A retrospective study using the Surveillance Epidemiology and End Results (SEER) database to predict risk and prognostic factors for lung metastasis in cervical carcinoma: nomogram development and validation

Introduction

Cervical carcinoma is the fourth most prevalent and fatal cancer affecting women around the world. The Global Cancer Statistics 2020 reported 604,000 new cases and 342,000 deaths worldwide, with low and middle-income countries bearing a disproportionately high burden (1). Chronic infection with high-risk human papillomavirus (HPV) is the main cause of cervical cancer, especially types 16 and 18, although other cofactors, such as smoking, immunosuppression, and socioeconomic status, also contribute to disease progression (2). Despite advances in screening programs and HPV vaccination, many patients are diagnosed at later stages, leading to poor prognoses.

Metastasis is the primary contributor to cervical carcinoma mortality. While regional lymph nodes, the liver, and bones are commonly affected, lung metastasis is becoming more acknowledged as a major issue in cervical cancer, particularly in advanced and recurrent cases (3). A study suggest that the incidence of lung metastasis in cervical cancer ranges from 4% to 9%, with adenocarcinoma and adenosquamous carcinoma showing a higher predilection for lung involvement compared to squamous cell carcinoma (4). Lung metastases can present as solitary or multiple lesions, with diverse clinical manifestations ranging from asymptomatic nodules detected on imaging to severe respiratory symptoms (5).

Despite improvements in detecting lung metastasis with imaging techniques like chest computed tomography (CT) and positron emission tomography (PET), challenges remain in differentiating metastatic lesions from other lung pathologies, such as infections or benign nodules (6). Additionally, the optimal management strategy for cervical cancer with lung metastases remains unclear. Systemic chemotherapy, immune checkpoint inhibitors, and targeted therapies have shown potential, but their efficacy is limited by tumor heterogeneity and resistance mechanisms (7).

Nomograms are essential for forecasting cancer outcomes and handling recurrence. They transform intricate statistical models into straightforward numerical forecasts, providing an accurate assessment of the probability of events like recurrence or death. By integrating individual patient-specific variables, nomograms offer personalized predictions that enhance clinical decision-making (8,9). In cancer care, nomograms have become a more effective option compared to the traditional tumor-node-metastasis (TNM) staging systems across various cancer types, setting a new benchmark in medical care (10-12). Consequently, nomograms serve as an efficient means to accomplish our objectives (13). Using the Surveillance, Epidemiology, and End Results (SEER) database, this research examined the risk factors, occurrence, and outcomes of lung metastasis in cervical cancer. Additionally, we developed two nomograms: one for predicting lung metastasis in cervical cancer patients and another for estimating the overall survival (OS) of those with lung metastasis. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-221/rc).

Methods

Selection of patients

This study is a retrospective cohort analysis that utilized data on cervical cancer patients from the SEER database (https://seer.cancer.gov/), spanning from 2000 to 2021 and incorporating information from 17 cancer registries. Initiated in 1973 and supported by the National Cancer Institute (NCI), the SEER program provides comprehensive information on cancer incidence and survival rates, encompassing approximately 28% of the U.S. population via national cancer registries (14). Data for this study were collected using SEER*Stat software version 8.4.4. The criteria for inclusion were described as below: (I) patients with cervical carcinoma confirmed through histological examination, diagnosed based on the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3); (II) the availability of demographic information, such as age, marital status, and race; and (III) availability of clinical and pathological data, such as histology, grade, primary site, T stage, N stage (according to the 7th Edition of the American Joint Committee on Cancer Staging Manual), surgical intervention, treatment modalities (radiotherapy and chemotherapy), presence of lung metastasis, liver metastasis and bone metastasis. Patients were excluded if they did not have a histologically confirmed diagnosis, unknown marital status, unknown race, missing information on lung, liver, or bone metastasis, incomplete data on radiotherapy or chemotherapy, unclear surgical history, unknown grade, unknown T stage, N stage, or primary site. The study excluded patients who had incomplete medical records or were lost to follow-up. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Collection of data

This study included 12,632 cervical carcinoma patients after the exclusion of ineligible cases, among whom 379 exhibited lung metastasis. The full patient cohort was utilized to create a diagnostic model identifying risk factors for lung metastasis and to construct a predictive nomogram. Additionally, a cohort for prognostic analysis was formed, comprising 379 cervical carcinoma patients with lung metastasis who had detailed treatment data, encompassing details on surgical interventions, radiotherapy and chemotherapy. This group was utilized to develop a novel prognostic nomogram for forecasting the outcomes of patients with lung metastases. The diagnostic group was divided randomly into a training set comprising 70% and a validation set comprising 30%. The prognostic group included patients with lung metastasis selected from the diagnostic cohort’s training and validation sets. The training set in each cohort was used to build the nomogram, and its performance was assessed using the validation set. Furthermore, prognostic factors for cervical carcinoma patients with lung metastasis were analyzed through survival analyses. The primary outcome was OS, which was measured from the time of diagnosis until death from any cause.

Statistical analysis

In this study, all statistical analyses were performed using SPSS software (version 26.0) and R software (version 4.4.1; R Foundation for Statistical Computing, Vienna, Austria, http://www.r-project.org). SPSS was used to perform univariate and multivariate logistic regression, as well as univariate and multivariate Cox regression analyses. A P value below 0.05 on both sides was considered statistically significant. To evaluate the distribution of variables between the two groups, the Chi-squared test or Fisher’s exact test was employed. The Chi-squared test was applied when the minimum expected frequency exceeded 5. Fisher’s exact test was used as an alternative when the Chi-squared test P value was close to 0.05, and it was also applied when the minimum expected frequency was under 5. The R software packages “rms”, “foreign”, “regplot”, “ggplot2”, “pROC”, “ggDCA”, “survival”, and “survminer” were used to construct and visualize nomograms, receiver operating characteristic (ROC) curves, calibration curves, decision curve analyses (DCA), and Kaplan-Meier survival curves. In the diagnostic cohort, univariate and multivariate logistic regression analyses were conducted to identify independent risk factors associated with lung metastasis in patients with cervical carcinoma. A diagnostic nomogram was created using these independent risk factors, and its effectiveness was assessed by determining the area under the curve (AUC) via ROC curve analysis. Furthermore, the nomogram’s performance was assessed through calibration and DCA curves. We performed univariate and multivariate Cox regression analyses to identify independent prognostic factors. A prognostic nomogram was created using these independent predictors to forecast the OS of cervical carcinoma patients with lung metastasis. Based on the nomogram’s methodology, risk ratings were determined for each person. Additionally, time-dependent ROC curves were developed to assess the nomogram’s predictive performance over various time periods. Additionally, calibration and DCA curves were produced to offer supplementary evaluation of the nomogram’s functionality. The median risk score was used to divide patients with lung metastases from cervical cancer into high-risk and low-risk categories. The OS between the high-risk and low-risk groups was evaluated and compared using Kaplan-Meier survival curves and the log-rank test.

Results

Clinical characteristics at baseline of cervical cancer patients

A total of 12,632 patients with a cervical cancer diagnosis were included in this study. The patient cohort was divided into two sets: 8,839 individuals in the training set and 3,793 in the validation set, enabling comprehensive stratification and analysis. Out of the 12,632 cases, 379 patients developed lung metastasis, with 253 cases assigned to the training cohort and the remaining 126 to the internal validation cohort. Table 1 displays the baseline clinical characteristics and treatment protocols of patients with cervical carcinoma. It was found that 76.30% of patients were white, and squamous cell carcinoma (65.50%) was the most frequently observed histological subtype. Grade II differentiation was the most prevalent (43.27%). Among primary tumor sites, the cervix uteri was the most common location (75.89%). The majority of patients were classified as stage T1 (58.35%) and stage N0 (73.99%). Regarding treatment, 7,858 patients (62.21%) underwent surgery, 7,547 patients (59.74%) received radiotherapy, and 6,603 patients (52.27%) received chemotherapy. In terms of distant metastasis, 205 cases (1.62%) presented with bone metastasis, 156 cases (1.23%) with liver metastasis, and 379 cases (3.00%) with lung metastasis. Furthermore, the randomness of the allocation was examined using the Chi-squared test, and the results confirmed complete randomization.

Significant differences were observed between these patients without lung metastasis and those with lung in terms of age (median: 48 years, range, 15–90 years vs. median: 57 years, range, 16–90 years, P<0.001), histology, grade, primary site, T stage, N stage, whether surgery was performed, whether radiotherapy was administered, whether chemotherapy was administered, and the presence of bone and liver metastasis (Table 2).

Factors independently associated with lung metastasis in cervical carcinoma patients

The findings of the univariate logistic analysis of thirteen possible factors are shown in Table 3, which found eleven variables linked to lung metastasis, including age, marital status, histology, grade, primary site, T stage, N stage, surgery, chemotherapy, liver metastasis, and bone metastasis. The multivariable logistic regression analysis identified age, histology, grade, primary site, T stage, N stage, surgery, liver metastasis, and bone metastasis as independent predictors of cervical carcinoma with lung metastasis.

Development and validation of the diagnostic nomogram

Based on eight independent indicators, a novel nomogram was developed to forecast lung metastases in patients with cervical cancer (Figure 1). ROC curves were created to evaluate the nomogram’s discriminative power; the training set’s AUC of 0.908, which denotes exceptional performance, was obtained (Figure 2A). Furthermore, Figure 2B shows that the calibration curve showed a high degree of agreement between the observed and predicted results. The DCA curve results clearly demonstrated the diagnostic nomogram’s enhanced performance in clinical practice, as illustrated in Figure 2C. The nomogram’s outstanding discriminative ability was demonstrated by the ROC analysis, which produced an AUC of 0.908 in the validation set (Figure 2D). A high level of concordance between the expected and actual results was shown by the calibration curve for the validation set (Figure 2E). Moreover, DCA for the validation set affirmed the nomogram’s strong clinical performance (Figure 2F). Figure 3A illustrates how the training and validation sets’ ROC curves differed noticeably even though their AUC values were the same. To further emphasize the importance of each independent predictor variable in determining the probability of lung metastasis, ROC curves were created for each one. To further illustrate the predictive performance of our model, we compared the ROC curves of individual predictors with that of the nomogram. As shown in Figure 3B,3C, our findings indicate that while some variables, such as surgery T stage and N stage, showed strong discriminatory ability on their own, the nomogram, which integrates multiple predictors, demonstrated superior overall performance. This highlights the advantage of using a comprehensive model that considers multiple risk factors rather than relying on a single variable for risk stratification. Notably, the nomogram achieved a higher AUC in both the training and validation cohorts than any of the independent variables. The nomogram’s higher prediction performance over individual predictors is highlighted by the significant difference in AUC values.

Figure 1 Nomogram to estimate the risk of lung metastasis in patients with cervical carcinoma. ADC, adenocarcinoma; LM, lung metastasis; N, node; SCC, squamous cell carcinoma; T, tumor.

Figure 2 Performance of the nomogram for predicting prognosis in the training cohort and the validation cohort in cervical carcinoma patients with lung metastasis. The ROC curves (A), calibration curves (B), and DCA (C) of the training set cohort. The ROC curves (D), calibration curves (E), and DCA (F) of the validation set cohort. AUC, area under the curve; DCA, decision curve analysis; ROC, receiver operating characteristic.

Figure 3 Comparison of predictive performance between the nomogram and individual predictors in cervical carcinoma patients with lung metastasis. (A) Merged ROC curves of the training and validation cohorts, the blue curve represents the training set, and the red curve represents the validation set. The area under the ROC curves was compared between the nomogram and all independent factors in both the training set (B) and validation set (C). AUC, area under the curve; N, node; ROC, receiver operating characteristic; T, tumor.

Univariate and multivariate Cox regression analyses for identifying OS predictive markers

This study analyzed prognostic factors using a cohort of 379 cervical carcinoma patients with lung metastases. Of these, 126 patients were allocated to the validation cohort, whereas 253 patients were assigned to the training cohort. As shown in Table 4, 42 patients (11.08%) underwent surgery, 248 patients (65.43%) received radiotherapy and 273 patients (72.03%) received chemotherapy. Similarly, among this group of cervical cancer patients with lung metastases, 68 patients (17.94%) had liver metastases, and 81 patients (21.37%) had bone metastases. The results of the chi-squared and Fisher’s exact tests showed that there were no appreciable variations in the baseline variables between the training and validation groups.

Surgery, radiotherapy, chemotherapy, liver metastasis, and bone metastasis were found to be significantly associated with OS in the univariate Cox proportional hazards regression analysis. We also included variables with a P value less than 0.1, such as histology and T stage, in the multivariate Cox analysis. In patients with cervical cancer who had lung metastases, multivariate Cox proportional hazards regression analysis verified that the existence of liver metastases, the absence of chemotherapy, and the absence of radiation therapy were independent predictors of OS (Table 5).

Development and validation of the prognostic nomogram

Three independent predictive markers were used to generate a prognostic nomogram for individuals with cervical cancer who had lung metastases (Figure 4). The accuracy of the nomogram in predicting OS at different time intervals was highlighted by the calibration curves, which showed a high degree of consistency between the actual outcomes in the training cohort and the anticipated 3-, 6-, and 12-month OS probabilities (Figure 5A-5C). Additionally, DCA curves for the training cohort highlighted the nomogram’s practical utility in clinical settings (Figure 5D-5F). For 3-, 6-, and 12-month OS, the calibration curves in the validation cohort also showed a good level of agreement between the actual and predicted probabilities (Figure 6A-6C). Additionally, the validation set’s DCA curves provided additional evidence of the nomogram’s efficacy in actual clinical settings (Figure 6D-6F).

Figure 4 Nomogram to predict the overall survival of cervical carcinoma patients with lung metastases.

Figure 5 The calibration curves of the prognostic nomogram for the 3 (A), 6 (B), and 12 (C) months in the training set and the DCA of the prognostic nomogram for the 3 (D), 6 (E), and 12 (F) months in the training set. DCA, decision curve analysis; OS, overall survival.

Figure 6 The calibration curves of the prognostic nomogram for the 3 (A), 6 (B), and 12 (C) months in the validation set and the DCA of the prognostic nomogram for the 3 (D), 6 (E), and 12 (F) months in the validation set. DCA, decision curve analysis; OS, overall survival.

The nomogram successfully differentiated OS outcomes for cervical cancer patients with lung metastases, according to the ROC analysis. For the training set, the AUC values at 3, 6, and 12 months were 0.825, 0.726, and 0.712, respectively, as shown in Figure 7A. At 3, 6, and 12 months, the AUC values in the validation set were 0.765, 0.704, and 0.664, respectively (Figure 7B). These findings demonstrate the nomogram’s great discrimination and prediction accuracy. Moreover, Kaplan-Meier survival analysis indicated a notable disparity in prognosis between the high-risk and low-risk groups, with patients in the high-risk group experiencing significantly poorer outcomes (P<0.0001) (Figure 7C,7D). In addition, the prognostic nomogram showed a significant net benefit over a wide range of mortality risks, highlighting its important clinical relevance in predicting OS for patients with lung metastases from cervical cancer.

Figure 7 Time-dependent ROC curves of 3, 6, and 12 months in the training set (A) and the validation set (B) and the Kaplan-Meier survival curves of the training set (C) and the validation set (D). AUC, area under the curves; ROC, receiver operating characteristic.

Discussion

Cervical carcinoma is a prevalent malignancy among women worldwide, with lung metastasis being one of the most common distant metastatic sites. Approximately 4–6% of cervical carcinoma patients develop lung metastasis, typically in advanced stages of the disease (stage IVB). Patients with lung metastasis often present with poor prognosis due to aggressive tumor behavior and limited treatment options. Since these patients typically have a median survival of less than a year, better management techniques are required (15,16).

Management of cervical carcinoma patients with lung metastasis typically involves systemic therapy, local treatment for symptom control, and palliative care. Platinum-based chemotherapy (e.g., cisplatin/paclitaxel) remains the cornerstone for first-line treatment. More recently, immune checkpoint inhibitors such as pembrolizumab (anti-PD-1 antibody) have shown promise in PD-L1-positive patients and are being integrated into treatment regimen (17,18). Additionally, anti-angiogenic therapy (e.g., bevacizumab) combined with chemotherapy has demonstrated improved survival rates in metastatic cervical cancer (19,20). Radiotherapy and surgery are occasionally employed for solitary or oligometastatic lung lesions. These approaches can provide local control and symptomatic relief but have limited efficacy in widespread metastatic disease. For patients with severe stages of the disease, improving their quality of life requires symptom treatment, which includes pain management and respiratory support.

Despite advancements in treatment, several challenges remain unresolved. Response rates to chemotherapy and immunotherapy in cervical cancer patients with lung metastases remain suboptimal, and the emergence of resistance to systemic therapies further limits their long-term effectiveness. The absence of reliable biomarkers for predicting the outcomes of immunotherapy or chemotherapy complicates efforts to tailor individualized treatment strategies (21). Additionally, the molecular mechanisms underlying cervical cancer metastasis, particularly to the lungs, remain poorly understood, hindering the development of effective targeted therapies (22). In low- and middle-income countries, the restricted availability of advanced treatments like immune checkpoint inhibitors and targeted therapies presents a major challenge (23). Furthermore, patients with distant metastases are often underrepresented in clinical trials, resulting in a lack of robust, evidence-based guidelines for managing this high-risk subgroup. However, the overall prognosis for cervical carcinoma patients with lung metastasis remains poor and presents significant challenges. Finding useful risk and predictive markers for this patient population is therefore essential. In the end, these initiatives can improve patient outcomes by promoting accurate prognostic evaluation, enabling prompt preventative interventions, and facilitating early diagnosis (24).

In this study, nomograms for diagnosis and prognosis were developed using data from a big database. These tools are intended to predict the likelihood of lung metastasis in cervical carcinoma patients and their OS once lung metastasis is present. By using patient-specific data in these nomograms, a total score can be computed. This score enables clinicians to identify high-risk individuals for lung metastasis on the diagnostic chart, facilitating early interventions and targeted clinical management. Similarly, the prognostic nomogram allows for precise evaluation of OS in cervical carcinoma patients with lung metastasis, aiding in the determination of optimal treatment strategies. These nomograms are straightforward to implement in clinical practice, providing physicians with a practical and reliable method to enhance decision-making with greater convenience and accuracy. To the best of our knowledge, this is the first multicenter, thorough retrospective analysis that aims to develop predictive nomogram models for determining the risk and prognosis of lung metastases in patients with cervical cancer.

Our analysis revealed that among the predictors included in the lung metastasis risk model for cervical cancer, liver metastasis contributed the most to the predictive power. This finding is biologically plausible, as the liver is a major blood supply organ, and its metastasis often has a substantial impact on the likelihood of further metastatic spread. The second most significant contributor was T stage, suggesting that tumor size plays a critical role in determining the risk of lung metastasis. This underscores the importance of tumor burden in metastatic progression and highlights the need for close monitoring of patients with advanced T stage cervical cancer. For the survival prediction model, the most influential factor was whether the patient received chemotherapy, which had a greater impact on survival than radiotherapy. This indicates that chemotherapy plays a crucial role in improving the prognosis of cervical cancer patients with lung metastases. Understanding these key determinants can assist clinicians in tailoring personalized treatment strategies, optimizing therapeutic interventions, and improving patient outcomes. Future studies should focus on refining risk stratification by incorporating additional clinical and molecular markers to enhance model accuracy and clinical applicability.

Our study primarily focuses on the statistical validation of the nomograms; however, their clinical applicability is equally crucial. A key question is whether specific risk thresholds identified in the nomograms can guide clinical decision-making. While our model provides individualized risk estimation, further studies are needed to establish precise cutoffs at which management strategies should change. For example, if a patient’s predicted risk of lung metastasis exceeds a certain threshold, clinicians might consider intensified surveillance strategies, including more frequent imaging follow-ups and earlier therapeutic interventions. Conversely, for patients with a low predicted risk, routine follow-up might be sufficient, potentially reducing unnecessary testing and interventions, thus optimizing resource utilization. Furthermore, integrating these nomograms into clinical workflows could help stratify patients into different risk categories, facilitating personalized treatment approaches. Comparison with existing clinical guidelines is necessary to determine whether these risk thresholds align with current standard-of-care recommendations. Future research should focus on prospective validation and real-world implementation of these nomograms to refine their role in clinical practice and integrate them into existing treatment guidelines.

Addressing the molecular mechanisms underlying lung metastases in cervical cancer holds promise for identifying novel therapeutic targets (25). Pathways involved in processes such as epithelial-mesenchymal transition (EMT) and immune evasion are particularly compelling areas for investigation. Emerging therapeutic modalities, including bispecific antibodies, CAR-T cell therapies, and oncolytic viruses, have shown promise in other malignancies and warrant exploration in metastatic cervical cancer. The advent of liquid biopsy technologies offers a non-invasive approach to monitor tumor burden, enable early detection of metastases, and identify predictive biomarkers for personalized therapy selection. Additionally, international collaborations are crucial to expand access to advanced treatments and clinical trials, especially in resource-constrained settings where disparities in care remain a significant challenge. Combining immunotherapy, chemotherapy, and targeted therapies presents a promising avenue for enhancing treatment efficacy and overcoming resistance mechanisms. By integrating these approaches with ongoing research into molecular drivers and biomarker discovery, the field can move toward more effective and equitable care for cervical cancer patients with lung metastases.

Nevertheless, there are a few limitations in our study that should be noted. First of all, there may be selection bias because the data came from a retrospective review of the SEER database (26). Another limitation is that the SEER database does not provide comprehensive details, such as patients’ family cancer history or specific data regarding chemotherapy and radiation therapies, which hindered our ability to include these factors (27). The relatively low incidence of cervical cancer led to fewer patients, which may have reduced the research’s overall impact even though our study had a bigger sample size than earlier studies. Finally, although we developed and validated the nomogram using a training set, the lack of inclusion of publicly available cervical carcinoma data from other databases may have introduced potential bias in our results.

Conclusions

In conclusion, we identified risk factors linked to lung metastasis in cervical cancer using univariate and multivariate logistic regression analysis. In order to determine the predictive markers for patients with cervical cancer who had lung metastases, we also conducted univariate and multivariate Cox regression analysis. Based on these findings, we created two prediction nomograms that offer crucial assistance for clinical judgment and improving patient care tactics.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-221/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-221/prf

Funding: This work was supported by grants from Tianjin Binhai New Area Health Research Project (No. 2024BWKZ09) and the National Natural Science Foundation of China (No. 82473144).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-221/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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