Development and Validation of a FIGO 2018 Staging-Based Risk Prediction Model for Lymph Node Metastasis in Cervical Cancer: A SEER Database Analysis with Internal and External Validation

Development and Validation of a FIGO 2018 Staging-Based Risk Prediction Model for Lymph Node Metastasis in Cervical Cancer: A SEER Database Analysis with Internal and External Validation | 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 Development and Validation of a FIGO 2018 Staging-Based Risk Prediction Model for Lymph Node Metastasis in Cervical Cancer: A SEER Database Analysis with Internal and External Validation Yang Wang, Xinyou Wang, Jing Na, Qiao Lu, Ya Li, Shichao Han, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7288190/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Objective : To explore the risk factors for lymph node metastasis in patients with cervical cancer, construct a risk prediction model for cervical cancer lymph node metastasis based on the FIGO 2018 staging system, and validate the model using both internal and external datasets. This model aims to provide a simple and effective tool for the clinical assessment of lymph node metastasis risk in cervical cancer patients. Methods : A total of 5787 patients with pathologically confirmed cervical cancer from the SEER database, diagnosed between 2000 and 2021, were selected. The patients were randomly divided into a training group and an internal validation group in a 7:3 ratio using R software. Univariate and binary logistic regression analyses were employed to identify independent factors affecting lymph node metastasis in cervical cancer patients. A nomogram prediction model was developed based on the selected factors. The model's performance was evaluated using receiver operating characteristic curves and calculating the area under the curve , along with calibration curves. The Hosmer-Lemeshow goodness-of-fit test and Spiegelhalter Z test were used to assess model performance. Additionally, an external validation cohort consisting of 338 cervical cancer patients treated at our institution between January 2019 and December 2024 was used for receiver operating characteristic curve and calibration curve validation. Results : A total of 5787 cervical cancer patients from the SEER database were included, with 4051 patients in the training group and 1736 patients in the internal validation group. Among these, 729 patients (18.00%) in the training group and 312 patients (17.97%) in the internal validation group had lymph node metastasis. Univariate analysis revealed that histologic type, tumor stage and grade, and tumor size were significantly associated with lymph node metastasis in cervical cancer (P < 0.05). Binary logistic regression analysis identified age, histologic type, tumor stage and grade, and tumor size as factors significantly related to lymph node metastasis in cervical cancer (P < 0.001). The nomogram model based on these variables showed an AUC of 0.758 (95% CI: 0.739–0.776) in the training group, 0.753 (95% CI: 0.726–0.781) in the internal validation group, and 0.742 (95% CI: 0.678–0.805) in the external validation group, indicating good discrimination and stability of the model. The Hosmer-Lemeshow test for the training and internal validation groups both yielded P values > 0.05, and the Spiegelhalter Z test for the external validation group yielded a P value of 0.0989. Calibration curves showed good agreement between predicted probabilities and actual observed outcomes, suggesting excellent model fit. Conclusion : The FIGO 2018-based risk prediction model for lymph node metastasis in cervical cancer, developed using the SEER database, demonstrates high predictive accuracy and strong clinical applicability. This model provides an effective reference for the precise preoperative assessment of lymph node metastasis risk in cervical cancer patients. SEER database cervical cancer lymph nodes nomogram model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cervical cancer (CC) is one of the most common gynecological malignancies among women worldwide. According to the latest statistics, approximately 604,000 new cases of cervical cancer were diagnosed globally in 2020, with an estimated 342,000 deaths from the disease【1】. Currently, the initial treatment strategies for cervical cancer are primarily based on the clinical staging of the patient. According to the most recent staging system published by the International Federation of Gynecology and Obstetrics (FIGO) in 2018【2】, once pelvic or paraaortic lymph node metastasis is confirmed, patients are classified as stage IIIC. This highlights that lymph node metastasis (LNM) is not only a critical prognostic factor for cervical cancer, but also a determining factor in whether adjuvant therapy is necessary. To accurately assess the presence of LNM, patients with cervical cancer who undergo surgical treatment typically require pelvic and paraaortic lymphadenectomy【3】. However, it has been reported that the incidence of lymph node metastasis in early-stage cervical cancer is only 15–25%【4】, meaning that the majority of patients undergo unnecessary lymphadenectomy during surgery, which brings additional surgical complications, including vascular, nerve, and ureteral injuries, as well as postoperative lymphocele and lymphedema. These complications not only affect short-term recovery but can also lead to a long-term decline in the quality of life【5】. To reduce unnecessary lymphadenectomy and its associated complications, sentinel lymph node biopsy (SLNB) has been widely adopted as an alternative approach in clinical practice. Although SLNB has reduced the need for comprehensive lymphadenectomy to some extent, its sensitivity remains around 80–90%, meaning that 5–10% of patients may still have occult lymph node metastasis despite negative SLNB results【6】. Additionally, the false-negative rate of SLNB is about 5–10%, indicating that there is still a risk of missed diagnoses【7】. Therefore, preoperative accurate assessment of lymph node status is crucial for the development of individualized treatment plans, avoiding overtreatment, reducing surgical risks, and improving patient prognosis. Currently, the main methods for predicting lymph node metastasis in cervical cancer rely on imaging techniques, including color Doppler ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET-CT). However, these imaging modalities have a sensitivity for detecting lymph node metastasis ranging from only 30–70%【8】, particularly for small metastatic lymph nodes that have not shown an increase in size, making them difficult to detect effectively by imaging and limiting the accuracy of these methods. Thus, there is an urgent need to develop a risk assessment tool for lymph node metastasis based on preoperative, easily accessible clinical indicators that offers high efficiency and predictive accuracy. In light of this, the present study aims to construct and validate a risk prediction model for lymph node metastasis in cervical cancer patients based on large-scale data from the SEER database, in conjunction with the FIGO 2018 staging criteria. The goal is to provide scientific evidence for precise preoperative assessment and individualized treatment planning. 1. Materials and Methods 1.1 Study Subjects This study is based on clinical data of cervical cancer patients registered in the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute of the United States, with data extracted from 2000 to 2021. The collected data includes information on diagnosis date, race, tumor site, tumor grade and stage, tumor size, lymph node metastasis, and other relevant clinical factors. Inclusion criteria were as follows: ( 1 ) Patients with cervical cancer diagnosis according to the morphology code of cervical cancer in the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3); ( 2 ) The primary site of cancer confirmed as the cervix; ( 3 ) Complete clinical data available. Exclusion criteria were as follows: ( 1 ) Excluding cases with dual primary tumors; ( 2 ) Incomplete or missing clinical data; ( 3 ) Presence of distant metastasis (M1). A total of 5,787 cervical cancer patients who met the inclusion criteria were included in the study, and these patients were randomly divided into a training group (4,051 cases) and an internal validation group (1,736 cases) at a 7:3 ratio, as shown in Fig. 1 . Furthermore, to further validate the applicability and stability of the model, this study retrospectively collected clinical data of cervical cancer patients diagnosed and surgically treated at the Second Affiliated Hospital of Dalian Medical University between January 2019 and December 2024. Tumor size can be assessed preoperatively through clinical pelvic examination and imaging techniques such as CT or MRI, and is defined as the longest diameter of the tumor.The diagnosis of LNM was confirmed based on histopathological examination of surgical specimens.The inclusion criteria were: ( 1 ) Pathologically confirmed diagnosis of cervical cancer; ( 2 ) Primary cancer site confirmed as the cervix; ( 3 ) Complete clinical data available. Exclusion criteria included incomplete clinical information.A total of 338 patients were included as the external validation group. The use of the SEER database does not require additional informed consent as patient privacy data has been protected. Additionally, informed consent was obtained from the patients involved in this study, and there are no potential conflicts of interest. The study protocol has been approved by the Ethics Committee of the Second Affiliated Hospital of Dalian Medical University(Approval No. KY2025-314-01). 1.2 Statistical Methods Data were analyzed using SPSS 27.0 software. Categorical data were expressed as frequency (n) and percentage (%). The baseline characteristics of patients in the model group and internal validation group were compared using the Chi-square test (χ² test). Univariate analysis was conducted using the Chi-square test or Fisher's exact test. Variables with statistical significance (P < 0.05) and those confirmed to be associated in previous studies were included in multivariate analysis. Multivariate analysis was performed using binary logistic regression to identify independent factors influencing lymph node metastasis in cervical cancer patients. R 3.7 software and relevant R packages were used to generate a nomogram, receiver operating characteristic (ROC) curve, and calibration curve. The area under the curve (AUC) and its 95% confidence interval (95% CI) were calculated to assess the model's discriminative ability. Statistical significance was set at P 0.05 indicates no significant difference between the predicted and observed values, suggesting good model fit. 2. Results 2.1 Patient Population A total of 5787 patients were included, with 4051 patients in the training group and 1736 patients in the internal validation group. There were no statistically significant differences between the two groups in terms of age, pathological type, tumor stage, grade, and tumor diameter (P > 0.05). See Table 1 . Table 1 Basic clinical data of two groups of patients Characteristics Training set (n=4051 ) Validation set (n=1736 ) X 2 P Age, n(%) < 45 1984(48.98) 830(47.81) 0.985 0.061 45–65 1605(39.62) 712(41.01) ≥ 65 462 (11.40) 194(11.18) Histologictype, n(%) Squamous cell carcinoma 2406(59.39) 1015(58.47) 4.028 0.133 Adenocarcinoma 963 (23.77) 392 (22.58) Others 682 (16.84) 329 (18.95) Stage IA 1068(26.36) 472(27.19) 0.963 0.810 IB 1808(44.63) 755(43.49) IIA 391 (9.65) 163(9.39) IIB 784 (19.35) 346(19.93) Grade G1 380 (9.38) 186(10.71) 3.178 0.365 G2 1015(25.06) 411(23.68) G3 716 (17.67) 310(17.86) Unkown 1940(47.89) 829(47.75) Size < 2cm 1644(40.58) 676(38.94) 2.953 0.399 2−4cm 999 (24.66) 420(24.19) 4−8cm 1264(31.21) 568(32.72) ≥ 8cm 144 (3.55) 72 (4.15) LNM Yes 729(18.00) 312 (17.97) < 0.01 1.000 No 3322(82.00) 1424(82.03) LNM, lymph node metastasis 2.2 General Information of the External Validation Group To assess the robustness and applicability of the model, this study included 338 patients in the external validation group, all of whom were pathologically confirmed cervical cancer cases. After comparing the baseline clinical characteristics between the external validation group and the SEER database cohort, we found significant differences between the two groups in several variables, including age, tumor grade and stage, histologic type, and tumor size (P < 0.05), indicating that the clinical feature distributions were not completely consistent between the cohorts (see Table 2 ). Table 2 Comparison of patient characteristics between the External Validation Group and the SEER Database Cohort Characteristics External Validation Group (n = 338) SEER Database Cohort(n = 5787) X 2 P Age, n(%) < 45 58(17.16) 2814(48.63) 45–65 231(68.34) 2317(40.04) 131.710 < 0.05 ≥ 65 49(14.50) 656 (11.33) Histologictype, n(%) Squamous cell carcinoma 301(89.05) 3421(59.13) Adenocarcinoma 32(9.47) 1355(23.41) 123.760 < 0.05 Others 5 (1.48) 1011(17.46) Stage, n(%) IA 4 (1.18) 1540(26.61) IB 231(68.35) 2563(44.29) 189.920 <0.05 IIA 74(21.89) 554 (9.57) IIB 29(8.58) 1130 (19.53) Grade, n(%) G1 11(3.25) 566(9.78) G2 201 (59.47) 1426(24.64) 386.860 <0.05 G3 126 (37.28) 1026(17.73) unknown 0 (0.00) 2769(47.85) Size, n(%) < 2cm 58(17.16) 2320(40.09) 2−4cm 162(47.93) 1419(24.52) 115.850 <0.05 4−8cm 113(33.43) 1832(31.66) ≥ 8cm 5 (1.48) 216(3.73) LNM Yes 64 (18.93) 1041(17.99) 0.135 0.714 No 274(81.07) 4746(82.01) LNM, lymph node metastasis 2.3 Factors Influencing Lymph Node Metastasis in the Training Group Univariate analysis in the training group showed that pathological type, tumor stage and grade, and tumor diameter were significantly associated with lymph node metastasis in cervical cancer patients (P 0.05). Binary logistic regression analysis indicated that age, pathological type, tumor stage and grade, and tumor diameter were significantly associated with lymph node metastasis in cervical cancer patients (P < 0.001). See Table 3 . Table 3 Univariate and Multivariate Analysis of Lymph Node Metastasis Risk in Cervical Cancer Patients Characteristics Univariate analysis Multivariate analysis OR 95%CI P OR 95%CI P Age, n(%) < 45 Reference Reference 45–65 1.021 0.860–1.211 0.816 0.859 0.713–1.033 0.107 ≥ 65 0.990 0.756–1.285 0.942 0.598 0.448–0.791 < 0.001 Histologic type, n(%) Squamous cell carcinoma Reference Reference Adenocarcinoma 0.382 0.301–0.480 < 0.001 0.572 0.443–0.733 < 0.001 Others 0.585 0.461–0.737 < 0.001 0.659 0.510–0.845 0.001 Stage IA Reference Reference IB 3.242 2.426–4.407 < 0.001 1.695 1.215–2.391 0.002 IIA 7.074 5.018–10.074 < 0.001 2.316 1.547–3.490 < 0.001 IIB 10.493 7.789–14.370 < 0.001 3.243 2.244–4.737 < 0.001 Grade G1 Reference Reference G2 4.081 2.682–6.486 < 0.001 1.935 1.236–3.145 0.005 G3 5.910 3.866–9.431 < 0.001 2.367 1.504–3.868 < 0.001 Unkown 2.523 1.673–3.984 < 0.001 1.712 1.112–2.748 0.019 Size < 2cm Reference Reference 2−4cm 3.558 2.733–4.659 < 0.001 2.532 1.881–3.428 < 0.001 4−8cm 7.782 6.139–9.956 < 0.001 3.997 2.987–5.395 < 0.001 ≥ 8cm 13.496 9.135–19.958 < 0.001 6.398 4.152–9.869 < 0.001 OR, Odds Ratio; CI, Confidence Interval 2.4 Establishment of the Nomogram Prediction Model for Lymph Node Metastasis in Cervical Cancer Patients and External Dataset Validation Based on the results of multivariate regression analysis, a nomogram was developed to predict lymph node metastasis in cervical cancer patients by incorporating the identified risk factors. The nomogram for predicting lymph node metastasis in cervical cancer patients is shown in Fig. 2. For each influencing factor, a corresponding score is assigned, and the total score is calculated by summing the individual scores, which then corresponds to the risk of lymph node metastasis in cervical cancer as shown in the nomogram. The AUC in the training group was 0.758 (95% CI: 0.739–0.776, Fig. 4A), and the AUC in the internal validation group was 0.753 (95% CI: 0.726–0.781, Fig. 4B), indicating good diagnostic performance. The calibration plots demonstrated good consistency between the predicted and actual nomogram values in both the training and validation groups (Fig. 4C-D). Additionally, the Hosmer-Lemeshow goodness-of-fit test yielded P = 0.874 in the training group and P = 0.472 in the internal validation group, with both P values > 0.05, suggesting that there was no significant difference between the predicted and observed values in the nomogram. The final multivariable logistic regression model for predicting lymph node metastasis (N) is presented in Figure 3.The formula details are provided in the design of the logistic regression model Excel template, included as Appendix 1. 2.5 Risk Validation of Lymph Node Metastasis in Cervical Cancer Using the External Validation Set The AUC in the external validation group was 0.742 (95% CI: 0.678-0.805), as shown in Figure 5A. The calibration curve indicated that the trends of the three curves in the modeling group and the external validation group were nearly identical (P > 0.05), suggesting that the model has good predictive value, as shown in Figure 5B. The Hosmer-Lemeshow goodness-of-fit test yielded P < 0.05. Therefore, a Spiegelhalter Z test was conducted to further assess the presence of systematic bias in the model. The result showed Z = -1.65, P = 0.099. Given the consistency with the calibration curve trends, this suggests that the model has good predictive and calibration capabilities. 3. Discussion This study successfully developed a risk prediction model for lymph node metastasis in cervical cancer based on five easily accessible preoperative clinical indicators: age, clinical stage, histologic type, maximum tumor diameter, and pathological type, under the 2018 FIGO staging system. The model demonstrated good discrimination (AUC = 0.758) and calibration (Hosmer-Lemeshow test, P = 0.874), providing a strong quantitative basis for individualized preoperative treatment decisions. In the external validation cohort, we assessed the model's calibration using both the Hosmer-Lemeshow test and Spiegelhalter’s Z test. Although the Hosmer-Lemeshow test indicated a statistical difference between the predicted and observed values (P < 0.05), suggesting some calibration bias in certain risk strata, the Spiegelhalter’s Z test (Z = -1.65, P = 0.099) showed no significant difference between the overall predicted and actual outcomes. Therefore, despite some potential bias in certain subgroups, the model demonstrates good overall calibration and clinical applicability. In this study, although the model demonstrated good predictive performance in the external validation dataset, we observed notable differences in clinical characteristics between the external modeling cohort and the SEER database cohort. These discrepancies may be attributed to differences in data sources, geographic regions of patient recruitment, clinical management strategies, or inclusion criteria. The external validation cohort was derived from a single-center hospital in China, whereas the SEER database includes data from multiple regions across the United States, potentially reflecting variations in ethnicity, healthcare systems, and screening practices. Based on the relationship between the total score of the nomogram and the probability of event occurrence, this study further stratified patients into low, medium, and high-risk groups. The low-risk group (total score 0–97) had a lymph node metastasis probability of 0.25–10%, the medium-risk group (total score 97.1–130.3) had a probability of 10.7–31.3%, and the high-risk group (total score > 130.3) had a probability of 31.3–99.75%. This stratification system helps clinicians accurately identify patients at different risks for lymph node metastasis, enabling the development of differentiated treatment strategies. For example, low-risk patients can avoid unnecessary extensive lymphadenectomy, while high-risk patients can be considered for adjuvant radiotherapy or chemotherapy in advance. This approach maximizes the effectiveness of individualized treatment and reduces postoperative complication risks. It is important to emphasize that lymph node metastasis is a key factor influencing the prognosis of cervical cancer 【9, 10, 11】 A cohort study by Deng et al. 【12】 demonstrated that the 5-year survival rate of cervical cancer patients without lymph node metastasis was as high as 93%, whereas the 5-year survival rate dropped to 64% and 42% in patients with pelvic and paraaortic lymph node metastasis, respectively (P < 0.001)【13】. Furthermore, a study by Cibula et al. 【3】indicated that the risk of lymph node metastasis significantly increases with the progression of staging (with a metastasis rate of 2.1% in stage IA and 34.7% in stage IIB, P < 0.001). However, at present, clinical assessment of lymph node status mainly relies on imaging techniques, which have significant limitations. As shown in a study by Choi et al.【14】, while Positron Emission Tomography–Magnetic Resonance Imaging(PET-MRI)demonstrates a sensitivity of 78% and specificity of 92% for detecting metastatic lymph nodes greater than 1 cm in diameter, its sensitivity drops drastically to 21% for detecting small metastatic lesions (< 5 mm ), making it challenging to meet the needs of early and precise evaluation. In clinical practice, while lymph node dissection remains the gold standard for diagnosing lymph node metastasis, the lack of clear indications for its use has led to many low-risk patients undergoing unnecessary lymphadenectomy, resulting in severe complications such as lymphocyst, lymphedema, and lower limb dysfunction 【15, 16】. In fact, the overall lymph node metastasis rate in cervical cancer is approximately 15–20% 【17】, meaning that over 70% of patients do not have lymph node metastasis post-surgery, highlighting the urgent need for accurate preoperative risk assessment tools to guide treatment strategies. To address this issue, SLNB has been proposed as an effective method to reduce unnecessary dissection rates 【18–19】. A study by Lécuru et al.【6】demonstrated that for early cervical cancer patients with tumors smaller than 2 cm, the detection rate of SLNB was 94.7%, with a false-negative rate of only 2.1%. However, for tumors larger than 4 cm, the SLNB detection rate dropped to 66.7%, with a false-negative rate increasing to 15.8%, suggesting its limited applicability in advanced or large tumors. Furthermore, Only a few medical institutions worldwide have the advanced technology required to perform SLNB, and the lack of technology and experience, especially in developing countries, severely limits the widespread adoption of SLNB. Faced with the current dilemma between “over-treatment” of cervical cancer lymphadenectomy and the need for “accurate assessment,” it is crucial to develop lymph node metastasis risk prediction tools based on preoperatively available indicators to assist clinicians in making more rational treatment decisions, ultimately achieving the dual goal of reducing complications and improving survival rates. Therefore, establishing a stratified risk prediction model for cervical cancer lymph node metastasis based on clinical data is of significant clinical importance【20】. The nomogram model developed in this study, based on easily accessible clinical variables, demonstrated favorable diagnostic performance in predicting LNM in cervical cancer, with an AUC of 0.75. Although its predictive accuracy is slightly lower than that of some advanced models utilizing radiomics or molecular markers, it offers substantial advantages in terms of clinical feasibility, cost-effectiveness, and operational simplicity, thereby serving as a valuable complementary tool in clinical practice. Numerous studies have reported predictive models for LNM in cervical cancer. Radiomics-based models extract high-dimensional features from MRI or CT images to construct predictive algorithms. For instance, Dong et al. developed a radiomics model based on multiparametric MRI features, achieving an AUC of 0.803 in the test set【21】. Another study demonstrated that combining radiomics with clinical features improved model performance, reaching an AUC of 0.848【22】. However, radiomics approaches often require dedicated software and advanced algorithms, involve complex image processing workflows, and depend heavily on imaging equipment, which limits their applicability in resource-constrained healthcare settings. Models based on molecular biomarkers utilize gene expression profiles to predict LNM, with some studies reporting AUCs exceeding 0.855【23】. Despite their high predictive accuracy, such models demand high-quality biospecimens, sophisticated testing platforms, and robust bioinformatics analysis pipelines, thereby posing significant barriers to clinical translation. By contrast, the model proposed in this study relies solely on routinely collected clinical parameters, such as FIGO stage, tumor size, and histological type. It does not require additional imaging or molecular testing, making it highly accessible and easily generalizable. Although the AUC is modestly lower than that of more complex models, its practical predictive ability remains robust. Notably, it may serve as a rapid and effective preoperative risk stratification tool, particularly in settings where advanced diagnostic modalities such as SLNB or PET-CT are unavailable. This model can assist clinicians in preliminarily identifying patients at elevated risk of LNM and guide decisions regarding the need for sentinel lymph node biopsy or systematic lymphadenectomy. It is important to underscore that this model is not intended to replace SLNB or advanced imaging, especially in the detection of micrometastases, where its sensitivity may be limited. Nevertheless, it has considerable clinical value during the early stages of patient assessment and treatment planning, particularly in screening high-risk individuals and streamlining therapeutic pathways. Future research should focus on integrating this nomogram with radiomic and molecular predictors to construct multidimensional, hybrid models that may enhance overall predictive performance. Moreover, this model offers a user-friendly and intuitive risk assessment tool for both clinicians and patients, thereby facilitating precision medicine and personalized care. Future studies should emphasize multicenter external validation, cross-population applicability, and dynamic model updating to ensure its robustness and clinical utility across diverse healthcare environments. Preoperative or pretreatment assessment of lymph node status in cervical cancer involves a variety of methods, including MRI, PET-CT, CT, SLNB, frozen section examination (FSE), fine needle aspiration (FNA), radionuclide imaging, and spectral CT. Each method differs in terms of diagnostic accuracy, clinical applicability, and operational complexity. While MRI and PET-CT offer relatively high accuracy, they cannot fully replace pathological confirmation. SLN biopsy and FSE provide real-time histological information to support intraoperative decision-making. Emerging techniques such as FNA and spectral CT show promise but require further validation. Clinicians should tailor diagnostic strategies based on individual patient characteristics and integrate multiple approaches to optimize treatment planning, improve therapeutic outcomes, and enhance overall survival and quality of life【24–25】. This study integrated data from the SEER database (n = 5787) and real-world data (n = 388) to construct a preoperative risk prediction tool applicable to various healthcare resource settings, providing strong support for individualized clinical treatment. However, it is important to note several limitations in this study: ( 1 ) Risk of data bias: Due to its retrospective design, some key variables were missing, particularly since the SEER database does not include important pathological parameters such as vascular invasion, depth of stromal invasion, or extent of lymphadenectomy, which may affect the comprehensiveness and accuracy of the model; ( 2 ) Insufficient integration of imaging data: This study did not incorporate imaging features from modalities like CT/MRI, limiting the model's ability to identify small lymph node metastases (e.g., micrometastases or isolated tumor cell clusters) ; ( 3 ) Methodological limitations: The statistical modeling methods used are based on assumptions of linear relationships between data, which may fail to capture the complex non-linear associations and interaction effects among variables, making the model sensitive to outliers and multicollinearity, and potentially performing poorly in situations of sample imbalance (e.g., low incidence of lymph node metastasis).( 4 ) External validation was limited to a single center, and significant differences in case composition may limit the generalizability of the model.( 5 )Although the nomogram model developed in this study demonstrated good discrimination and calibration in the validation cohort, its overall predictive performance still has room for improvement. Future studies may consider incorporating more advanced statistical approaches or machine learning algorithms (such as random forest, gradient boosting, or neural networks) to capture complex nonlinear relationships and enhance model accuracy. However, it is important to balance model complexity with clinical interpretability to ensure practical applicability in real-world clinical settings.༈6༉In this study, a substantial proportion of tumor grade information was missing (approximately 48%), which may affect the stability and generalizability of the model. Future studies should validate the applicability and reliability of the model using broader datasets and populations to enhance its practical value and potential for clinical implementation. In conclusion, although this study provides a new tool for preoperative lymph node metastasis risk stratification in cervical cancer, future research should involve prospective, multicenter large-scale cohort data validation, integrating imaging genomics, molecular markers, and multimodal data to further optimize and enhance the model's accuracy and applicability. 4. Conclusion This study successfully developed a cervical cancer lymph node metastasis risk prediction model based on five easily accessible preoperative clinical indicators, demonstrating good discrimination and clinical utility. The model can effectively identify patients with varying risks of lymph node metastasis, providing scientific evidence for clinicians to develop individualized treatment plans. It has the potential to significantly reduce 15–20% of unnecessary lymphadenectomies, lowering related complications and improving patient prognosis. In the future, further validation through multicenter, prospective cohort studies is needed, alongside integration of imaging genomics, genomics, pathology, and clinical data to develop predictive tools with high interpretability and generalizability. This would support the development of more precise and personalized cervical cancer treatment decisions, ultimately achieving the dual goals of improving patient survival and optimizing quality of life. Declarations The study had adhered to the principles sated in the “Declaration of Helsinki” Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to protecting personal privacy, but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Disclosure statement No potential conflict of interest was reported by the author(s). Funding The author(s) reported there is no funding associated with the work featured in this article. Authors' contributions All authors contributed substantially to this work. JW and SH contributed to the conception and design of the study, data analysis and interpretation, drafting and finalization of the manuscript, and conceiving the analysis. YW and XW contributed to the conception and acquisition of data, and critical revision of the manuscript. JN、QL and YL contributed to data acquisition and manuscript drafting. YL contributed to the acquisition and analysis of the data and critical revision of the manuscript. All authors have read and approved the final manuscript. Acknowledgements Not applicable References Arbyn, M., Weiderpass, E., Bruni, L. et al. Estimates of incidence and mortality of cervical cancer in 2020: a worldwide analysis. The Lancet Global Health, 2020. DOI: 10.1016/S2214-109X(19)30482-6 Bhatla N, Berek JS, Cuello Fredes M, et al. Revised FIGO staging for carcinoma of the cervix uteri [published correction appears in Int J Gynaecol Obstet. 2019 Nov;147(2):279-280. doi: 10.1002/ijgo.12969.]. Int J Gynaecol Obstet. 2019;145(1):129-135. doi:10.1002/ijgo.12749 Cibula D, Pötter R, Planchamp F, et al. The European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology Guidelines for the Management of Patients With Cervical Cancer. Int J Gynecol Cancer. 2018;28(4):641-655. doi:10.1097/IGC.0000000000001216 Wright JD, Matsuo K, Huang Y, et al. Prognostic Performance of the 2018 International Federation of Gynecology and Obstetrics Cervical Cancer Staging Guidelines. Obstet Gynecol. 2019;134(1):49-57. doi:10.1097/AOG.0000000000003311 Ashley CW, Da Cruz Paula A, Kumar R, et al. Analysis of mutational signatures in primary and metastatic endometrial cancer reveals distinct patterns of DNA repair defects and shifts during tumor progression. Gynecol Oncol. 2019;152(1):11-19. doi:10.1016/j.ygyno.2018.10.032 Lecuru FR, McCormack M, Hillemanns P, et al. SENTICOL III: an international validation study of sentinel node biopsy in early cervical cancer. A GINECO, ENGOT, GCIG and multicenter study. Int J Gynecol Cancer. 2019;29(4):829-834. doi:10.1136/ijgc-2019-000332 Zhang X, Bao B, Wang S, Yi M, Jiang L, Fang X. Sentinel lymph node biopsy in early stage cervical cancer: A meta-analysis. Cancer Med. 2021;10(8):2590-2600. doi:10.1002/cam4.3645 Woo S, Atun R, Ward ZJ, Scott AM, Hricak H, Vargas HA. Diagnostic performance of conventional and advanced imaging modalities for assessing newly diagnosed cervical cancer: systematic review and meta-analysis. Eur Radiol. 2020;30(10):5560-5577. doi:10.1007/s00330-020-06909-3 Cibula D, Kocian R, Plaikner A, et al. Sentinel lymph node mapping and intraoperative assessment in a prospective, international, multicentre, observational trial of patients with cervical cancer: The SENTIX trial. Eur J Cancer. 2020;137:69-80. doi:10.1016/j.ejca.2020.06.034 Cibula D, Raspollini MR, Planchamp F, et al. ESGO/ESTRO/ESP Guidelines for the management of patients with cervical cancer - Update 2023. Int J Gynecol Cancer. 2023;33(5):649-666. Published 2023 May 1. doi:10.1136/ijgc-2023-004429 Ronsini C, Anchora LP, Restaino S, et al. The role of semiquantitative evaluation of lympho-vascular space invasion in early stage cervical cancer patients. Gynecol Oncol. 2021;162(2):299-307. doi:10.1016/j.ygyno.2021.06.002 Sakuragi N, Satoh C, Takeda N, et al. Incidence and distribution pattern of pelvic and paraaortic lymph node metastasis in patients with Stages IB, IIA, and IIB cervical carcinoma treated with radical hysterectomy. Cancer. 1999;85(7):1547-1554. doi:10.1002/(sici)1097-0142(19990401)85:73.0.co;2-2 Saleh M, Virarkar M, Javadi S, Elsherif SB, de Castro Faria S, Bhosale P. Cervical Cancer: 2018 Revised International Federation of Gynecology and Obstetrics Staging System and the Role of Imaging. AJR Am J Roentgenol. 2020;214(5):1182-1195. doi:10.2214/AJR.19.21819 Choi HJ, Roh JW, Seo SS, et al. Comparison of the accuracy of magnetic resonance imaging and positron emission tomography/computed tomography in the presurgical detection of lymph node metastases in patients with uterine cervical carcinoma: a prospective study. Cancer. 2006;106(4):914-922. doi:10.1002/cncr.21641 Jing H, Xiuhong W, Ying Y, et al. Complications of radical hysterectomy with pelvic lymph node dissection for cervical cancer: a 10-year single-centre clinical observational study. BMC Cancer. 2022;22(1):1286. Published 2022 Dec 8. doi:10.1186/s12885-022-10395-9 Hwang JH, Kim BW. The incidence of perioperative lymphatic complications after radical hysterectomy and pelvic lymphadenectomy between robotic and laparoscopic approach : a systemic review and meta-analysis. Int J Surg. 2023;109(8):2478-2485. Published 2023 Aug 1. doi:10.1097/JS9.0000000000000472 Bhatla N, Aoki D, Sharma DN, Sankaranarayanan R. Cancer of the cervix uteri: 2021 update. Int J Gynaecol Obstet. 2021;155 Suppl 1(Suppl 1):28-44. doi:10.1002/ijgo.13865 Zhang X, Bao B, Wang S, Yi M, Jiang L, Fang X. Sentinel lymph node biopsy in early stage cervical cancer: A meta-analysis. Cancer MFrumovitz M, Buda A. Encouraging worldwide adoption of sentinel lymph node biopsies for gynecologic malignancies. Int J Gynecol Cancer. 2020;30(3):281-282. doi:10.1136/ijgc-2020-001240ed. 2021;10(8):2590-2600. doi:10.1002/cam4.3645 Frumovitz M, Buda A. Encouraging worldwide adoption of sentinel lymph node biopsies for gynecologic malignancies. Int J Gynecol Cancer. 2020;30(3):281-282. doi:10.1136/ijgc-2020-001240 Zhang Z, Wan X, Lei X, et al. Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer. Insights Imaging. 2023;14(1):65. Published 2023 Apr 15. doi:10.1186/s13244-023-01405-w Hou L, Zhou W, Ren J, et al. Radiomics analysis of multiparametric MRI for the preoperative prediction of lymph node metastasis in cervical cancer. Front Oncol. 2020;10:1393. doi:10.3389/fonc.2020.01393 Hou L, Zhou W, Ren J, et al. Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer. Front Oncol. 2020;10:1393. Published 2020 Aug 20. doi:10.3389/fonc.2020.01393 Wei J, Wang M, Wu Y. Comprehensive analysis of mRNA and lncRNA expression for predicting lymph node metastasis in cervical cancer: a novel seven-gene signature approach. Front Genet. 2025;16:1524821. Published 2025 May 15. doi:10.3389/fgene.2025.1524821 Ren J, Li Y, Liu XY, et al. Diagnostic performance of ADC values and MRI-based radiomics analysis for detecting lymph node metastasis in patients with cervical cancer: A systematic review and meta-analysis. Eur J Radiol. 2022;156:110504. doi:10.1016/j.ejrad.2022.110504 Ditto A, Leone Roberti Maggiore U, Evangelisti G, et al. Diagnostic Accuracy of Magnetic Resonance Imaging in the Pre-Operative Staging of Cervical Cancer Patients Who Underwent Neoadjuvant Treatment: A Clinical-Surgical-Pathologic Comparison. Cancers (Basel). 2023;15(7):2061. Published 2023 Mar 30. doi:10.3390/cancers15072061 Appendix Appendix is not available with this version. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 23 Nov, 2025 Reviewers agreed at journal 23 Nov, 2025 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviewers invited by journal 19 Aug, 2025 Editor invited by journal 11 Aug, 2025 Editor assigned by journal 10 Aug, 2025 Submission checks completed at journal 10 Aug, 2025 First submitted to journal 04 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7288190","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503980938,"identity":"440472de-6f4c-4287-be69-fdd6ade3926c","order_by":0,"name":"Yang Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Wang","suffix":""},{"id":503980940,"identity":"e06f7e73-0d2c-4baf-9c43-63f7a46bddcb","order_by":1,"name":"Xinyou Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinyou","middleName":"","lastName":"Wang","suffix":""},{"id":503980941,"identity":"c4f082f3-d5b5-4903-a71c-34b392d65e89","order_by":2,"name":"Jing Na","email":"","orcid":"","institution":"The Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Na","suffix":""},{"id":503980942,"identity":"e0fb5dc1-998c-44a8-9093-0d81680645b2","order_by":3,"name":"Qiao Lu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiao","middleName":"","lastName":"Lu","suffix":""},{"id":503980944,"identity":"30bc6155-5d74-4eaf-ac0b-12a7f4c39fea","order_by":4,"name":"Ya Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Li","suffix":""},{"id":503980946,"identity":"97da138a-3c49-4abd-b61d-beb8943780ae","order_by":5,"name":"Shichao Han","email":"","orcid":"","institution":"The Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shichao","middleName":"","lastName":"Han","suffix":""},{"id":503980949,"identity":"0463faab-5fc6-4659-9745-13e7849542f0","order_by":6,"name":"Jun Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIie3QsQqDMBCA4YjQKa3rufURTgKZgn2QLlcEt76Bg1BoR1cf5+TA1VfQN3Ds0KF265aMhebfDvIdXJSKxX4yYV6dwixNZQ4k42Xpa4X5Y1djINHG6I3gpI8QBHBgC5pKY0QrVI07+wlzDUCVtbLnWY31tfURyzxC8WRn5UCYtBJChjsQsTM3jRBIJEUmtpiGkhOPydJSZUC2T6aQW/J+WuVFZdF1IvPaOD9RwF8DeZ9/yvxbY7FY7N97A4nCRLL+FTn8AAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-04 07:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7288190/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7288190/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90301153,"identity":"4b4359c6-fed4-4075-a7ff-83e8c77c2591","added_by":"auto","created_at":"2025-09-01 08:57:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94092,"visible":true,"origin":"","legend":"\u003cp\u003eProcess diagram for screening patients\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7288190/v1/b7e56ceb6c66ec49265d4f1f.png"},{"id":90301155,"identity":"6dfba480-fa9c-4d94-8344-afcfd8147ef2","added_by":"auto","created_at":"2025-09-01 08:57:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107411,"visible":true,"origin":"","legend":"\u003cp\u003eRisk \u0026nbsp;\u0026nbsp;prediction model and Decision Curve Analysis (DCA) curve\u003c/p\u003e\n\u003cp\u003eThe probability of metastatic lymph node involvement is determined by plotting a straight line based on the corresponding points for each of the following variables on the coordinate axes: age at diagnosis, histologictype, tumor grade, tumor size, and stage.Sum the points obtained for each variable and locate the corresponding position on the \"Total Score\" line. Then, extend a vertical line downward from the total score point to the predicted probability scale at the bottom to obtain the individualized probability of lymph node metastasis for the patient.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7288190/v1/b8fdf7680e970c374c1ff197.png"},{"id":90301154,"identity":"55ce0134-81cf-4dda-8bf5-5b4111e0262a","added_by":"auto","created_at":"2025-09-01 08:57:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17864,"visible":true,"origin":"","legend":"\u003cp\u003ethe final multivariable logistic regression model\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7288190/v1/8e9d2c82a74cbc9a7e9fd281.png"},{"id":90301168,"identity":"267790a5-6f3f-4803-8203-2d907fd4ec8b","added_by":"auto","created_at":"2025-09-01 08:57:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":117500,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic \u0026nbsp;\u0026nbsp;curves and calibration curves of the training group and internal validation \u0026nbsp;\u0026nbsp;group. AUC, area under the curve; CI, confdence interval\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7288190/v1/0e0abbdc3738d7a2788db56a.png"},{"id":90301166,"identity":"9c7cbb2e-1cf5-4ce4-8d0e-71680acb6351","added_by":"auto","created_at":"2025-09-01 08:57:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60262,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve and calibration curve of external validation set. AUC, area under the curve; CI, confdence interval\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7288190/v1/ff85822b02ac6ebccd94bb04.png"},{"id":90304311,"identity":"de99ea4c-4cc5-4ede-a8be-ac0e03dee4b0","added_by":"auto","created_at":"2025-09-01 09:13:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1233172,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7288190/v1/d5aaca73-b4b3-48f4-81f4-8668c0450f10.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a FIGO 2018 Staging-Based Risk Prediction Model for Lymph Node Metastasis in Cervical Cancer: A SEER Database Analysis with Internal and External Validation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer (CC) is one of the most common gynecological malignancies among women worldwide. According to the latest statistics, approximately 604,000 new cases of cervical cancer were diagnosed globally in 2020, with an estimated 342,000 deaths from the disease【1】. Currently, the initial treatment strategies for cervical cancer are primarily based on the clinical staging of the patient. According to the most recent staging system published by the International Federation of Gynecology and Obstetrics (FIGO) in 2018【2】, once pelvic or paraaortic lymph node metastasis is confirmed, patients are classified as stage IIIC. This highlights that lymph node metastasis (LNM) is not only a critical prognostic factor for cervical cancer, but also a determining factor in whether adjuvant therapy is necessary.\u003c/p\u003e\u003cp\u003eTo accurately assess the presence of LNM, patients with cervical cancer who undergo surgical treatment typically require pelvic and paraaortic lymphadenectomy【3】. However, it has been reported that the incidence of lymph node metastasis in early-stage cervical cancer is only 15\u0026ndash;25%【4】, meaning that the majority of patients undergo unnecessary lymphadenectomy during surgery, which brings additional surgical complications, including vascular, nerve, and ureteral injuries, as well as postoperative lymphocele and lymphedema. These complications not only affect short-term recovery but can also lead to a long-term decline in the quality of life【5】. To reduce unnecessary lymphadenectomy and its associated complications, sentinel lymph node biopsy (SLNB) has been widely adopted as an alternative approach in clinical practice. Although SLNB has reduced the need for comprehensive lymphadenectomy to some extent, its sensitivity remains around 80\u0026ndash;90%, meaning that 5\u0026ndash;10% of patients may still have occult lymph node metastasis despite negative SLNB results【6】. Additionally, the false-negative rate of SLNB is about 5\u0026ndash;10%, indicating that there is still a risk of missed diagnoses【7】.\u003c/p\u003e\u003cp\u003eTherefore, preoperative accurate assessment of lymph node status is crucial for the development of individualized treatment plans, avoiding overtreatment, reducing surgical risks, and improving patient prognosis. Currently, the main methods for predicting lymph node metastasis in cervical cancer rely on imaging techniques, including color Doppler ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET-CT). However, these imaging modalities have a sensitivity for detecting lymph node metastasis ranging from only 30\u0026ndash;70%【8】, particularly for small metastatic lymph nodes that have not shown an increase in size, making them difficult to detect effectively by imaging and limiting the accuracy of these methods.\u003c/p\u003e\u003cp\u003eThus, there is an urgent need to develop a risk assessment tool for lymph node metastasis based on preoperative, easily accessible clinical indicators that offers high efficiency and predictive accuracy. In light of this, the present study aims to construct and validate a risk prediction model for lymph node metastasis in cervical cancer patients based on large-scale data from the SEER database, in conjunction with the FIGO 2018 staging criteria. The goal is to provide scientific evidence for precise preoperative assessment and individualized treatment planning.\u003c/p\u003e"},{"header":"1. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Study Subjects\u003c/h2\u003e\u003cp\u003eThis study is based on clinical data of cervical cancer patients registered in the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute of the United States, with data extracted from 2000 to 2021. The collected data includes information on diagnosis date, race, tumor site, tumor grade and stage, tumor size, lymph node metastasis, and other relevant clinical factors.\u003c/p\u003e\u003cp\u003eInclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Patients with cervical cancer diagnosis according to the morphology code of cervical cancer in the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The primary site of cancer confirmed as the cervix; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Complete clinical data available.\u003c/p\u003e\u003cp\u003eExclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Excluding cases with dual primary tumors; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Incomplete or missing clinical data; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Presence of distant metastasis (M1).\u003c/p\u003e\u003cp\u003eA total of 5,787 cervical cancer patients who met the inclusion criteria were included in the study, and these patients were randomly divided into a training group (4,051 cases) and an internal validation group (1,736 cases) at a 7:3 ratio, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurthermore, to further validate the applicability and stability of the model, this study retrospectively collected clinical data of cervical cancer patients diagnosed and surgically treated at the Second Affiliated Hospital of Dalian Medical University between January 2019 and December 2024. Tumor size can be assessed preoperatively through clinical pelvic examination and imaging techniques such as CT or MRI, and is defined as the longest diameter of the tumor.The diagnosis of LNM was confirmed based on histopathological examination of surgical specimens.The inclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Pathologically confirmed diagnosis of cervical cancer; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Primary cancer site confirmed as the cervix; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Complete clinical data available. Exclusion criteria included incomplete clinical information.A total of 338 patients were included as the external validation group. The use of the SEER database does not require additional informed consent as patient privacy data has been protected. Additionally, informed consent was obtained from the patients involved in this study, and there are no potential conflicts of interest. The study protocol has been approved by the Ethics Committee of the Second Affiliated Hospital of Dalian Medical University(Approval No. KY2025-314-01).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Statistical Methods\u003c/h2\u003e\u003cp\u003eData were analyzed using SPSS 27.0 software. Categorical data were expressed as frequency (n) and percentage (%). The baseline characteristics of patients in the model group and internal validation group were compared using the Chi-square test (χ\u0026sup2; test).\u003c/p\u003e\u003cp\u003eUnivariate analysis was conducted using the Chi-square test or Fisher's exact test. Variables with statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and those confirmed to be associated in previous studies were included in multivariate analysis. Multivariate analysis was performed using binary logistic regression to identify independent factors influencing lymph node metastasis in cervical cancer patients.\u003c/p\u003e\u003cp\u003eR 3.7 software and relevant R packages were used to generate a nomogram, receiver operating characteristic (ROC) curve, and calibration curve. The area under the curve (AUC) and its 95% confidence interval (95% CI) were calculated to assess the model's discriminative ability. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Additionally, the Hosmer-Lemeshow goodness-of-fit test and Spiegelhalter Z test were used to evaluate the model's fit. A P value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates no significant difference between the predicted and observed values, suggesting good model fit.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Patient Population\u003c/h2\u003e\u003cp\u003eA total of 5787 patients were included, with 4051 patients in the training group and 1736 patients in the internal validation group. There were no statistically significant differences between the two groups in terms of age, pathological type, tumor stage, grade, and tumor diameter (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). See 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\u003eBasic clinical data of two groups of patients\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\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraining set (n=4051 )\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValidation set\u003c/p\u003e\u003cp\u003e(n=1736 )\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, n(%)\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;\u0026thinsp;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1984(48.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e830(47.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1605(39.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e712(41.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e462 (11.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e194(11.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistologictype, n(%)\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\u003eSquamous cell carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2406(59.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1015(58.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e4.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdenocarcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e963 (23.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e392 (22.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e682 (16.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e329 (18.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1068(26.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e472(27.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1808(44.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e755(43.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e391 (9.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e163(9.39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e784 (19.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e346(19.93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade\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\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e380 (9.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e186(10.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e3.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1015(25.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e411(23.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e716 (17.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e310(17.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnkown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1940(47.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e829(47.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize\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;\u0026thinsp;2cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1644(40.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e676(38.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e2.953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026minus;4cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e999 (24.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e420(24.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026minus;8cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1264(31.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e568(32.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;8cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 (3.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (4.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLNM\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e729(18.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e312 (17.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3322(82.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1424(82.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eLNM, lymph node metastasis\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=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2 General Information of the External Validation Group\u003c/h2\u003e\u003cp\u003eTo assess the robustness and applicability of the model, this study included 338 patients in the external validation group, all of whom were pathologically confirmed cervical cancer cases.\u003c/p\u003e\u003cp\u003eAfter comparing the baseline clinical characteristics between the external validation group and the SEER database cohort, we found significant differences between the two groups in several variables, including age, tumor grade and stage, histologic type, and tumor size (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that the clinical feature distributions were not completely consistent between the cohorts (see 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\u003eComparison of patient characteristics between the External Validation Group and the SEER Database Cohort\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\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExternal Validation Group (n\u0026thinsp;=\u0026thinsp;338)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSEER Database Cohort(n\u0026thinsp;=\u0026thinsp;5787)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, n(%)\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;\u0026thinsp;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58(17.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2814(48.63)\u003c/p\u003e\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\u003e45\u0026ndash;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e231(68.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2317(40.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49(14.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e656 (11.33)\u003c/p\u003e\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\u003eHistologictype, n(%)\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\u003eSquamous cell carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e301(89.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3421(59.13)\u003c/p\u003e\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\u003eAdenocarcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32(9.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1355(23.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1011(17.46)\u003c/p\u003e\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\u003eStage, n(%)\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\u003eIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1540(26.61)\u003c/p\u003e\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\u003eIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e231(68.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2563(44.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e189.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74(21.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e554 (9.57)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29(8.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1130 (19.53)\u003c/p\u003e\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\u003eGrade, n(%)\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\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11(3.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e566(9.78)\u003c/p\u003e\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\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e201 (59.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1426(24.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e386.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126 (37.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1026(17.73)\u003c/p\u003e\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\u003eunknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2769(47.85)\u003c/p\u003e\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\u003eSize, n(%)\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;\u0026thinsp;2cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58(17.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2320(40.09)\u003c/p\u003e\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\u003e2\u0026minus;4cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162(47.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1419(24.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026minus;8cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113(33.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1832(31.66)\u003c/p\u003e\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;8cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e216(3.73)\u003c/p\u003e\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\u003eLNM\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (18.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1041(17.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e274(81.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4746(82.01)\u003c/p\u003e\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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eLNM, lymph node metastasis\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\u003e2.3 Factors Influencing Lymph Node Metastasis in the Training Group\u003c/h2\u003e\u003cp\u003eUnivariate analysis in the training group showed that pathological type, tumor stage and grade, and tumor diameter were significantly associated with lymph node metastasis in cervical cancer patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age was not associated with lymph node metastasis in cervical cancer patients (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eBinary logistic regression analysis indicated that age, pathological type, tumor stage and grade, and tumor diameter were significantly associated with lymph node metastasis in cervical cancer patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). 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\u003eUnivariate and Multivariate Analysis of Lymph Node Metastasis Risk in Cervical Cancer Patients\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\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;45\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\u003cp\u003eReference\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\u003e45\u0026ndash;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.860\u0026ndash;1.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.713\u0026ndash;1.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.756\u0026ndash;1.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.448\u0026ndash;0.791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistologic type, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSquamous cell carcinoma\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\u003cp\u003eReference\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\u003eAdenocarcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.301\u0026ndash;0.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.443\u0026ndash;0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.461\u0026ndash;0.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.510\u0026ndash;0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\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=\"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\u003eIA\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\u003cp\u003eReference\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\u003eIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.426\u0026ndash;4.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.215\u0026ndash;2.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.018\u0026ndash;10.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.547\u0026ndash;3.490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.789\u0026ndash;14.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.244\u0026ndash;4.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG1\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\u003cp\u003eReference\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\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.682\u0026ndash;6.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.236\u0026ndash;3.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.866\u0026ndash;9.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.504\u0026ndash;3.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnkown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.673\u0026ndash;3.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.112\u0026ndash;2.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2cm\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\u003cp\u003eReference\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\u003e2\u0026minus;4cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.733\u0026ndash;4.659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.881\u0026ndash;3.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026minus;8cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.139\u0026ndash;9.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.987\u0026ndash;5.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;8cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.135\u0026ndash;19.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.152\u0026ndash;9.869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eOR, Odds Ratio; CI, Confidence Interval\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e2.4 Establishment of the Nomogram Prediction Model for Lymph Node Metastasis in Cervical Cancer Patients and External Dataset Validation\u003c/p\u003e\u003cp\u003eBased on the results of multivariate regression analysis, a nomogram was developed to predict lymph node metastasis in cervical cancer patients by incorporating the identified risk factors. The nomogram for predicting lymph node metastasis in cervical cancer patients is shown in Fig.\u0026nbsp;2. For each influencing factor, a corresponding score is assigned, and the total score is calculated by summing the individual scores, which then corresponds to the risk of lymph node metastasis in cervical cancer as shown in the nomogram.\u003c/p\u003e\u003cp\u003eThe AUC in the training group was 0.758 (95% CI: 0.739\u0026ndash;0.776, Fig.\u0026nbsp;4A), and the AUC in the internal validation group was 0.753 (95% CI: 0.726\u0026ndash;0.781, Fig.\u0026nbsp;4B), indicating good diagnostic performance. The calibration plots demonstrated good consistency between the predicted and actual nomogram values in both the training and validation groups (Fig.\u0026nbsp;4C-D). Additionally, the Hosmer-Lemeshow goodness-of-fit test yielded P\u0026thinsp;=\u0026thinsp;0.874 in the training group and P\u0026thinsp;=\u0026thinsp;0.472 in the internal validation group, with both P values\u0026thinsp;\u0026gt;\u0026thinsp;0.05, suggesting that there was no significant difference between the predicted and observed values in the nomogram.\u003c/p\u003e\u003cp\u003eThe final multivariable logistic regression model for predicting lymph node metastasis (N) is presented in Figure 3.The formula details are provided in the design of the logistic regression model Excel template, included as Appendix 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.5 Risk Validation of Lymph Node Metastasis in Cervical Cancer Using the External Validation Set\u003c/p\u003e\n\u003cp\u003eThe AUC in the external validation group was 0.742 (95% CI: 0.678-0.805), as shown in Figure 5A. The calibration curve indicated that the trends of the three curves in the modeling group and the external validation group were nearly identical (P \u0026gt; 0.05), suggesting that the model has good predictive value, as shown in Figure 5B.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Hosmer-Lemeshow goodness-of-fit test yielded P \u0026lt; 0.05. Therefore, a Spiegelhalter Z test was conducted to further assess the presence of systematic bias in the model. The result showed Z = -1.65, P = 0.099. Given the consistency with the calibration curve trends, this suggests that the model has good predictive and calibration capabilities.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThis study successfully developed a risk prediction model for lymph node metastasis in cervical cancer based on five easily accessible preoperative clinical indicators: age, clinical stage, histologic type, maximum tumor diameter, and pathological type, under the 2018 FIGO staging system. The model demonstrated good discrimination (AUC\u0026thinsp;=\u0026thinsp;0.758) and calibration (Hosmer-Lemeshow test, P\u0026thinsp;=\u0026thinsp;0.874), providing a strong quantitative basis for individualized preoperative treatment decisions. In the external validation cohort, we assessed the model's calibration using both the Hosmer-Lemeshow test and Spiegelhalter\u0026rsquo;s Z test. Although the Hosmer-Lemeshow test indicated a statistical difference between the predicted and observed values (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting some calibration bias in certain risk strata, the Spiegelhalter\u0026rsquo;s Z test (Z = -1.65, P\u0026thinsp;=\u0026thinsp;0.099) showed no significant difference between the overall predicted and actual outcomes. Therefore, despite some potential bias in certain subgroups, the model demonstrates good overall calibration and clinical applicability.\u003c/p\u003e\u003cp\u003eIn this study, although the model demonstrated good predictive performance in the external validation dataset, we observed notable differences in clinical characteristics between the external modeling cohort and the SEER database cohort. These discrepancies may be attributed to differences in data sources, geographic regions of patient recruitment, clinical management strategies, or inclusion criteria. The external validation cohort was derived from a single-center hospital in China, whereas the SEER database includes data from multiple regions across the United States, potentially reflecting variations in ethnicity, healthcare systems, and screening practices.\u003c/p\u003e\u003cp\u003eBased on the relationship between the total score of the nomogram and the probability of event occurrence, this study further stratified patients into low, medium, and high-risk groups. The low-risk group (total score 0\u0026ndash;97) had a lymph node metastasis probability of 0.25\u0026ndash;10%, the medium-risk group (total score 97.1\u0026ndash;130.3) had a probability of 10.7\u0026ndash;31.3%, and the high-risk group (total score\u0026thinsp;\u0026gt;\u0026thinsp;130.3) had a probability of 31.3\u0026ndash;99.75%. This stratification system helps clinicians accurately identify patients at different risks for lymph node metastasis, enabling the development of differentiated treatment strategies. For example, low-risk patients can avoid unnecessary extensive lymphadenectomy, while high-risk patients can be considered for adjuvant radiotherapy or chemotherapy in advance. This approach maximizes the effectiveness of individualized treatment and reduces postoperative complication risks.\u003c/p\u003e\u003cp\u003eIt is important to emphasize that lymph node metastasis is a key factor influencing the prognosis of cervical cancer 【9, 10, 11】 A cohort study by Deng et al. 【12】 demonstrated that the 5-year survival rate of cervical cancer patients without lymph node metastasis was as high as 93%, whereas the 5-year survival rate dropped to 64% and 42% in patients with pelvic and paraaortic lymph node metastasis, respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)【13】. Furthermore, a study by Cibula et al. 【3】indicated that the risk of lymph node metastasis significantly increases with the progression of staging (with a metastasis rate of 2.1% in stage IA and 34.7% in stage IIB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, at present, clinical assessment of lymph node status mainly relies on imaging techniques, which have significant limitations. As shown in a study by Choi et al.【14】, while Positron Emission Tomography\u0026ndash;Magnetic Resonance Imaging(PET-MRI)demonstrates a sensitivity of 78% and specificity of 92% for detecting metastatic lymph nodes greater than 1 cm in diameter, its sensitivity drops drastically to 21% for detecting small metastatic lesions (\u0026lt;\u0026thinsp;5 mm ), making it challenging to meet the needs of early and precise evaluation.\u003c/p\u003e\u003cp\u003eIn clinical practice, while lymph node dissection remains the gold standard for diagnosing lymph node metastasis, the lack of clear indications for its use has led to many low-risk patients undergoing unnecessary lymphadenectomy, resulting in severe complications such as lymphocyst, lymphedema, and lower limb dysfunction 【15, 16】. In fact, the overall lymph node metastasis rate in cervical cancer is approximately 15\u0026ndash;20% 【17】, meaning that over 70% of patients do not have lymph node metastasis post-surgery, highlighting the urgent need for accurate preoperative risk assessment tools to guide treatment strategies.\u003c/p\u003e\u003cp\u003eTo address this issue, SLNB has been proposed as an effective method to reduce unnecessary dissection rates 【18\u0026ndash;19】. A study by L\u0026eacute;curu et al.【6】demonstrated that for early cervical cancer patients with tumors smaller than 2 cm, the detection rate of SLNB was 94.7%, with a false-negative rate of only 2.1%. However, for tumors larger than 4 cm, the SLNB detection rate dropped to 66.7%, with a false-negative rate increasing to 15.8%, suggesting its limited applicability in advanced or large tumors. Furthermore, Only a few medical institutions worldwide have the advanced technology required to perform SLNB, and the lack of technology and experience, especially in developing countries, severely limits the widespread adoption of SLNB.\u003c/p\u003e\u003cp\u003eFaced with the current dilemma between \u0026ldquo;over-treatment\u0026rdquo; of cervical cancer lymphadenectomy and the need for \u0026ldquo;accurate assessment,\u0026rdquo; it is crucial to develop lymph node metastasis risk prediction tools based on preoperatively available indicators to assist clinicians in making more rational treatment decisions, ultimately achieving the dual goal of reducing complications and improving survival rates. Therefore, establishing a stratified risk prediction model for cervical cancer lymph node metastasis based on clinical data is of significant clinical importance【20】.\u003c/p\u003e\u003cp\u003eThe nomogram model developed in this study, based on easily accessible clinical variables, demonstrated favorable diagnostic performance in predicting LNM in cervical cancer, with an AUC of 0.75. Although its predictive accuracy is slightly lower than that of some advanced models utilizing radiomics or molecular markers, it offers substantial advantages in terms of clinical feasibility, cost-effectiveness, and operational simplicity, thereby serving as a valuable complementary tool in clinical practice.\u003c/p\u003e\u003cp\u003eNumerous studies have reported predictive models for LNM in cervical cancer. Radiomics-based models extract high-dimensional features from MRI or CT images to construct predictive algorithms. For instance, Dong et al. developed a radiomics model based on multiparametric MRI features, achieving an AUC of 0.803 in the test set【21】. Another study demonstrated that combining radiomics with clinical features improved model performance, reaching an AUC of 0.848【22】. However, radiomics approaches often require dedicated software and advanced algorithms, involve complex image processing workflows, and depend heavily on imaging equipment, which limits their applicability in resource-constrained healthcare settings.\u003c/p\u003e\u003cp\u003eModels based on molecular biomarkers utilize gene expression profiles to predict LNM, with some studies reporting AUCs exceeding 0.855【23】. Despite their high predictive accuracy, such models demand high-quality biospecimens, sophisticated testing platforms, and robust bioinformatics analysis pipelines, thereby posing significant barriers to clinical translation.\u003c/p\u003e\u003cp\u003eBy contrast, the model proposed in this study relies solely on routinely collected clinical parameters, such as FIGO stage, tumor size, and histological type. It does not require additional imaging or molecular testing, making it highly accessible and easily generalizable. Although the AUC is modestly lower than that of more complex models, its practical predictive ability remains robust. Notably, it may serve as a rapid and effective preoperative risk stratification tool, particularly in settings where advanced diagnostic modalities such as SLNB or PET-CT are unavailable. This model can assist clinicians in preliminarily identifying patients at elevated risk of LNM and guide decisions regarding the need for sentinel lymph node biopsy or systematic lymphadenectomy.\u003c/p\u003e\u003cp\u003eIt is important to underscore that this model is not intended to replace SLNB or advanced imaging, especially in the detection of micrometastases, where its sensitivity may be limited. Nevertheless, it has considerable clinical value during the early stages of patient assessment and treatment planning, particularly in screening high-risk individuals and streamlining therapeutic pathways. Future research should focus on integrating this nomogram with radiomic and molecular predictors to construct multidimensional, hybrid models that may enhance overall predictive performance.\u003c/p\u003e\u003cp\u003eMoreover, this model offers a user-friendly and intuitive risk assessment tool for both clinicians and patients, thereby facilitating precision medicine and personalized care. Future studies should emphasize multicenter external validation, cross-population applicability, and dynamic model updating to ensure its robustness and clinical utility across diverse healthcare environments.\u003c/p\u003e\u003cp\u003ePreoperative or pretreatment assessment of lymph node status in cervical cancer involves a variety of methods, including MRI, PET-CT, CT, SLNB, frozen section examination (FSE), fine needle aspiration (FNA), radionuclide imaging, and spectral CT. Each method differs in terms of diagnostic accuracy, clinical applicability, and operational complexity. While MRI and PET-CT offer relatively high accuracy, they cannot fully replace pathological confirmation. SLN biopsy and FSE provide real-time histological information to support intraoperative decision-making. Emerging techniques such as FNA and spectral CT show promise but require further validation. Clinicians should tailor diagnostic strategies based on individual patient characteristics and integrate multiple approaches to optimize treatment planning, improve therapeutic outcomes, and enhance overall survival and quality of life【24\u0026ndash;25】.\u003c/p\u003e\u003cp\u003eThis study integrated data from the SEER database (n\u0026thinsp;=\u0026thinsp;5787) and real-world data (n\u0026thinsp;=\u0026thinsp;388) to construct a preoperative risk prediction tool applicable to various healthcare resource settings, providing strong support for individualized clinical treatment. However, it is important to note several limitations in this study: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Risk of data bias: Due to its retrospective design, some key variables were missing, particularly since the SEER database does not include important pathological parameters such as vascular invasion, depth of stromal invasion, or extent of lymphadenectomy, which may affect the comprehensiveness and accuracy of the model; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Insufficient integration of imaging data: This study did not incorporate imaging features from modalities like CT/MRI, limiting the model's ability to identify small lymph node metastases (e.g., micrometastases or isolated tumor cell clusters) ; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Methodological limitations: The statistical modeling methods used are based on assumptions of linear relationships between data, which may fail to capture the complex non-linear associations and interaction effects among variables, making the model sensitive to outliers and multicollinearity, and potentially performing poorly in situations of sample imbalance (e.g., low incidence of lymph node metastasis).(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) External validation was limited to a single center, and significant differences in case composition may limit the generalizability of the model.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)Although the nomogram model developed in this study demonstrated good discrimination and calibration in the validation cohort, its overall predictive performance still has room for improvement. Future studies may consider incorporating more advanced statistical approaches or machine learning algorithms (such as random forest, gradient boosting, or neural networks) to capture complex nonlinear relationships and enhance model accuracy. However, it is important to balance model complexity with clinical interpretability to ensure practical applicability in real-world clinical settings.༈6༉In this study, a substantial proportion of tumor grade information was missing (approximately 48%), which may affect the stability and generalizability of the model. Future studies should validate the applicability and reliability of the model using broader datasets and populations to enhance its practical value and potential for clinical implementation.\u003c/p\u003e\u003cp\u003eIn conclusion, although this study provides a new tool for preoperative lymph node metastasis risk stratification in cervical cancer, future research should involve prospective, multicenter large-scale cohort data validation, integrating imaging genomics, molecular markers, and multimodal data to further optimize and enhance the model's accuracy and applicability.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study successfully developed a cervical cancer lymph node metastasis risk prediction model based on five easily accessible preoperative clinical indicators, demonstrating good discrimination and clinical utility. The model can effectively identify patients with varying risks of lymph node metastasis, providing scientific evidence for clinicians to develop individualized treatment plans. It has the potential to significantly reduce 15\u0026ndash;20% of unnecessary lymphadenectomies, lowering related complications and improving patient prognosis.\u003c/p\u003e\u003cp\u003eIn the future, further validation through multicenter, prospective cohort studies is needed, alongside integration of imaging genomics, genomics, pathology, and clinical data to develop predictive tools with high interpretability and generalizability. This would support the development of more precise and personalized cervical cancer treatment decisions, ultimately achieving the dual goals of improving patient survival and optimizing quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe study had adhered to the principles sated in the “Declaration of Helsinki”\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to protecting personal privacy, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) reported there is no funding associated with the work featured in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed substantially to this work. JW and SH contributed to the conception and design of the study, data analysis and interpretation, drafting and finalization of the manuscript, and conceiving the analysis. YW and XW contributed to the conception and acquisition of data, and critical revision of the manuscript. JN、QL and YL contributed to data acquisition and manuscript drafting. YL contributed to the acquisition and analysis of the data and critical revision of the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArbyn, M., Weiderpass, E., Bruni, L. et al. Estimates of incidence and mortality of cervical cancer in 2020: a worldwide analysis. The Lancet Global Health, 2020. DOI: 10.1016/S2214-109X(19)30482-6\u003c/li\u003e\n\u003cli\u003eBhatla N, Berek JS, Cuello Fredes M, et al. 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Eur Radiol. 2020;30(10):5560-5577. doi:10.1007/s00330-020-06909-3\u003c/li\u003e\n\u003cli\u003eCibula D, Kocian R, Plaikner A, et al. Sentinel lymph node mapping and intraoperative assessment in a prospective, international, multicentre, observational trial of patients with cervical cancer: The SENTIX trial. Eur J Cancer. 2020;137:69-80. doi:10.1016/j.ejca.2020.06.034\u003c/li\u003e\n\u003cli\u003eCibula D, Raspollini MR, Planchamp F, et al. ESGO/ESTRO/ESP Guidelines for the management of patients with cervical cancer - Update 2023. Int J Gynecol Cancer. 2023;33(5):649-666. Published 2023 May 1. doi:10.1136/ijgc-2023-004429\u003c/li\u003e\n\u003cli\u003eRonsini C, Anchora LP, Restaino S, et al. The role of semiquantitative evaluation of lympho-vascular space invasion in early stage cervical cancer patients. Gynecol Oncol. 2021;162(2):299-307. doi:10.1016/j.ygyno.2021.06.002\u003c/li\u003e\n\u003cli\u003eSakuragi N, Satoh C, Takeda N, et al. 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Published 2023 Mar 30. doi:10.3390/cancers15072061\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Appendix","content":"\u003cp\u003eAppendix is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SEER database, cervical cancer, lymph nodes, nomogram, model","lastPublishedDoi":"10.21203/rs.3.rs-7288190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7288190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: To explore the risk factors for lymph node metastasis in patients with cervical cancer, construct a risk prediction model for cervical cancer lymph node metastasis based on the FIGO 2018 staging system, and validate the model using both internal and external datasets. This model aims to provide a simple and effective tool for the clinical assessment of lymph node metastasis risk in cervical cancer patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 5787 patients with pathologically confirmed cervical cancer from the SEER database, diagnosed between 2000 and 2021, were selected. The patients were randomly divided into a training group and an internal validation group in a 7:3 ratio using R software. Univariate and binary logistic regression analyses were employed to identify independent factors affecting lymph node metastasis in cervical cancer patients. A nomogram prediction model was developed based on the selected factors. The model's performance was evaluated using receiver operating characteristic curves and calculating the area under the curve , along with calibration curves. The Hosmer-Lemeshow goodness-of-fit test and Spiegelhalter Z test were used to assess model performance. Additionally, an external validation cohort consisting of 338 cervical cancer patients treated at our institution between January 2019 and December 2024 was used for receiver operating characteristic curve and calibration curve validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 5787 cervical cancer patients from the SEER database were included, with 4051 patients in the training group and 1736 patients in the internal validation group. Among these, 729 patients (18.00%) in the training group and 312 patients (17.97%) in the internal validation group had lymph node metastasis. Univariate analysis revealed that histologic type, tumor stage and grade, and tumor size were significantly associated with lymph node metastasis in cervical cancer (P \u0026lt; 0.05). Binary logistic regression analysis identified age, histologic type, tumor stage and grade, and tumor size as factors significantly related to lymph node metastasis in cervical cancer (P \u0026lt; 0.001). The nomogram model based on these variables showed an AUC of 0.758 (95% CI: 0.739–0.776) in the training group, 0.753 (95% CI: 0.726–0.781) in the internal validation group, and 0.742 (95% CI: 0.678–0.805) in the external validation group, indicating good discrimination and stability of the model. The Hosmer-Lemeshow test for the training and internal validation groups both yielded P values \u0026gt; 0.05, and the Spiegelhalter Z test for the external validation group yielded a P value of 0.0989. Calibration curves showed good agreement between predicted probabilities and actual observed outcomes, suggesting excellent model fit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The FIGO 2018-based risk prediction model for lymph node metastasis in cervical cancer, developed using the SEER database, demonstrates high predictive accuracy and strong clinical applicability. This model provides an effective reference for the precise preoperative assessment of lymph node metastasis risk in cervical cancer patients.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a FIGO 2018 Staging-Based Risk Prediction Model for Lymph Node Metastasis in Cervical Cancer: A SEER Database Analysis with Internal and External Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 08:57:24","doi":"10.21203/rs.3.rs-7288190/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-25T13:20:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-23T15:52:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125083804720240918758689932530922117038","date":"2025-11-23T14:35:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T04:35:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100762398236252747346338823715042812272","date":"2025-10-19T14:37:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-19T13:26:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-11T17:35:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T02:34:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-11T02:32:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Surgery","date":"2025-08-04T07:25:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"77d7e744-eba8-4b2e-bf15-1397bceedf15","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T13:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 08:57:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7288190","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7288190","identity":"rs-7288190","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}