,000, which is why some doctors treat low-risk PTC more conservatively\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.Treatment of papillary thyroid microcarcinoma is more conservative. Active surveillance is one of the currently recommended strategies\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.However, in clinical practice, some scholars believe that Central lymph node metastasis (CLNM) may occur even in small lesions\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.In addition, according to the 2015 edition of the American thyroid association (ATA) management guidelines, a large number (\u0026gt;\u0026thinsp;5) CLNM is a key risk factor for risk stratification evaluation of PTC patients.For patients with a large number (\u0026gt;\u0026thinsp;5) of CLNM, a more aggressive treatment strategy is needed, and postoperative radioactive iodine (RAI) therapy is recommended. At the same time, total thyroidectomy is the basis for postoperative RAI therapy. Due to thyroidectomy, it is inevitable to cause tissue adhesion, surgical scars, etc. When performing a second surgery to remove residual thyroid tissue, the difficulty of the operation will greatly increase, especially in the preservation of the recurrent laryngeal nerve and parathyroid function, and the second surgery will bring great negative impact on the patient's life and psychology\u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.Therefore, preoperative or intraoperative identification of high-risk subgroups of patients who may have a large number of CLNM may help surgeons better personalize the extent of surgery and guide patients for postoperative radioiodine therapy\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.This study aims to identify clinical risk factors associated with a large number of CLNM in PTMC patients based on a large number of multi-population clinical characteristics in the SEER database, and to establish a nomogram for individualized risk prediction of this subgroup. In addition, a validation cohort from the hospital departments was used to validate the conclusions drawn in the nomogram.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eData for the training group were obtained from the SEER database. The data of the validation group were from the electronic medical record system of Hebei General Hospital. The clinical medical records of PTMC patients who were treated with standardized surgery in Hebei General Hospital from January 2021 to December 2023 were collected.Hebei General Hospital approved the protocol for this study. Ethical approval was waived by the local Ethics Committee of the Hebei General Hospital given the study's retrospective nature and all the procedures being performed were part of the routine care.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient selection\u003c/h3\u003e\n\u003cp\u003eWe selected patients from the SEER database who were diagnosed with PTC (ICD-O-3 codes 8050/3 and 8260/3) between January 2016 and December 2020. Clinical-pathological features included: age at diagnosis, gender, race, primary tumor size, extrathyroidal invasion, unilaterality, multifocality, histological variations, TNM staging (based on the AJCC 8th edition), number of regional lymph nodes examined, number of positive regional lymph nodes, and distant metastasis at initial diagnosis. Exclusion criteria included: 1) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years or \u0026gt;\u0026thinsp;85 years; 2) presence of another primary cancer; 3) lack of histologically confirmed nodal involvement; 4) absence of thyroidectomy; 5) examination of \u0026lt;\u0026thinsp;5 regional lymph nodes; and 6) incomplete or missing medical records.\u003c/p\u003e \u003cp\u003eIn the medical record system of Hebei General Hospital, we identified patients with PTMC between January 2021 and December 2023. Inclusion criteria: 1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years old; 2) underwent thyroidectomy and central lymph node dissection (CLND); 3) PTMC confirmed by postoperative pathology; 4) with complete clinical data. Exclusion criteria: 1) age\u0026thinsp;\u0026lt;\u0026thinsp;18 or \u0026gt;\u0026thinsp;85 years old; 2) patients with previous partial thyroidectomy; 3) patients with insufficient regional lymph nodes (\u0026lt;\u0026thinsp;5 lymph nodes); 4) patients with distant metastasis or concurrent history of other systemic tumors; 5) patients with incomplete or missing medical records.\u003c/p\u003e\n\u003ch3\u003eResearch Methods\u003c/h3\u003e\n\u003cp\u003e According to the inclusion and exclusion criteria, 2085 patients from SEER database and 283 patients from Hebei General Hospital were included in this study. Data from SEER database were used as training group, and data from Hebei General Hospital were used as validation group. The clinicopathological characteristics of the training group and the validation group were compared at baseline. The cases in the training group and the validation group were grouped according to whether the number of central lymph node metastasis was more than 5. Univariate and multivariate analyses were performed on the variables to screen out the independent risk factors affecting a large number of central lymph node metastasis. ROC curve and area under the curve (AUC) were used to evaluate the discrimination of the prediction model. Hosmer-Lemeshow test, calibration curve and decision curve were used to evaluate the prediction model.\u003c/p\u003e\n\u003ch3\u003eStatistic analysis\u003c/h3\u003e\n\u003cp\u003eIBM SPSS software (version 26.0) and R software (version 4.3.2) were used to complete all data processing, analysis and mapping. The classification was described by the number of cases (constituent ratio, %), and the continuous variables were tested for normality first. The results of data conforming to the normal distribution were described by the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and the results of data not conforming to the normal distribution were described by the median and interquartile range. Chi-square test was used for comparison between groups. Univariate Logistic regression analysis was performed on the variables, and variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included in multivariate Logistic regression analysis (stepwise backward likelihood ratio regression method) to obtain independent risk factors. The \"rms package\" of R software was used to establish the clinical model and draw the nomogram, the \"pROC package\" was used to draw the ROC curve and calculate the AUC, the \"rms package\" was used to draw the calibration curve, and the \"rmda package\" was used to draw the DCA curve.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOverall clinicopathological characteristics of the patients\u003c/h2\u003e \u003cp\u003eAccording to the inclusion and exclusion criteria, 2085 patients were included in the training group, including 332 males (15.92%) and 1753 females (84.08%), with an average age of 47 (37,59) years. A total of 283 patients were included in the validation group, including 60 males (21.20%) and 223 females (78.80%), with an average age of 46 (37,55) years. CLNM was identified in 34.82% (726 cases) PTMC patients in the training group and 53.71% (152 cases) in the validation group. A large number of CLNM were identified in only 9.88% (206 cases) of PTMC patients in the training group and 11.31% (32 cases) of patients in the validation group. Details of baseline characteristics are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eClinicopathological characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining cohort(n\u0026thinsp;=\u0026thinsp;2085)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExternal cohort(n\u0026thinsp;=\u0026thinsp;283)\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 \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332(15.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(21.20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1753(84.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223(78.80%)\u003c/p\u003e \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=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(37, 59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46(37, 55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1537(73.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e226(79.86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e548(26.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57(20.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1689(81.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(3.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326(15.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(4,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(4,8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e590(28.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58(20.49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1495(71.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225(79.51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiated Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(4.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(0.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1981(95.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaterality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166(7.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174(61.48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(1.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109(38.52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1892(90.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1697(81.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250(88.34%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381(18.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(11.66%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(0.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1359(65.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131(46.29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e726(34.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152(53.71%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe number of lymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1879(90.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251(88.69%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206(9.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(11.31%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtrathyroidal invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1851(88.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244(86.22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234(11.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(13.78%)\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 \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1048(50.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151(53.36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1037(49.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132(46.64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003eOther: defined as the Asian/Pacific Islander and American Indian/Alaska Native\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003eb\u003c/sup\u003eStage:according to the 8th version of TNM by the American Joint Committee on Cancer (AJCC); high-volume: the number of positive lymph nodes\u0026thinsp;\u0026gt;\u0026thinsp;5\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInfluencing factors in univariate Logistic regression analysis of the training group\u003c/h3\u003e\n\u003cp\u003eUnivariate Logistic regression analysis of the training group data showed that male sex (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), larger tumor size (\u0026gt;\u0026thinsp;5mm, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), multifocality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and extrathyroidal extension (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were risk factors for a large number of CLNM in PTMC patients, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eUnivariate Logistic regression analysis was performed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\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\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.928(1.374\u0026thinsp;~\u0026thinsp;2.707)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.601(0.433\u0026thinsp;~\u0026thinsp;0.836)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.910(0.688\u0026thinsp;~\u0026thinsp;1.205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.539(0.263\u0026thinsp;~\u0026thinsp;1.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (mm)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.126(1.452\u0026thinsp;~\u0026thinsp;3.112)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eStage\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.328(0.073\u0026thinsp;~\u0026thinsp;1.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.383(0.084\u0026thinsp;~\u0026thinsp;1.748)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtrathyroidal invasion\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e181.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.310(5.283\u0026thinsp;~\u0026thinsp;10.115)\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 \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.712(1.274\u0026thinsp;~\u0026thinsp;2.299)\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBold values indicate statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003eOther: defined as the Asian/Pacific Islander and American Indian/Alaska Native\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003eStage:according to the 8th version of TNM by the American Joint Committee on Cancer (AJCC); high-volume: the number of positive lymph nodes\u0026thinsp;\u0026gt;\u0026thinsp;5\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eInfluencing factors of multivariate Logistic regression analysis in the training group\u003c/h3\u003e\n\u003cp\u003eThe statistically significant indicators screened by univariate Logistic regression analysis were included in multivariate Logistic regression analysis, and the stepwise backward likelihood ratio regression method was used to further screen the independent risk factors. Five influencing factors were finally screened out, among which male patients, large tumor size, multifocality and extrathyroidal invasion were independent risk factors for massive CLNM in PTMC patients, while age (\u0026ge;\u0026thinsp;55 years old) was a protective factor. See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eMultivariate Logistic regression analysis was performed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\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 \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.772(1.230\u0026thinsp;~\u0026thinsp;2.554)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.524(0.352\u0026thinsp;~\u0026thinsp;0.781)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (mm)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.867(1.253\u0026thinsp;~\u0026thinsp;2.782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtrathyroidal invasion\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.877(4.931\u0026thinsp;~\u0026thinsp;9.590)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.666(1.219\u0026thinsp;~\u0026thinsp;2.277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBold values indicate statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNomogram construction\u003c/h2\u003e \u003cp\u003eOn the basis of multivariate Logistic regression analysis, a nomogram model was established, and the corresponding scores were calculated according to the regression coefficients of the included influencing factors. The risk probability of a large number of CLNM in this patient could be obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEI extrathyroidal invasion\u003c/p\u003e \u003cp\u003eThe model contained a satisfying C-index of 0.763, which was equivalent to the AUC (Fig.\u0026nbsp;2A). The utility of the nomogram was further validated by an external cohort with a C-index of 0.725 (Fig.\u0026nbsp;2B).\u003c/p\u003e \u003c/div\u003e\u003cp\u003eMoreover, a calibration curve for evaluating the accuracy of the predictive ability in high-volume CLNM was also performed via 1000 bootstrap repetitions (Fig.\u0026nbsp;3A, B). The curves (apparent, ideal, and bias-corrected lines) suggested a relatively good agreement in the training model and external validation cohort, with a mean absolute error of 0.009 and 0.01, respectively. Moreover, the decision curve analyses (DCA) were performed to evaluate the utility of the model in detecting high-volume CLNM for PTMC patients. The DCA curves presented that the prediction ability derived from the nomogram would be more effective than a treat-none or treat-all strategy when the threshold probability ranged from 0.1 to 0.6 in three cohorts (Fig.\u0026nbsp;3C, D).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, the incidence of PTC has increased significantly worldwide, but its mortality has remained stable or even decreased\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. This is due to the development of ultrasound imaging technology and aggressive neck ultrasound\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Except for some papillary thyroid carcinoma (PTC) with specific gene mutations, most patients with PTC have a relatively good prognosis and a relatively low risk of recurrence. As a result, the clinical management of PTC, especially PTMC, has become more conservative, and even active surveillance has recently been recognized as a major management modality for low-risk populations\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. However, lymph node metastasis in PTC occurs earlier, and most patients have cervical lymph node metastasis at the time of diagnosis. Level Ⅵ is the most common metastatic site. As an important risk factor for local recurrence, a large number of (\u0026gt;\u0026thinsp;5) CLNM also appear in cN0 PTMC patients\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Whether to perform pCLND in these patients remains controversial.At present, the treatment of PTC mainly relies on surgery, which covers the treatment of the primary tumor and the management of regional lymph nodes. Central lymph node (CLN) dissection is divided into prophylactic CLN dissection \u0026lt;link rid=\"Sec12\"\u0026gt;\u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003ea\u003c/span\u003e\u0026lt;/link\u0026gt;nd therapeutic CLN dissection. Prophylactic dissection is usually performed in the presence of high-risk factors to prevent potential lymph node metastasis. In contrast, therapeutic dissection is performed when lymph-node metastasis is established and is intended to remove cancer cells and reduce the risk of recurrence. The current debate focuses on the cost (postoperative complications) and benefit (long-term disease-free survival) of PTMC patients undergoing Total thyroidectomy (TT) combined with pCLND\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. For lymph node management, the 2015 American Thyroid Association (ATA) guidelines recommend that pCLND is usually recommended for patients with papillary thyroid carcinoma in stage T3, T4 or cN1b. However, pCLND is generally not recommended for T1, T2, non-invasive cN0 PTC. According to the Chinese standard of diagnosis and treatment of thyroid cancer, cN1a should be dissected in the central area of the affected side. For cN0 patients, central compartment dissection may be considered if there are high risk factors (such as T3-T4 lesions, multifocal cancer, family history, and childhood ionizing radiation exposure history). For low-risk patients with cN0 (without high-risk factors), individualized treatment can be performed. Previous studies have shown that there is no statistically significant difference in postoperative recurrence rate between patients with and without pCLND\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. However, some studies have shown that the incidence of permanent hypoparathyroidism after total thyroidectomy combined with pCLND is higher in patients with clinically nodenegative papillary thyroid carcinoma (TT\u0026thinsp;=\u0026thinsp;1.55%vs.TT\u0026thinsp;+\u0026thinsp;pCLND\u0026thinsp;=\u0026thinsp;3.45%). However, the incidence of permanent recurrent laryngeal nerve dysfunction was similar (TT\u0026thinsp;=\u0026thinsp;0.89%vs.TT\u0026thinsp;+\u0026thinsp;pCLND\u0026thinsp;=\u0026thinsp;0.96%)\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. If the central lymph nodes are not treated, some cases will have residual lesions, which may lead to recurrence in the future. Secondary surgery will significantly increase the risk of recurrent laryngeal nerve and parathyroid injury\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. At present, ultrasound is still the primary examination for evaluating the central lymph node status of thyroid cancer. Due to the anatomical characteristics of the neck, the detection of central lymph nodes by ultrasound is limited, and it is more difficult to determine its nature. Moreover, metastatic lymph nodes are often micrometastases, and may not have any manifestations on ultrasound\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The ultrasound technique is dependent on the experience of the operator and has certain limitations in its application.\u003c/p\u003e \u003cp\u003eIn this study, CLNM was identified in 34.82% (726/2085) of PTMC patients from the SEER database, which is consistent with previously published studies from different databases. The 8th edition of American Joint Committee on Cancer (AJCC) guidelines used 55 as the age risk stratification for DTC. In this study, the subjects were analyzed using the age of 55 as the cut-off point in combination with the AJCC guidelines. Shukla et al. showed that the total incidence of lymph node metastasis was 26.11% compared with PTC patients aged 0 to 10 years, 11 to 20 years, 21 to 30 years and older than 30 years, which increased with the decrease of age\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Decreasing age was also associated with increased total positive lymph nodes, increased proportion of lymph nodes, and increased risk of lateral neck disease. Children and young adults with papillary thyroid carcinoma are considered to be at greater risk of lymph node metastasis, greater nodal disease burden, and greater risk of lateral cervical metastasis. In the training group of this study, there were 2085 patients, including 332 males, accounting for 15.92% of the total number. Univariate Logistic regression analysis showed that gender had a statistically significant difference in predicting the risk of massive CLNM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Further multivariate regression analysis showed that we confirmed that male sex was an independent risk factor for the onset of CLNM, which was consistent with the findings of Shen et al.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. There is no clear cut-off value for tumor diameter. Tumor size is often positively correlated with the risk of CLNM, but the threshold of tumor size is not consistent in various studies\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In this study, 5mm was used as the cut-off value of tumor diameter. After univariate and multivariate regression analysis, tumor diameter greater than 5mm was an independent risk factor for massive CLNM in PTMC patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.785). A retrospective study found that in PTMC patients, the presence of extrathyroidal extension indicated increased CLN metastasis by multivariate analysis (OR\u0026thinsp;=\u0026thinsp;1.647, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In the present study, the results of univariate and multivariate Logistic regression analysis also showed that extrathyroidal extension of nodules was an independent risk factor for predicting massive CLNM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;6.877). In this study, through univariate Logistic regression analysis, it was found that multifocality was statistically significant in predicting the risk of massive CLNM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), that is, patients with multifocality had a higher risk of massive CLNM. Further multivariate Logistic regression analysis also confirmed that multifocality was an independent risk factor for massive CLNM in PTMC patients (P\u0026thinsp;=\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.666). Therefore, for patients with multifocal PTMC, total thyroidectomy and further frozen section of central lymph nodes can be considered during operation to improve the treatment effect and prognosis of patients, and better guide the subsequent treatment of patients.\u003c/p\u003e \u003cp\u003eIn this study, five key factors including age, gender, tumor diameter, multifocality and extrathyroidal extension were comprehensively considered. Based on the OR value and Logisitc regression equation of these factors, a nomogram clinical prediction model was successfully constructed. This model aims to establish a risk assessment system for central lymph node massive metastasis in patients with papillary thyroid microcarcinoma. However, the construction of the model is not the ultimate goal, and its real value lies in the embodiment of its clinical significance. In this study model. The discrimination of the model was evaluated by ROC curve, and the area under the curve (AUC) was a specific quantitative index. In this study, the AUC value was 0.763 in the training group and 0.725 in the validation group. These two values validate the ability of the model to distinguish between patients at high and low risk of CLNM. Although the AUC value did not reach the high discrimination standard of 0.8, the consistency performance in the training group and the validation group still suggested that the model had a certain ability to distinguish the risk of CLNM in patients. In the future, we can further optimize the model to improve its discrimination. In this study, the goodness-of-fit test of the data of the training group and the validation group in the two models were all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, which proved that the model was accurate in predicting the risk of a large number of CLNM. Among the calibration curves drawn in this study, the actual curve and the calibrated curve have a high degree of overlap, and both curves have a high degree of overlap with the diagonal dashed line representing the ideal state, which proves that the model in this study has good calibration ability and prediction performance and high accuracy for predicting the risk of a large number of CLNM.\u003c/p\u003e \u003cp\u003eReviewing similar work in predicting CLNM in PTC patients, there are partial differences in our study and further studies are also needed. We aimed to identify a high-risk population of patients with PTMC who need to receive a relatively more aggressive treatment modality. This study explored a subgroup of patients with PTMC who have a large (\u0026gt;\u0026thinsp;5) CLNM, accounting for approximately 10% of all patients with PTMC. These patients (ATA intermediate-risk group) may require more aggressive treatment strategies such as total thyroidectomy and postoperative radioiodine (RAI). At the same time, total thyroidectomy is the basis of postoperative radioiodine therapy. Although ATA guidelines state that patients with ATA intermediate risk DTC should consider adjuvant RAI therapy after total thyroidectomy, this recommendation level is weak and the quality of evidence is low. Furthermore, whether RAI treatment improves overall and disease-specific survival in PTMC remains controversial, even if there is conflicting evidence on the impact on recurrence. Further studies are needed to determine the benefit of RAI in PTMC patients with intermediate ATA risk. However, this study still has some limitations; first, it is a retrospective study from the SEER database, which does introduce some selection bias. Secondly, the currently used external validation cohorts are based on single-center, retrospective studies, and their sample sizes are relatively small, which may have certain biases and limitations. In order to further improve the performance and accuracy of the model, it is necessary to include multi-center, large-sample and prospective research data to refine and optimize the model. This will enable a more comprehensive assessment of the feasibility and practicability of the nomogram we constructed in clinical practice. Therefore, future studies should aim to collect data from more patients to achieve continuous optimization and validation of model performance. In addition, there were only five prognostic factors in the nomogram, suggesting that there may be potential variables waiting to be discovered and validated that could make our nomogram more complete and reliable, including but not limited to body mass index (BMI), ultrasound features, and some laboratory test results, which have been previously identified to be associated with CLNM in PTC patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBy univariate and multivariate Logistic regression analysis, male, tumor size (\u0026gt;\u0026thinsp;5mm), multifocality and extrathyroidal extension were considered to be independent risk factors for massive CLNM in PTMC patients. In contrast, age at diagnosis (\u0026ge;\u0026thinsp;55 years) was identified as a protective factor. Based on the above influencing factors, the Nomogram clinical prediction model established has good predictive value. The nomogram was successfully established and validated with five clinical indicators. This model can help surgeons make better personalized clinical decisions regarding the management of patients with papillary thyroid microcarcinoma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e We acknowledged Dr. Fenghua Zhang for the substantial contribution to thyroid surgery in our department. Also, we acknowledge the contributions of the Surveillance, Epidemiology, and End Results (SEER) Program registries for creating and updating the SEER database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e (I) Conception and design: Xuan Guo, Fenghua Zhang. (II) Administrative support: Fenghua Zhang. (III) Provision of study materials or patients: Xuan Guo, Fenghua Zhang. (IV) Collection and assembly of data: \u0026nbsp;Xuan Guo. (V) Data analysis and interpretation: Xuan Guo. (VI) Manuscript writing: all authors. (VII) Final approval of manuscript: all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e The software application generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e Ethical approval was waived by the local Ethics Committee of the Hebei General Hospital in view of the retrospective nature of the study and all the procedures being performed were part of the routine care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWei X, Min Y, Feng Y, et al. 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The American Journal of Surgery, 2020, 220（2）: 334-340.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"papillary thyroid microcarcinoma, central lymph node metastasis, logistic regression analyses, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-5662887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5662887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo develop a nomogram model for predicting the risk of high volume (\u0026gt;5) of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma and to evaluate the effectiveness of the model in clinical application, in order to achieve the goal of initial risk stratification of patients with PTMC, individualized design of the scope of surgery, and reduction the incidence of secondary surgery in patients with PTMC by the clinicians.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRetrospective analysis of clinical characteristics of patients with PTMC in the Surveillance, Epidemiology, and End Results (SEER) database between January 2016 and December 2020 and the clinical case data of patients who presented to the Gland Surgery, Hebei General Hospital, underwent surgical treatment, and were ultimately pathologically diagnosed with PTMC between January 2021 and December 2023.The clinicopathological characteristics included in the training group were screened using univariate and multivariate logistic regression analyses, to determine the independent risk factors for high volume CLNM in patients with PTMC, and to construct a nomogram model for predicting high volume CLNM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe male gender, lager tumor size(\u0026gt;5mm), multifocality, and extra-thyroidal invasion were independent risk factors for high volume CLNM in patients with papillary thyroid microcarcinoma. In contrast, elderly age(\u0026ge;\u0026thinsp;55years) at diagnosis was identified as a protective factor.Based on these independent risk factors, a nomogram model was further constructed to predict high volume CLNM.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e1 Male, large tumor diameter (\u0026gt;5mm), multifocal, and extra-thyroidal invasion were independent risk factors for high volume CLNM of patients with papillary thyroid microcarcinoma. In contrast, age\u0026thinsp;\u0026ge;\u0026thinsp;55 at the time of diagnosis was identified as a protective factor.2 The clinical prediction model based on the above mentioned factors has good predictive value, and provides a better individualized clinical decision for the management of PTMC patients by surgeons.\u003c/p\u003e","manuscriptTitle":"To Develop and Validate a Nomogram Model for Predicting High Volume (＞5) Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-25 15:37:20","doi":"10.21203/rs.3.rs-5662887/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01db87fe-9a86-4084-bff3-c5fb63c5c189","owner":[],"postedDate":"December 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-15T12:08:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-25 15:37:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5662887","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5662887","identity":"rs-5662887","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}