A Retrospective Analysis: A Predictive Model Using Platelets and Neutrophil-to- Lymphocyte Ratio for the Number of Lymph Node Metastasis in Papillary Thyroid Carcinoma

A Retrospective Analysis: A Predictive Model Using Platelets and Neutrophil-to- Lymphocyte Ratio for the Number of Lymph Node Metastasis in Papillary Thyroid Carcinoma | 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 A Retrospective Analysis: A Predictive Model Using Platelets and Neutrophil-to- Lymphocyte Ratio for the Number of Lymph Node Metastasis in Papillary Thyroid Carcinoma YuYing Chen, Fan Wu, Mengqian Ge, Tao Hu, Shuoying Qian, Yuan Cai, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4147192/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 Objective The aim of this study was to investigate the high-risk clinical factors for large-number lymph node metastases (LNLNM) inthyroid papillary carcinoma (PTC). Methods The clinicopathological data from the 731 PTC patients who underwent thyroid operation between September 2021to October 2022 in the surgical oncology of Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine were collected. Univariate and multivariate logistic analyses were conducted to identify risk factors for LNLNM in PTC. A predictive model for assessing LNLNM in PTC was established and validated by using receiver operating characteristic curves (ROC), the Hosmer-Lemeshow (HL)test, calibration curves, and decision curve analysis (DCA). Results Age, tumor diameter, platelets and neutrophil-to-lymphocyte ratio (NLR) were identified as independent risk factors for LNLNM in PTC patients. A predictive model was developed to evaluate the risk of LNLNMwith an area under the curve (AUC) of 0.827 ( P <0.001, 95%CI: 0.784-0.870) and the specificity and sensitivity were both 75.8%. The AUC of the validation group was 0.824( P <0.001, 95%CI: 0.757-0.890) with a specificity of 79.5% and a sensitivity of 76.0%. Furthermore, themodel demonstrated good calibration through the HL test and favorable diagnostic value by calibration curve and DCA. Conclusion Age, tumor diameter, platelets and NLRare high-risk factors for LNLNM in PTC, and the predictive model established in combination with the above factors couldeffectively predict the occurrence of LNLNM in PTC. This study provides support for surgeons to accurately predict the possibility of LNLNM and develop personalized treatment plans before surgery. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Papillary thyroid carcinoma (PTC) is the one of the most common endocrine tumors with its occurrence experiencing a significant surge over the past decades, leading to widespread public concern[ 1 , 2 ]. Although the majority PTCs are relatively indolent with a high 10-year survival rate, there still 20–90% of PTC patients will develop lymph node metastasis (LNM), significantly increasing the risk of recurrence and adversely affecting the prognosis[ 3 ]. Patients with more LNM have poorer outcomes than patients with fewer LNM, indicating that the number of LNM was significantly associated with prognosis of cancer and the recurrence risk[ 4 – 7 ]. The latest American Thyroid Association (ATA) guidelines have determined that if more than five LNM will reach the intermediate risk or above, the recurrence risk will vary from 4% in fewer than five LNM to 19% in more than five LNM in PTC patients[ 8 ]. Therefore, the number of LNM, specifically the number of LNM is more than five which defined as large-number lymph node metastases (LNLNM), will significantly reduce the prognosis of patients[ 9 ]. Regrettably, existing conventional methods, such as preoperative cervical ultrasound and computed tomography, could not accurately detected the LNLNM in PTC patients[ 10 ]. Consequently, there is an imperative to find a suitable approach for predicting the presence of LNLNM in PTC patients before surgery to enhance clinical decision-making and optimize treatment efficacy. An increasing findings support that the blood immune indicators are recognized as important factors in the development and prognosis of malignant tumors, including PTC[ 11 – 13 ]. Some inflammatory indicators, including neutrophils (N), lymphocytes (L), monocytes (M), platelets (Plt), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammatory index (SII) are considered effective predictors in many malignant tumors[ 14 – 17 ]. Notably, a study has established a noteworthy association between heightened Plt levels and cancer metastasis, as well as an unfavorable prognosis[ 18 ]. Additionally, an elevated NLR has been found to be correlated with a higher recurrence rate and is considered as a negative prognostic factor for cancer survival[ 19 ]. Consequently, blood inflammatory immune indicators are emerging as potentially influential biomarkers for determining cancer prognosis and LNM due to their availability and cheapness. However, the association between blood immune indicators and the prognosis in PTC, particularly in relation to LNLNM, remains uncertain. The objective of this study is to investigate the potential risk factors associated with LNLNM in PTC patients with a particular emphasis on blood immune indicators, as well as to establish and verify the predictive model. The findings of our study not only offer valuable insights for surgeons, enabling them to accurately predict the possibility of LNLNM before surgery, but also assist surgeons in devising personalized treatment strategies, ultimately leading to improved patient prognosis and quality of life. Materials and methods Patient selection In this study, the relevant data of patients with radical thyroidectomy in Hangzhou First People's Hospital of Westlake University School of Medicine (Hangzhou, China) from September 2021 to October 2022 were collected for retrospective analysis. The inclusion criteria were as follows: (1) initial radical thyroidectomy was performed for PTC, (2) standard surgical procedures included excision of at least one glandular lobe and ipsilateral lymph node dissection, (3) postoperative pathological diagnosis confirmed the presence of PTC, (4) peripheral blood routine examination was conducted within 3 days prior to surgery, (5) no concurrent malignancies were detected. The exclusion criteria were as follows: (1) presence of evident infection or inflammatory disease prior to surgery, (2) history of chemotherapy or exposure to radioactive substances before surgery, (3) incomplete preoperative and postoperative clinical data, (4) history of chronic diseases such as diabetes, hepatitis, tuberculosis or kidney disease. The flowchart of the patient selection process is shown in Fig. 1 . Data collection Fasting peripheral blood was collected in the morning 3 days before radical thyroidectomy. The blood samples were processed by using the Mindary BC-6800 automatic blood cell analyzer (Shenzhen Mairui Biomedical Electronics Co., Ltd., Shenzhen, China) to obtain the absolute values of N, M, Plt, and L. The NLR, PLR, LMR and SII were calculated based on the ratios of the aforementioned inflammatory indicators. The pathological data included tumor diameter, multifocality, and the number of LNM. In cases of multifocal tumors, analysis was performed on the largest tumor. LNLNM referred to the presence of more than five metastatic lymph nodes. Additionally, general patient information such as gender and age was collected. Statistical Analysis Since our data were non-parametric distributed, the median (M) and inter quartile range (IQR) was used for the continuous variables, while categorical variables were presented as frequency and percentage. We perform the Wilcoxon tests for continuous variables and Pearson’s Chi-square test or Fisher’s exact test for categorical variables. Variables with a significance level of P < 0.05 in the univariate analysis were selected for inclusion in the multivariate analysis. Binary logistic regression analysis was employed to establish the risk prediction model and construct the nomogram. Receiver operating characteristic curve (ROC) and area under curve (AUC) were used to evaluate the discrimination of the above model. A higher AUC value indicates better model performance. Sensitivity and specificity of the models were calculated at the maximum Youden index. Furthermore, the Hosmer-Lemeshow (HL) test is employed for the purpose of forecasting the calibration of the model. When the P > 0.05 in HL test indicates that the prediction model has a good fit. Additionally, a calibration diagram was constructed to assess the performance of the predictive model. Finally, the decision curve analysis (DCA) was used to verify the clinical net benefit rate of the predicted model. All statistical analysis and figures were were conducted utilizing R studio (version 4.3.2). Result General information This study included 731 PTC patients based on inclusion and exclusion criteria, including 200 males (27.35%) and 531 females (72.64%). A total of 176 patients (24.07%) with LNLNM while 555 patients (75.92%) without LNLNM. A random distribution of 7:3 was used to divide the PTC patients into a model group and a validation group. The model group consisted of 531 patients while the validation group consisted of 218 patients. Specifically, 124 patients (24.17%) with LNLNM and 389 patients (75.82%) without LNLNM in the model group, while 52 patients (23.85%) with LNLNM and 166 patients (76.14%) without LNLNM in the validation group. The general characteristics of the patients are shown in Table 1 . Table 1 General information of thyroid papillary carcinoma (PTC) patients in model and validation groups Model group Validation group Without LNLNM With LNLNM Without LNLNM With LNLNM (N = 389) (N = 124) (N = 166) (N = 52) Gender Female 289 (74.2%) 77 (62.1%) 126 (75.9%) 39 (75.0%) Male 100 (25.7%) 47 (37.9%) 40 (24.1%) 13 (25.0%) Age M (IQR) 46.000 (35.000-55.500) 36.000 (30.000–47.000) 48.000 (36.000–58.000) 40.000 (30.000–49.000) Tumor Diameter (mm) M (IQR) 8.000 (6.000–12.000) 14.000 (10.000–22.000) 8.000 (6.000–12.000) 13.000 (9.250–23.500) Multiplicity Yes 104 (26.7%) 40 (32.3%) 122 (73.5%) 18 (34.6%) No 285 (73.3%) 84 (67.7%) 44 (26.5%) 34 (67.3%) Plt M (IQR) 215.000 (184.000-255.000) 233.500 (198.000-274.800) 226.500 (196.800-259.300) 224.000 (194.300–264.000) N M (IQR) 3.000 (2.400–3.850) 3.300 (2.800–4.100) 3.200 (2.600–3.900) 3.645 (3.645–4.650) L M (IQR) 1.800 (1.500–2.200) 1.830 (1.500–2.200) 1.800 (1.500-2.000) 1.900 (1.503–2.155) M M (IQR) 0.300 (0.295-0.400) 0.400 (0.300-0.495) 0.300 (0.300–0.400) 0.400 (0.300–0.500) PLR M (IQR) 117.500 (92.340–143.300) 121.300 (102.500-154.800) 123.500 (99.230–153.800) 126.700 (100.800-156.900) NLR M (IQR) 1.692 (1.325–2.106) 1.760 (1.381–2.333) 1.769 (1.413–2.351) 1.890 (1.564–2.573) LMR M (IQR) 5.667 (4.500-7.292) 5.333 (4.250–6.650) 5.708 (4.333–7.050) 5.000 (4.000-6.500) SII M (IQR) 361.500 (265.600-485.300) 431.200 (325.900-589.900) 399.100 (295.200-537.600) 452.400 (376.700-616.600) Screening of predictive model variables The results of the univariate analysis demonstrated that age, gender, tumor diameter, Plt, N, M, NLR, LMR, PLR, and SII are significant predictors of LNLNM in PTC patients, with a statistically significant correlation ( P 0.05). The corresponding P-values could be found in the Table 2 . Significant factors in the univariate analysis were then included in the multivariate analysis. Multivariate logistic regression analysis demonstrated that age ( P < 0.001 OR: 0.959, 95%CI: 0.939–0.978), tumor diameter ( P < 0.001 OR: 1.149, 95%CI: 1.111–1.193), Plt ( P < 0.01 OR: 1.006, 95%CI: 1.002–1.011) and NLR ( P < 0.01 OR: 1.604, 95%CI: 1.166–2.198) were the independent risk factors for LNLNM in PTC patients. The specific values are shown in Table 2 Table 2 Results of univariate regression analysis and logistic multivariate regression analysis P-value(Univariate analysis) P-value(Logistic analysis) OR(Logistic analysis) 95%CI(Logistic analysis) Gender 0.014 Age < 0.001 < 0.001 0.959 0.939–0.978 Tumor Diameter (mm) < 0.001 < 0.001 1.149 1.111–1.193 Multiplicity 0.249 Plt < 0.001 < 0.01 1.006 1.002–1.011 N 0.002 L 0.361 M 0.499 PLR 0.038 NLR 0.021 < 0.01 1.604 1.166–2.198 LMR 0.045 SII < 0.001 Model construction and validation The logistic multiple regression analysis findings were incorporated into the construction of the prediction model, which identified age, tumor diameter, NLR, and Plt. Consequently, these independent predictors were combined to construct a predictive model for LNLNM in PTC patients (Fig. 2 ). The ROC curve was generated based on the logistic multiple regression outcomes. The area under the curve (AUC) of the model was 0.827 ( P < 0.001, 95%CI: 0.784–0.870), which indicates excellent discrimination. When the Youden index was the highest, the specificity and sensitivity were 75.8% (Fig. 3 a). The HL test showed good goodness-of-fit ( P = 0.464), indicating that the predictive model had good calibration in predicting the probability of LNLNM. Besides, the calibration curves showed good agreement between the predicted and observed probabilities of LNLNM, with a mean absolute error of 0.014 (Fig. 4 a). Additionally, the DCA demonstrated the effectiveness of the nomograms across a wide range of clinical utility (Fig. 5 ) . A total of 218 validation data were included to verify the generalization of the model. The results showed that the AUC of the validation group was 0.824 ( P < 0.001, 95% CI: 0.757–0.890). The specificity was 79.5% and the sensitivity was 76.9% at the maximum Youden index, which means the model has good repeatability and wide applicability (Fig. 3 b ) . The HL test showed a great calibration in the validation group ( P = 0.219). In addition, the calibration curve and the DCA of the validation group also indicated that the model had good diagnostic value (Fig. 4 b and 5 ) . Discussion Universally acknowledged, PTC is the most prevalent malignant disease in the endocrine system, with 20–90% of cases experiencing the LNM. The occurrence of LNM not only elevates the risk of recurrence but also significantly diminishes the patient’s prognosis and overall quality of life[ 3 , 20 , 21 ]. Patients exhibiting a greater number of LNM tend to experience inferior outcomes compared to those with a less quantity, especially the recurrence risk in PTC significantly increases 5 times when there are more than five metastatic lymph nodes[ 8 , 22 , 23 ]. Similarly, another study discovered that recurrence occurred in up to 37.3% of patients with more than five LNM[ 24 ]. Notably, the number of LNM greater than 5 will significantly reduces the prognosis in PTC patients, arousing the attention of clinicians. Therefore, in this study, we artificially defined the presence of LNM more than five as LNLNM referring to previous studies, and explore the risk factors of LNLNM[ 9 ]. Analysis found that age, tumor diameter, NLR, and Plt were independent risk factors for LNLNM in PTC patients. Also, the prediction model provides precise predictions for LNLNM in PTC patients, thereby offering enhanced guidance for clinical practice. It has been documented that blood immune indicators is closely linked to cancers, holding significant research value[ 25 , 26 ]. Many indicators have demonstrated effective predictive capabilities for the presence and prognosis of malignancies across various types of cancers[ 27 – 29 ]. NLR, as a more popular and reliable blood immune indicators compared to others, had been extensively utilized as a long-term predictor of outcomes across various malignancies[ 30 , 31 ]. Preoperatively elevated NLR has been identified as a negative prognostic factor for survival in various types of cancers[ 19 , 32 – 34 ]. In addition, it was discovered that NLR was associated with the clinicopathologic aggressiveness of PTC and could be used as marker for risk assessment in patients with PTC[ 29 ]. Similarly, our investigation found that higher NLR levels tended to be present in patients with LNLNM, which was indicative of a poor prognosis. Interestingly, Natalia’s study found that it is the increased neutrophils rather than decreased lymphocytes that drive the predictive value of NLR.[ 35 ]. Neutrophils play a critical role in the metastasis of cancer cells[ 36 , 37 ]. This may be because neutrophils have the ability to promote cancer growth by releasing growth factors and granular proteins, as well as inducing the dissociation of tumor cells through remodeling of the extracellular matrix, thus preparing the tumor cells for metastasis[ 34 , 38 ]. Additionally, the neutrophils will induce the metastasis of tumor cells by secreting tumor necrosis factor and Cathepsin G[ 39 ]. Furthermore, the neutrophils may suppress the immune system, allowing tumor cells to avoid being killed by immune cells during metastasis[ 40 ]. Based on these findings, it is suggested that high levels of neutrophils and NLR may be associated with the metastasis of PTC, making them potential markers for predicting LNLNM in PTC patients. Platelets are a crucial component of the circulatory system that play a significant role in maintaining hemostasis and various pathological processes, such as inflammation, atherosclerosis, and cancer metastasis[ 41 , 42 ]. Clinically, high level of platelets has been associated with an unfavorable prognosis in a number of malignancies, including ovarian, lung, gastric and breast cancers[ 18 , 43 – 45 ]. Our study reveals that a high level of platelets is associated with LNLNM in PTC, indicating a poor prognosis. Additionally, previous research has reported that platelets can serve as useful predictors of thyroid cancer malignancy and LNM[ 46 ]. The mechanistic process by which elevated levels of platelets contribute to the metastasis to lymph nodes by forming the platelet coating, increasing surface P-selectin expression and promoting lymphatic and blood vessel formation[ 47 – 51 ].This suggests that an increase in platelets levels plays a crucial role in facilitating cancer metastasis. Furthermore, platelets may serve as a valuable predictor for the presence of LNLNM in PTC and assessing the risk stratification of the disease. Moreover, previous research has predominantly relied on ultrasound characteristics to develop models for predicting the number of LNM in PTC patients[ 9 ]. Notably, ultrasound factors such as nodule size, multifocal disease and taller-than-wide shape, of LNM have been identified as independent predictors with superior predictive efficacy. Nevertheless, ultrasound has a low sensitivity in the diagnosis of cervical LNM[ 10 ]. According to various studies, the false-positive and false-negative rate of ultrasound to palpable LNM both range from 20–30%[ 52 ]. Conversely, blood inflammation indicators offer widely applicable and ease of acquisition, having a significant and potential value[ 53 ]. In this study, we used preoperative blood inflammation indicators for the first time to predict the presence of LNLNM in PTC. The ROC and HL tests showed the prediction model of LNLNM in PTC patients has good discrimination and calibration. The calibration curve and DCA, both in model and validation group, indicate that the model had a good predictive ability. In summary, this prediction model improves the prediction of LNLNM in PTC patients, aiding surgeons in evaluating patients' conditions and determining personalized treatment plans. However, our study has several limitations that should be acknowledged. Firstly, it is important to note that our prediction model is a single-center study, which may limit the generalizability of our findings. In the follow-up study, we should include more centers for large-sample verification to improve the accuracy of the prediction model. Secondly, it is worth mentioning that our study is a retrospective study, introducing the possibility of selection bias in the sample selection process. To address this limitation, we recommend to combine prospective studies in the future. Finally, the diameter of metastatic lymph nodes was not included in the analysis due to its difficult to measure their diameter accurately. In subsequent studies, it is crucial to incorporate both the size and number of lymph nodes to obtain a more comprehensive evaluation of the prognosis. Conclusions In conclusion, age, tumor diameter, NLR and Plt are identified as significant risk factors for LNLNM in PTC patients, and the utilization of a prediction model incorporating these aforementioned factors can yield accurate predictions for LNLNM in PTC patients, thereby offering improved guidance for clinical practice. Abbreviations LNLNM large-number lymph node metastases PTC thyroid papillary carcinoma ROC receiver operating characteristic curves HL Hosmer-Lemeshow DCA decision curve analysis NLR neutrophil-to-lymphocyte ratio AUC area under the curve LNM lymph node metastasis ATA American Thyroid Association N neutrophils L lymphocytes M monocytes Plt platelets LMR lymphocyte-to-monocyte ratio PLR platelet-to-lymphocyte ratio SII systemic immune-inflammatory index M median IQR inter quartile range Declarations Ethics approval and consent to participate This study was approved by Ethics Committee of Hangzhou First People’s Hospital and written informed consent was obtained from all patients. Acknowledgements Not applicable. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request Competing interests The authors declare no competing interests. Funding This work was supported by Zhejiang Provincial Basic Public Welfare Research Project (grant numbers: LGF22H160082), Zhejiang Provincial Medical and Health Technology Project (grant numbers: 2022KY939), and the Medical and Health Technology Projects of Hangzhou (grant numbers: Z20210025). Author contribution statement Yuying Chen and Fan Wu conceptualized and designed the study. Shuoying Qian, Yuan Cai, Xuanwei Huang and Kaiyuan Huang collected and analyzed the data. Yuying Chen wrote the original draft. Mengqian Ge and Tao Hu revised the manuscript. Dingcun Luo and Gang Pan reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version. Author details 1 The Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China, 310053; 2 Department of Surgical Oncology, Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China, 310006. 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Haemmerle M, Stone RL, Menter DG, Afshar-Kharghan V, Sood AK. The Platelet Lifeline to Cancer: Challenges and Opportunities. Cancer Cell. 2018;33:965-83. Stone RL, Nick AM, McNeish IA, Balkwill F, Han HD, Bottsford-Miller J, et al. Paraneoplastic thrombocytosis in ovarian cancer. N Engl J Med. 2012;366:610-8. Pedersen LM, Milman N. Prognostic significance of thrombocytosis in patients with primary lung cancer. Eur Respir J. 1996;9:1826-30. Ikeda M, Furukawa H, Imamura H, Shimizu J, Ishida H, Masutani S, et al. Poor prognosis associated with thrombocytosis in patients with gastric cancer. Ann Surg Oncol. 2002;9:287-91. Wang X, Liu W, Zhao M. [Correlation between preoperative platelet parameters and clinicopathological features of differentiated thyroid cancer]. Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2023;37:796-800. Kim YJ, Borsig L, Varki NM, Varki A. P-selectin deficiency attenuates tumor growth and metastasis. Proc Natl Acad Sci U S A. 1998;95:9325-30. Placke T, Salih HR, Kopp HG. GITR ligand provided by thrombopoietic cells inhibits NK cell antitumor activity. J Immunol. 2012;189:154-60. Nieswandt B, Hafner M, Echtenacher B, Männel DN. Lysis of tumor cells by natural killer cells in mice is impeded by platelets. Cancer Res. 1999;59:1295-300. Lala PK, Nandi P, Majumder M. Roles of prostaglandins in tumor-associated lymphangiogenesis with special reference to breast cancer. Cancer Metastasis Rev. 2018;37:369-84. Möhle R, Green D, Moore MA, Nachman RL, Rafii S. Constitutive production and thrombin-induced release of vascular endothelial growth factor by human megakaryocytes and platelets. Proc Natl Acad Sci U S A. 1997;94:663-8. Qiu Y, Fei Y, Liu J, Liu C, He X, Zhu N, et al. Prevalence, Risk Factors And Location Of Skip Metastasis In Papillary Thyroid Carcinoma: A Systematic Review And Meta-Analysis. Cancer Manag Res. 2019;11:8721-30. Luo QQ, Wang T, Zhang KH. New indexes derived from routine blood tests and their clinical application in hepatocellular carcinoma. Clin Res Hepatol Gastroenterol. 2022;46:102043. 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-4147192","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283325114,"identity":"36814668-59cc-4a67-8317-11c8b3d48ee8","order_by":0,"name":"YuYing Chen","email":"","orcid":"","institution":"The Fourth Clinical Medical College, Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"YuYing","middleName":"","lastName":"Chen","suffix":""},{"id":283325115,"identity":"2d00b3be-9c52-400d-8c77-184a22833bb8","order_by":1,"name":"Fan Wu","email":"","orcid":"","institution":"Department of Oncological Surgery, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Wu","suffix":""},{"id":283325116,"identity":"54517a52-156c-48f1-bc6e-2e529a801fe7","order_by":2,"name":"Mengqian Ge","email":"","orcid":"","institution":"The Fourth Clinical Medical College, Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengqian","middleName":"","lastName":"Ge","suffix":""},{"id":283325117,"identity":"2e45102e-5d5e-4f40-ae32-4c90860923c0","order_by":3,"name":"Tao Hu","email":"","orcid":"","institution":"The Fourth Clinical Medical College, Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Hu","suffix":""},{"id":283325118,"identity":"54501ab1-e635-43c5-8926-ba544c48ab18","order_by":4,"name":"Shuoying Qian","email":"","orcid":"","institution":"The Fourth Clinical Medical College, Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuoying","middleName":"","lastName":"Qian","suffix":""},{"id":283325119,"identity":"fe2e4a4c-4f91-4b7c-8ae3-bf1d2f83fc73","order_by":5,"name":"Yuan Cai","email":"","orcid":"","institution":"The Fourth Clinical Medical College, Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Cai","suffix":""},{"id":283325120,"identity":"6f0a9da9-a7a9-42ae-badc-a0d17f249b9c","order_by":6,"name":"Xuanwei Huang","email":"","orcid":"","institution":"The Fourth Clinical Medical College, Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuanwei","middleName":"","lastName":"Huang","suffix":""},{"id":283325121,"identity":"c1da56cb-9c76-4930-9294-4978d1dfe33e","order_by":7,"name":"Kaiyuan Huang","email":"","orcid":"","institution":"The Fourth Clinical Medical College, Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kaiyuan","middleName":"","lastName":"Huang","suffix":""},{"id":283325122,"identity":"af56486d-83e1-4582-a577-f7919509a96d","order_by":8,"name":"Gang Pan","email":"","orcid":"","institution":"Department of Oncological Surgery, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Pan","suffix":""},{"id":283325123,"identity":"6a9c299e-d02e-4657-b8b5-50d8d2eb486e","order_by":9,"name":"Dingcun Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACPoYExsc/KhiYQRwJorSwMSQwGzOcIVELmzBjG4RDpBb25GfMhfPq2A0OMB+8zcNgl0dYC88zs8czt7ExGxxgS7bmYUguJqxFIofdgHcbD1ALj5k0D8OBxAYitLBJ8M6RAGrh/0a8FmneBgOQLWxEauF5Zmw441gCs+RhNmPLOQbJhLXwsyc/fPChpi6Z73jzwxtvKuwIa4GBZEhkGhCrHgjsSFA7CkbBKBgFIw0AAJfRMOfVKAbYAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Oncological Surgery, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou","correspondingAuthor":true,"prefix":"","firstName":"Dingcun","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-03-22 05:29:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4147192/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4147192/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53673196,"identity":"7f4b4785-d0d7-4de7-a6d3-4c57aef8b23f","added_by":"auto","created_at":"2024-03-28 18:21:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":283584,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of the patient selection process\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4147192/v1/1f35b71cdcd0478c8c73b85b.png"},{"id":53673193,"identity":"d66725bc-3024-4e90-a2c4-cb5c759a4c9b","added_by":"auto","created_at":"2024-03-28 18:21:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92779,"visible":true,"origin":"","legend":"\u003cp\u003eClinical factor-based nomogram used for preoperatively predicting the LNLNM\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4147192/v1/3db0753b6cd305254a847a91.png"},{"id":53673192,"identity":"ef928ca0-2f1a-478d-9207-6d894242075e","added_by":"auto","created_at":"2024-03-28 18:21:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85846,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operator characteristic curve of model group (a) and validation group(b)\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4147192/v1/156e895bdea0ad4783eb33b4.png"},{"id":53673195,"identity":"f3ba56d7-bd14-4e4b-abc9-73b24ffb33c4","added_by":"auto","created_at":"2024-03-28 18:21:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":198875,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of model group (a) and validation group(b). The X-axis represents the predicted probability, while the Y-axis represents the actual probability of LNLNM. The diagonal dashed line represents the ideal prediction model. The solid line represents the performance of the column chart model, with a higher fit to the diagonal dashed line indicating better predictive capability. LNLNM, large number lymph node metastases.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4147192/v1/be91ad7ca225813d8a352549.png"},{"id":53673194,"identity":"fb5cecb2-4477-468e-a5ba-e22c7506d9d0","added_by":"auto","created_at":"2024-03-28 18:21:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66557,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of model group and validation group. The net benefit is indicated on the Y-axis. The black line represents the assumption that all patients have severe morbidity. The gray line represents the assumption that no patient has severe morbidity. The red line represents the prediction model. The blue line represents the validation group of the model.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4147192/v1/7b5c5676c0f70a92c084a54e.png"},{"id":53696227,"identity":"6c185923-3fd5-4601-b711-daf561380f4f","added_by":"auto","created_at":"2024-03-29 03:44:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1005414,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4147192/v1/bf6e170d-00dc-4be1-ab8c-ad5d53fec84c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Retrospective Analysis: A Predictive Model Using Platelets and Neutrophil-to- Lymphocyte Ratio for the Number of Lymph Node Metastasis in Papillary Thyroid Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePapillary thyroid carcinoma (PTC) is the one of the most common endocrine tumors with its occurrence experiencing a significant surge over the past decades, leading to widespread public concern[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although the majority PTCs are relatively indolent with a high 10-year survival rate, there still 20\u0026ndash;90% of PTC patients will develop lymph node metastasis (LNM), significantly increasing the risk of recurrence and adversely affecting the prognosis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Patients with more LNM have poorer outcomes than patients with fewer LNM, indicating that the number of LNM was significantly associated with prognosis of cancer and the recurrence risk[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The latest American Thyroid Association (ATA) guidelines have determined that if more than five LNM will reach the intermediate risk or above, the recurrence risk will vary from 4% in fewer than five LNM to 19% in more than five LNM in PTC patients[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, the number of LNM, specifically the number of LNM is more than five which defined as large-number lymph node metastases (LNLNM), will significantly reduce the prognosis of patients[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Regrettably, existing conventional methods, such as preoperative cervical ultrasound and computed tomography, could not accurately detected the LNLNM in PTC patients[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, there is an imperative to find a suitable approach for predicting the presence of LNLNM in PTC patients before surgery to enhance clinical decision-making and optimize treatment efficacy.\u003c/p\u003e \u003cp\u003eAn increasing findings support that the blood immune indicators are recognized as important factors in the development and prognosis of malignant tumors, including PTC[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Some inflammatory indicators, including neutrophils (N), lymphocytes (L), monocytes (M), platelets (Plt), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammatory index (SII) are considered effective predictors in many malignant tumors[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Notably, a study has established a noteworthy association between heightened Plt levels and cancer metastasis, as well as an unfavorable prognosis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, an elevated NLR has been found to be correlated with a higher recurrence rate and is considered as a negative prognostic factor for cancer survival[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consequently, blood inflammatory immune indicators are emerging as potentially influential biomarkers for determining cancer prognosis and LNM due to their availability and cheapness. However, the association between blood immune indicators and the prognosis in PTC, particularly in relation to LNLNM, remains uncertain.\u003c/p\u003e \u003cp\u003eThe objective of this study is to investigate the potential risk factors associated with LNLNM in PTC patients with a particular emphasis on blood immune indicators, as well as to establish and verify the predictive model. The findings of our study not only offer valuable insights for surgeons, enabling them to accurately predict the possibility of LNLNM before surgery, but also assist surgeons in devising personalized treatment strategies, ultimately leading to improved patient prognosis and quality of life.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection\u003c/h2\u003e \u003cp\u003eIn this study, the relevant data of patients with radical thyroidectomy in Hangzhou First People's Hospital of Westlake University School of Medicine (Hangzhou, China) from September 2021 to October 2022 were collected for retrospective analysis. The inclusion criteria were as follows: (1) initial radical thyroidectomy was performed for PTC, (2) standard surgical procedures included excision of at least one glandular lobe and ipsilateral lymph node dissection, (3) postoperative pathological diagnosis confirmed the presence of PTC, (4) peripheral blood routine examination was conducted within 3 days prior to surgery, (5) no concurrent malignancies were detected. The exclusion criteria were as follows: (1) presence of evident infection or inflammatory disease prior to surgery, (2) history of chemotherapy or exposure to radioactive substances before surgery, (3) incomplete preoperative and postoperative clinical data, (4) history of chronic diseases such as diabetes, hepatitis, tuberculosis or kidney disease. The flowchart of the patient selection process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eFasting peripheral blood was collected in the morning 3 days before radical thyroidectomy. The blood samples were processed by using the Mindary BC-6800 automatic blood cell analyzer (Shenzhen Mairui Biomedical Electronics Co., Ltd., Shenzhen, China) to obtain the absolute values of N, M, Plt, and L. The NLR, PLR, LMR and SII were calculated based on the ratios of the aforementioned inflammatory indicators. The pathological data included tumor diameter, multifocality, and the number of LNM. In cases of multifocal tumors, analysis was performed on the largest tumor. LNLNM referred to the presence of more than five metastatic lymph nodes. Additionally, general patient information such as gender and age was collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSince our data were non-parametric distributed, the median (M) and inter quartile range (IQR) was used for the continuous variables, while categorical variables were presented as frequency and percentage. We perform the Wilcoxon tests for continuous variables and Pearson\u0026rsquo;s Chi-square test or Fisher\u0026rsquo;s exact test for categorical variables. Variables with a significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were selected for inclusion in the multivariate analysis. Binary logistic regression analysis was employed to establish the risk prediction model and construct the nomogram. Receiver operating characteristic curve (ROC) and area under curve (AUC) were used to evaluate the discrimination of the above model. A higher AUC value indicates better model performance. Sensitivity and specificity of the models were calculated at the maximum Youden index. Furthermore, the Hosmer-Lemeshow (HL) test is employed for the purpose of forecasting the calibration of the model. When the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 in HL test indicates that the prediction model has a good fit. Additionally, a calibration diagram was constructed to assess the performance of the predictive model. Finally, the decision curve analysis (DCA) was used to verify the clinical net benefit rate of the predicted model. All statistical analysis and figures were were conducted utilizing R studio (version 4.3.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGeneral information\u003c/h2\u003e \u003cp\u003eThis study included 731 PTC patients based on inclusion and exclusion criteria, including 200 males (27.35%) and 531 females (72.64%). A total of 176 patients (24.07%) with LNLNM while 555 patients (75.92%) without LNLNM. A random distribution of 7:3 was used to divide the PTC patients into a model group and a validation group. The model group consisted of 531 patients while the validation group consisted of 218 patients. Specifically, 124 patients (24.17%) with LNLNM and 389 patients (75.82%) without LNLNM in the model group, while 52 patients (23.85%) with LNLNM and 166 patients (76.14%) without LNLNM in the validation group. The general characteristics of the patients are shown in Table\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\u003eGeneral information of thyroid papillary carcinoma (PTC) patients in model and validation groups\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eValidation group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout LNLNM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith LNLNM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWithout LNLNM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eWith LNLNM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;389)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;166)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e289 (74.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126 (75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e39 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e13 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.000 (35.000-55.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.000\u003c/p\u003e \u003cp\u003e(30.000\u0026ndash;47.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.000\u003c/p\u003e \u003cp\u003e(36.000\u0026ndash;58.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e40.000\u003c/p\u003e \u003cp\u003e(30.000\u0026ndash;49.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor Diameter (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.000\u003c/p\u003e \u003cp\u003e(6.000\u0026ndash;12.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.000\u003c/p\u003e \u003cp\u003e(10.000\u0026ndash;22.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.000\u003c/p\u003e \u003cp\u003e(6.000\u0026ndash;12.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e13.000\u003c/p\u003e \u003cp\u003e(9.250\u0026ndash;23.500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMultiplicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 (73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e18 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (73.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (67.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e34 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlt\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215.000 (184.000-255.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233.500 (198.000-274.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e226.500\u003c/p\u003e \u003cp\u003e(196.800-259.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e224.000\u003c/p\u003e \u003cp\u003e(194.300\u0026ndash;264.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003cp\u003e(2.400\u0026ndash;3.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.300\u003c/p\u003e \u003cp\u003e(2.800\u0026ndash;4.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.200\u003c/p\u003e \u003cp\u003e(2.600\u0026ndash;3.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.645\u003c/p\u003e \u003cp\u003e(3.645\u0026ndash;4.650)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.800\u003c/p\u003e \u003cp\u003e(1.500\u0026ndash;2.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.830\u003c/p\u003e \u003cp\u003e(1.500\u0026ndash;2.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.800\u003c/p\u003e \u003cp\u003e(1.500-2.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.900\u003c/p\u003e \u003cp\u003e(1.503\u0026ndash;2.155)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003cp\u003e(0.295-0.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003cp\u003e(0.300-0.495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003cp\u003e(0.300\u0026ndash;0.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003cp\u003e(0.300\u0026ndash;0.500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.500 (92.340\u0026ndash;143.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.300 (102.500-154.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123.500 (99.230\u0026ndash;153.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e126.700\u003c/p\u003e \u003cp\u003e(100.800-156.900)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.692\u003c/p\u003e \u003cp\u003e(1.325\u0026ndash;2.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.760\u003c/p\u003e \u003cp\u003e(1.381\u0026ndash;2.333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.769\u003c/p\u003e \u003cp\u003e(1.413\u0026ndash;2.351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.890\u003c/p\u003e \u003cp\u003e(1.564\u0026ndash;2.573)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLMR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.667\u003c/p\u003e \u003cp\u003e(4.500-7.292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.333\u003c/p\u003e \u003cp\u003e(4.250\u0026ndash;6.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.708\u003c/p\u003e \u003cp\u003e(4.333\u0026ndash;7.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.000\u003c/p\u003e \u003cp\u003e(4.000-6.500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e361.500 (265.600-485.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e431.200 (325.900-589.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e399.100 (295.200-537.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e452.400 (376.700-616.600)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eScreening of predictive model variables\u003c/h2\u003e \u003cp\u003eThe results of the univariate analysis demonstrated that age, gender, tumor diameter, Plt, N, M, NLR, LMR, PLR, and SII are significant predictors of LNLNM in PTC patients, with a statistically significant correlation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, there was no statistically significant correlation in multiplicity and L (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The corresponding P-values could be found in the Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSignificant factors in the univariate analysis were then included in the multivariate analysis. Multivariate logistic regression analysis demonstrated that age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 OR: 0.959, 95%CI: 0.939\u0026ndash;0.978), tumor diameter (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 OR: 1.149, 95%CI: 1.111\u0026ndash;1.193), Plt (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 OR: 1.006, 95%CI: 1.002\u0026ndash;1.011) and NLR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 OR: 1.604, 95%CI: 1.166\u0026ndash;2.198) were the independent risk factors for LNLNM in PTC patients. The specific values are shown 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\u003eResults of univariate regression analysis and logistic multivariate regression analysis\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value(Univariate analysis)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value(Logistic analysis)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(Logistic analysis)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI(Logistic analysis)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014\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\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.939\u0026ndash;0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor Diameter (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.111\u0026ndash;1.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultiplicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.249\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\u003cb\u003ePlt\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.002\u0026ndash;1.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\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\u003cb\u003eL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.361\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\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.499\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\u003cb\u003ePLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.038\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\u003cb\u003eNLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.166\u0026ndash;2.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLMR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.045\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\u003cb\u003eSII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eModel construction and validation\u003c/h2\u003e \u003cp\u003eThe logistic multiple regression analysis findings were incorporated into the construction of the prediction model, which identified age, tumor diameter, NLR, and Plt. Consequently, these independent predictors were combined to construct a predictive model for LNLNM in PTC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The ROC curve was generated based on the logistic multiple regression outcomes. The area under the curve (AUC) of the model was 0.827 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95%CI: 0.784\u0026ndash;0.870), which indicates excellent discrimination. When the Youden index was the highest, the specificity and sensitivity were 75.8% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The HL test showed good goodness-of-fit (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.464), indicating that the predictive model had good calibration in predicting the probability of LNLNM. Besides, the calibration curves showed good agreement between the predicted and observed probabilities of LNLNM, with a mean absolute error of 0.014 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Additionally, the DCA demonstrated the effectiveness of the nomograms across a wide range of clinical utility (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eA total of 218 validation data were included to verify the generalization of the model. The results showed that the AUC of the validation group was 0.824 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI: 0.757\u0026ndash;0.890). The specificity was 79.5% and the sensitivity was 76.9% at the maximum Youden index, which means the model has good repeatability and wide applicability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. The HL test showed a great calibration in the validation group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.219). In addition, the calibration curve and the DCA of the validation group also indicated that the model had good diagnostic value (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUniversally acknowledged, PTC is the most prevalent malignant disease in the endocrine system, with 20\u0026ndash;90% of cases experiencing the LNM. The occurrence of LNM not only elevates the risk of recurrence but also significantly diminishes the patient\u0026rsquo;s prognosis and overall quality of life[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Patients exhibiting a greater number of LNM tend to experience inferior outcomes compared to those with a less quantity, especially the recurrence risk in PTC significantly increases 5 times when there are more than five metastatic lymph nodes[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, another study discovered that recurrence occurred in up to 37.3% of patients with more than five LNM[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Notably, the number of LNM greater than 5 will significantly reduces the prognosis in PTC patients, arousing the attention of clinicians. Therefore, in this study, we artificially defined the presence of LNM more than five as LNLNM referring to previous studies, and explore the risk factors of LNLNM[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Analysis found that age, tumor diameter, NLR, and Plt were independent risk factors for LNLNM in PTC patients. Also, the prediction model provides precise predictions for LNLNM in PTC patients, thereby offering enhanced guidance for clinical practice.\u003c/p\u003e \u003cp\u003eIt has been documented that blood immune indicators is closely linked to cancers, holding significant research value[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Many indicators have demonstrated effective predictive capabilities for the presence and prognosis of malignancies across various types of cancers[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. NLR, as a more popular and reliable blood immune indicators compared to others, had been extensively utilized as a long-term predictor of outcomes across various malignancies[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Preoperatively elevated NLR has been identified as a negative prognostic factor for survival in various types of cancers[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In addition, it was discovered that NLR was associated with the clinicopathologic aggressiveness of PTC and could be used as marker for risk assessment in patients with PTC[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, our investigation found that higher NLR levels tended to be present in patients with LNLNM, which was indicative of a poor prognosis. Interestingly, Natalia\u0026rsquo;s study found that it is the increased neutrophils rather than decreased lymphocytes that drive the predictive value of NLR.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Neutrophils play a critical role in the metastasis of cancer cells[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This may be because neutrophils have the ability to promote cancer growth by releasing growth factors and granular proteins, as well as inducing the dissociation of tumor cells through remodeling of the extracellular matrix, thus preparing the tumor cells for metastasis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, the neutrophils will induce the metastasis of tumor cells by secreting tumor necrosis factor and Cathepsin G[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, the neutrophils may suppress the immune system, allowing tumor cells to avoid being killed by immune cells during metastasis[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Based on these findings, it is suggested that high levels of neutrophils and NLR may be associated with the metastasis of PTC, making them potential markers for predicting LNLNM in PTC patients.\u003c/p\u003e \u003cp\u003ePlatelets are a crucial component of the circulatory system that play a significant role in maintaining hemostasis and various pathological processes, such as inflammation, atherosclerosis, and cancer metastasis[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Clinically, high level of platelets has been associated with an unfavorable prognosis in a number of malignancies, including ovarian, lung, gastric and breast cancers[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Our study reveals that a high level of platelets is associated with LNLNM in PTC, indicating a poor prognosis. Additionally, previous research has reported that platelets can serve as useful predictors of thyroid cancer malignancy and LNM[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The mechanistic process by which elevated levels of platelets contribute to the metastasis to lymph nodes by forming the platelet coating, increasing surface P-selectin expression and promoting lymphatic and blood vessel formation[\u003cspan additionalcitationids=\"CR48 CR49 CR50\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].This suggests that an increase in platelets levels plays a crucial role in facilitating cancer metastasis. Furthermore, platelets may serve as a valuable predictor for the presence of LNLNM in PTC and assessing the risk stratification of the disease.\u003c/p\u003e \u003cp\u003eMoreover, previous research has predominantly relied on ultrasound characteristics to develop models for predicting the number of LNM in PTC patients[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Notably, ultrasound factors such as nodule size, multifocal disease and taller-than-wide shape, of LNM have been identified as independent predictors with superior predictive efficacy. Nevertheless, ultrasound has a low sensitivity in the diagnosis of cervical LNM[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to various studies, the false-positive and false-negative rate of ultrasound to palpable LNM both range from 20\u0026ndash;30%[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Conversely, blood inflammation indicators offer widely applicable and ease of acquisition, having a significant and potential value[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In this study, we used preoperative blood inflammation indicators for the first time to predict the presence of LNLNM in PTC. The ROC and HL tests showed the prediction model of LNLNM in PTC patients has good discrimination and calibration. The calibration curve and DCA, both in model and validation group, indicate that the model had a good predictive ability. In summary, this prediction model improves the prediction of LNLNM in PTC patients, aiding surgeons in evaluating patients' conditions and determining personalized treatment plans.\u003c/p\u003e \u003cp\u003eHowever, our study has several limitations that should be acknowledged. Firstly, it is important to note that our prediction model is a single-center study, which may limit the generalizability of our findings. In the follow-up study, we should include more centers for large-sample verification to improve the accuracy of the prediction model. Secondly, it is worth mentioning that our study is a retrospective study, introducing the possibility of selection bias in the sample selection process. To address this limitation, we recommend to combine prospective studies in the future. Finally, the diameter of metastatic lymph nodes was not included in the analysis due to its difficult to measure their diameter accurately. In subsequent studies, it is crucial to incorporate both the size and number of lymph nodes to obtain a more comprehensive evaluation of the prognosis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, age, tumor diameter, NLR and Plt are identified as significant risk factors for LNLNM in PTC patients, and the utilization of a prediction model incorporating these aforementioned factors can yield accurate predictions for LNLNM in PTC patients, thereby offering improved guidance for clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLNLNM\u0026nbsp;large-number lymph node metastases\u003c/p\u003e\n\u003cp\u003ePTC\u0026nbsp;thyroid papillary carcinoma\u003c/p\u003e\n\u003cp\u003eROC\u0026nbsp;receiver operating characteristic curves\u003c/p\u003e\n\u003cp\u003eHL\u0026nbsp;Hosmer-Lemeshow\u003c/p\u003e\n\u003cp\u003eDCA\u0026nbsp;decision curve analysis\u003c/p\u003e\n\u003cp\u003eNLR\u0026nbsp;neutrophil-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp;area under the curve\u003c/p\u003e\n\u003cp\u003eLNM\u0026nbsp;lymph node metastasis\u003c/p\u003e\n\u003cp\u003eATA\u0026nbsp;American Thyroid Association\u003c/p\u003e\n\u003cp\u003eN neutrophils\u003c/p\u003e\n\u003cp\u003eL lymphocytes\u003c/p\u003e\n\u003cp\u003eM monocytes\u003c/p\u003e\n\u003cp\u003ePlt platelets\u003c/p\u003e\n\u003cp\u003eLMR lymphocyte-to-monocyte ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePLR platelet-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eSII systemic immune-inflammatory index\u003c/p\u003e\n\u003cp\u003eM median\u003c/p\u003e\n\u003cp\u003eIQR inter quartile range\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by Ethics Committee of Hangzhou First People\u0026rsquo;s Hospital and written informed consent was obtained from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Zhejiang Provincial Basic Public Welfare Research Project (grant numbers: LGF22H160082), Zhejiang Provincial Medical and Health Technology Project (grant numbers: 2022KY939), and the Medical and Health Technology Projects of Hangzhou (grant numbers: Z20210025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuying Chen and Fan Wu conceptualized and designed the study.\u0026nbsp;Shuoying Qian, Yuan Cai, Xuanwei Huang and Kaiyuan Huang\u0026nbsp;collected and analyzed the data. Yuying Chen\u0026nbsp;wrote the original draft.\u0026nbsp;Mengqian Ge and Tao Hu\u0026nbsp;revised the manuscript. Dingcun Luo and Gang Pan reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eThe Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China, 310053; \u003csup\u003e2\u003c/sup\u003eDepartment of Surgical Oncology, Hangzhou First People\u0026rsquo;s Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China, 310006.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDu L, Wang Y, Sun X, Li H, Geng X, Ge M, et al. Thyroid cancer: trends in incidence, mortality and clinical-pathological patterns in Zhejiang Province, Southeast China. 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Clin Res Hepatol Gastroenterol. 2022;46:102043.\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":"","lastPublishedDoi":"10.21203/rs.3.rs-4147192/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4147192/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eThe aim of this study was to investigate the high-risk clinical factors for large-number lymph node metastases (LNLNM) inthyroid papillary carcinoma (PTC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e The clinicopathological data from the 731 PTC patients who underwent thyroid operation between September 2021to October 2022 in the surgical oncology of Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine were collected. Univariate and multivariate logistic analyses were conducted to identify risk factors for LNLNM in PTC. A predictive model for assessing LNLNM in PTC was established and validated by using receiver operating characteristic curves (ROC), the Hosmer-Lemeshow (HL)test, calibration curves, and decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Age, tumor diameter, platelets and neutrophil-to-lymphocyte ratio (NLR) were identified as independent risk factors for LNLNM in PTC patients. A predictive model was developed to evaluate the risk of LNLNMwith an area under the curve (AUC) of 0.827 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, 95%CI: 0.784-0.870) and the specificity and sensitivity were both 75.8%. The AUC of the validation group was 0.824(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, 95%CI: 0.757-0.890) with a specificity of 79.5% and a sensitivity of 76.0%. Furthermore, themodel demonstrated good calibration through the HL test and favorable diagnostic value by calibration curve and DCA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eAge, tumor diameter, platelets and NLRare high-risk factors for LNLNM in PTC, and the predictive model established in combination with the above factors couldeffectively predict the occurrence of LNLNM in PTC. This study provides support for surgeons to accurately predict the possibility of LNLNM and develop personalized treatment plans before surgery.\u003c/p\u003e","manuscriptTitle":"A Retrospective Analysis: A Predictive Model Using Platelets and Neutrophil-to- Lymphocyte Ratio for the Number of Lymph Node Metastasis in Papillary Thyroid Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 18:21:04","doi":"10.21203/rs.3.rs-4147192/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":"58ca3a5b-a53e-4493-8208-5cf6eb1653be","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-06T07:44:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 18:21:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4147192","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4147192","identity":"rs-4147192","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}