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A Chinese prospective cohort research developed and validated a risk prediction model for patients with cervical cancer

Cancer Cell International volume 25, Article number: 142 (2025) Cite this article

Abstract

Objective

Cervical cancer constitutes a formidable health challenge imperiling the well-being and lives of women globally, particularly in underdeveloped nations. The survival rates among patients diagnosed with cervical cancer manifest considerable heterogeneity, shaped by a myriad of variables. Within the scope of this inquiry, a predictive model for projecting overall survival (OS) in cervical cancer patients was formulated and subsequently validated.

Methods

Clinicopathological and follow-up information of patients diagnosed with cervical cancer were prospectively collected from May 1, 2015, to December 12, 2019, as part of an ongoing longitudinal cohort study conducted at Chongqing University Cancer Hospital. Subsequent to the acquisition of follow-up data, the sample was randomly divided into two cohorts: a training cohort (n = 2788) and a testing cohort (n = 1194). The predictors for the model were selected through least absolute shrinkage and selection operator (LASSO) regression analysis. Cox stepwise regression analysis was then employed to identify independent predictive indicators. The study results were subsequently presented in the form of static and web-based dynamic nomograms. To elucidate the objective validation of the prognosis and anticipated survival, the concordance index (C-index) was computed. The model’s discriminatory ability across various variables and its predictive performance were assessed through calibration plots. Additionally, the predictive model’s capacity for outcome prediction and its net benefit were evaluated using the Net Reclassification Index (NRI) and Decision Curve Analysis (DCA) curves.

Results

The final model regarded the following variables from the training cohort as independent risk factors for cervical cancer patients: age, medical insurance, pathology, HPV infection status, chemotherapy, β2-microglobulin, neutrophil-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR). The C-indices of OS for the training group were 0.769 (95% CI, 0.748–0.789) and for the testing cohort were 0.779 (95% CI, 0.751–0.808). In both the training and testing cohorts, the calibration curve for estimating the chance of survival exhibited a significant agreement between prediction by nomogram and actual observation. In the training cohort, the areas under the curve (AUC) of the receiver operating characteristic (ROC) curves for 1-year, 3-year, and 5-year OS were 0.811, 0.760, and 0.782, respectively, while in the testing cohort, they were 0.818, 0.780, and 0.778, respectively. The Net Reclassification Index (NRI) and Decision Curve Analysis (DCA) provided evidence of the model’s superior predictive ability and net benefit when compared to the FIGO Staging system.

Conclusion

The prediction methods effectively forecasted the outcomes of cervical cancer patients. Due to the model’s excellent calibration and discrimination, it provided a reliable approach for predicting patient survival, potentially supporting the implementation of individualized treatment strategies.

Introduction

Following breast cancer, colorectal cancer, and lung cancer, cervical cancer stands as the most prevalent malignant tumor in the female reproductive system and the fourth most common malignant tumor in women. Annually, an estimated 570,000 new cases are diagnosed, resulting in 311,000 attributed deaths [1]. While the incidence and mortality rates of cervical cancer have significantly declined in high-income countries, challenges persist [2]. The selection of optimal treatment is contingent upon the FIGO staging. The ability to characterize the anatomical extent of disease and discern survival outcomes is pivotal in a qualified staging system. Cervical cancer staging is an ongoing process adapting to technological advancements that enhance diagnosis and therapy [3]. In the early-stage cervical cancer, radical hysterectomy with pelvic lymph node dissection is one of the options for the treatment, whereas approximately 25% of recurrences occur in patients who received radical surgery [4]. Concurrent chemoradiotherapy is chosen for individuals with high-risk variables (lymph node metastasis, invasion of the uterus, and unclean cutting edge). Adjuvant therapy selection is based on the Sedlis criteria for patients with medium-risk characteristics, including large-diameter tumors, deep muscle invasion, and intravascular tumor thrombus [5].

Despite the well-established efficacy of concurrent chemo-radiotherapy in improving cervical cancer survival, the specific contribution of adjuvant therapy for early-stage cancer patients remains inadequately addressed. The development of personalized therapy and follow-up plans relies on accurate prognostic assessment, necessitating the creation of an effective prognostic model [6, 7]. However, the prognostic evaluation models published in the existing literature are not accurate or personalized [8,9,10]. The nomogram method comprehensively integrates and quantifies multiple prognostic risk factors, enabling computation of an individual’s prognosis score and survival probability [11]. This technique has been utilized to predict overall survival, disease-free survival, and the probability of delayed release following surgery in cases of metastatic urothelial tumors, thyroid cancer, and gynecological malignancies [12,13,14]. Numerous factors contribute to the current prognostic model’s suboptimal performance, including the radicality of surgery, patient inclusion numbers, variations in adjuvant therapy regimens, and the method of variable selection used. An ideal prediction model should encompass all independent predictors of disease outcomes. Consequently, the creation of a reliable and beneficial prognostic model is essential for assessing patients’ chances of surviving cervical cancer and guiding specific treatment strategies.

Within this analytical research, nomogram models were constructed to generate numerical probabilities and provide a clear graphical representation of clinical events. Prognostic profiles were derived and validated to predict overall survival (OS) in a cohort of 3,982 cervical cancer patients from Chongqing University Cancer Hospital. These nomograms are anticipated to be beneficial for facilitating ongoing research and aiding in clinical decision-making. In comparison to prior research utilizing nomograms for analyzing survival prognosis in cervical cancer patients, our study incorporates a larger sample size and more comprehensive patient parameters, allowing for the utilization of more extensive real-world data. Importantly, we prognosticated overall survival (OS) and internally and externally assessed our model using these methodologies: C-index, receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis (DCA). These aspects render our study more exhaustive and dependable than previous investigations. Consequently, for the accurate estimation of prognosis and advancement of treatment techniques, the development of a new, more precise predictive model is deemed imperative.

Materials and methods

Data source

This prospective cohort study is based on the tumor database platform of Chongqing University Cancer Hospital, encompassing all patients newly diagnosed with cervical cancer at the hospital since 2015. The selected patient cohort consists of individuals admitted to the hospital between January 1, 2015, and May 31, 2019. Prospective collection of demographic data (including age, gender, ethnicity, date of diagnosis, and medical insurance) and clinical data (such as FIGO staging and pathological type) was conducted. Treatment modalities encompassed surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. Additionally, laboratory indicators, comprising HPV infection status, β2-microglobulin, albumin, albumin/globulin ratio (A/G ratio), neutrophils, lymphocyte count (LYM), neutrophil-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and follow-up data, were systematically recorded. During data collection, organization, analysis, and manuscript preparation, we deleted all private data that could identify patients to ensure the protection of their privacy.

Inclusion and exclusion criteria

The inclusion criteria were as follows: (1) patients newly diagnosed with cervical cancer who underwent primary treatment at Chongqing University Cancer Hospital; (2) Comprehensive clinical data, encompassing clinical diagnosis, pathology data, treatment plans, and follow-up information, as well as the patients’ basic information and medical expense data. The exclusion criteria were as follows: (1) individuals with non-new cases of cervical cancer or those who did not receive standard therapy; (2) Lack of follow-up documentation and a history of cancer treatment. The study’s flowchart is depicted in Fig. 1. This study adhered to the Declaration of Helsinki criteria and obtained approval from the Ethical Committee of Chongqing University Cancer Hospital. All participants provided written, informed consent.

Fig. 1
figure 1

Flow diagram of study design

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Variable

Demographic factors considered in this study included age, marital status (married or unmarried), ethnicity (Han and others), and occupation (clerk/worker, self-employed/unemployed, professional and technical staff, and others). Clinical staging was categorized according to the Federation International of Gynecology and Obstetrics, with pathology data and metastasis information being the selected clinical variables. Additionally, information on therapeutic approaches, including targeted therapy, pharmaceutical interventions, radiation, and blood test markers, was collected. Demographic data, encompassing gender, race, marital status, occupation, age at diagnosis, and date of diagnosis, were also gathered. Clinical information included cancer stage, pathological type, HPV infection status, human albumin levels, lymphocyte counts, and neutrophil counts. Treatment details, such as surgical interventions, radiotherapy, and chemotherapy, were collected, and an X-tile analysis was employed to determine the cutoff point. The primary endpoint of the study was the overall survival (OS) of patients with cervical cancer, calculated from the date of the initial cervical cancer diagnosis until the patient’s death or the last scheduled follow-up appointment.

Construction of nomogram

Patients were randomized into two groups: a testing group (consisting of 1,194 observations, approximately 30% of the data) and a training group (consisting of 2,788 observations, roughly 70% of the data). The training cohort data were utilized to construct the nomogram model. To ascertain the predictive significance of each covariate as an overall survival (OS) factor, a univariate Cox regression analysis was conducted. Variables with p-values of 0.05 were selected and incorporated into a multivariate analysis to identify independent risk factors associated with OS. The nomogram was developed based on the risk scores determined by the final Cox regression model, constructed through a step-by-step procedure. In the multivariate Cox analysis, we retained variables with p <.05 and used this to construct the Nomogram model.

Model performance and validation

The performance of the nomogram model was assessed using the concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). The C-index quantified the model’s accuracy by calculating the disparity between anticipated and actual values. To evaluate the model’s performance concerning prediction and reality, a calibration curve was employed. Decision curve analysis (DCA) computed the clinical “net benefit” for one or more prediction models compared to default treatment procedures for all or no patients.

Outcomes and follow-up

The primary outcomes of interest were the probabilities of one-, three-, and five-year overall survival (OS). Overall survival was defined as the duration from the initial diagnosis to the occurrence of death or loss to follow-up. Patient prognostic data were actively assessed through phone-based follow-up, while passive follow-up involved evaluating prognoses by referencing the patients’ most recent outpatient or inpatient information within the hospital information system.

Construction and validation of clinical prediction model

The predominant method employed for prognostic evaluation in clinical investigations is currently the nomogram, an algorithmic or integrative graphical calculation procedure that incorporates clinical and biological variables. It stands as a pivotal tool for risk assessment and clinical decision-making in oncology. The nomogram utilized in this study to predict overall survival (OS) in cervical cancer patients has been developed and validated. The data were employed to construct and validate a clinical model predicting the likelihood of surviving cervical cancer. The study comprised two patient cohorts: one for training (70% of the entire population) and another for testing (30% of the entire population). The LASSO Cox regression model was employed to select variables initially in the training cohort, including the creation of dummy variables for categorical factors. Subsequently, The optimum parameter λ of LASSO regression was determined by cross-validation, and the basic variables were selected according to the principle of λ minimization. Utilizing these characteristics, a multivariate Cox proportional hazards analysis, including hazard ratios (HRs) and 95% confidence intervals (CIs), was conducted to confirm significant predictors of OS in the training cohort. Based on the results of the Cox regression model, a nomogram was generated to predict the probabilities of 1-, 3-, and 5-year OS. In the test cohort, the nomogram’s performance in predicting survival outcomes was further assessed through discrimination and calibration. The calibration curve was produced to evaluate the model’s discrimination and prediction accuracy. To determine whether its accuracy and predictive power surpassed the FIGO Staging system model, the nomogram’s Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) were computed. Furthermore, Decision Curve Analysis (DCA) was employed to assess the potential clinical utility of the prediction models.

Statistical analysis

The Pearson Chi-square test was employed for the comparison of demographic and clinical factors between the training and validation groups. Significant features were identified through LASSO regression and multivariate Cox regression analysis. The statistical analyses described above were performed using R software version 4.1.0 (Institute for Statistics and Mathematics, Vienna, Austria). Statistical significance in a two-tailed test was considered when the p-value was less than 0.05. An online tool was developed utilizing the Shiny and DynaNom packages to forecast individual and dynamic patient survival rates based on the nomogram (http://www.shinyapps.io/). SPSS software version 26.0 (IBM Corp, Armonk, NY) and R software version 4.2.1 (Institute for Statistics and Mathematics, Vienna, Austria) were employed for statistical analysis. The model creation and testing utilized R packages including ‘survival’ (version 3.3-1), ‘foreign’ (version 0.8–82), ‘rms’ (version 6.3-1), ‘timeROC’ (version 0.4), ‘rms’ (version 5.0.1), and ‘ggDCA’ (version 5.0.1). Additionally, the R packages ‘rsconnect’ (version 0.8.27) and ‘Dyn Nom’ (version 5.0.1) were utilized for the development of the cervical cancer nomogram web server. The level of statistical significance was set at 0.05 for two-sided p-values.

Result

Baseline characteristic

Characteristics of the training and testing cohorts

Cervical carcinoma patients participating in follow-up visits were randomly allocated into training (n = 2788, 70%) and testing (n = 1194, 30%) groups from the Chongqing University Cancer Hospital tumor database platform. The mean age of the patients was 52.840 ± 10.58 years. Descriptive statistics for the population are presented in Table 1. In general, a substantial proportion of individuals in the cohort were of Han ethnicity (3884, 97.54%), married (3613, 90.73%), diagnosed with Squamous carcinoma (3468, 87.09%), at Stage I (1358, 34.10%), and exhibited HPV infection (1737, 43.62%).

Table 1 Demographics and clinical features of patients
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Independent predictive variables within the training group

Independent predictive factors in the training cohort (n = 2788) were determined using Cox proportional hazards models, and the modeled results are presented in Table 2. The variables identified as significant predictors of overall survival (OS) in univariable analysis included age, medical insurance, HPV infection status, surgery, radiotherapy, chemotherapy, β2-microglobulin, neutrophil-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR). The inclusion of pathology in the model was based on clinical consensus and previous studies. Notably, age (HR: 1.02; 95% CI 1.01–1.02, p <.001), medical insurance (UEBMI vs. URBMI, HR: 0.70, CI 0.57–0.86, p =.001), pathology (other vs. Squamous carcinoma and Adenocarcinoma, HR: 2.44; 95% CI 1.60–3.70, p <.001), HPV (HR: 9.50; 95% CI 6.93–13.01, p <.001), chemotherapy (HR: 0.33; 95% CI 0.25–0.42, p <.001), β2-microglobulin (HR: 1.51; 95% CI 1.25–1.83, p <.001), NLR (HR: 1.65; 95% CI 1.33–2.06, p <.001), and PLR (HR: 1.45; 95% CI 1.17–1.79, p =.001) were identified as independent predictors in multivariable analysis (Table 2).

Table 2 Univariate and multivariate analysis for overall training cohort survival
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Variable selection

The identification of variables for the nomogram was conducted in two steps. Initially, the LASSO regression method was employed in the training cohort for feature selection. The optimal tuning parameter for LASSO regression was determined to be 0.010, corresponding to the minimum partial probability binomial deviance (Fig. 2A). Subsequently, the optimal lambda was used to select variables with nonzero coefficients (Fig. 2B).

Fig. 2
figure 2

Variable selection by LASSO COX regression model. A coefficient profile plot was created against the log(lambda) sequence (A). After confirming the optimal parameter (lambda) in the LASSO model, the partial likelihood deviance (binomial deviance) curve was plotted versus log(lambda), and dotted vertical lines were constructed based on one standard error criterion (B)

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Developing the prognostic nomogram model

To construct a nomogram model predicting 1-, 3-, and 5-year overall survival (OS) in cervical cancer patients, independent predictors identified through multivariable analysis were selected. Each variable was assigned a point score based on corresponding Cox regression coefficients. The probability of OS was then calculated by summing the assigned points and referencing the total point table. Additionally, an online calculator (https://cqcervical.shinyapps.io/cqcervical/) was developed using the nomogram to estimate long-term OS in cervical cancer patients (Fig. 3).

Fig. 3
figure 3

Nomogram for predicting 1-, 3-, and 5-year overall survival in cervical cancer patients

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Model performance and nomogram validation

The nomogram’s concordance indices (C-indices) for predicting the overall survival (OS) of cervical cancer patients were 0.769 (95% CI, 0.748–0.789) in the training cohort and 0.779 (95% CI, 0.751–0.808) in the testing cohort, indicating robust discrimination. In the testing cohort, the corresponding receiver operating characteristic (ROC) values for 1-, 3-, and 5-year OS prediction were 0.811, 0.760, and 0.782 (Fig. 4A), while in the training cohort, they were 0.818, 0.780, and 0.778 (Fig. 4B). In contrast, the ROC curves for FIGO Stage’s 1-, 3-, and 5-year overall survival prediction in the training cohort were 0.741, 0.735, and 0.752 (Fig. 4C), while in the testing cohort, they were 0.722, 0.730, and 0.729 (Fig. 4D). The calibration curves for 1-, 3-, and 5-year OS in both the training (Fig. 5A) and testing cohorts (Fig. 5B) illustrated favorable concordance between observed and predicted probabilities using the nomogram. The results underscored the model’s exceptional predictive capacity and accuracy for survival. Decision curve analysis (DCA) evaluating the nomogram’s efficacy in predicting 1-, 3-, and 5-year OS in the training and testing cohorts revealed superior net benefits and more precise clinical outcome prediction values compared to FIGO staging (Fig. 5C-D). This study demonstrated a significant enhancement in the prediction of 5-year OS in cervical cancer patients with the nomogram. Figure (5E-F) displays the calculated number of high and low-risk patients in the training and testing sets based on the model. The outcomes indicated the model’s ability to distinguish between high and low risk (p <.05).

Fig. 4
figure 4

ROC curves of the nomogram in the training cohort (A) and testing cohort (B) for the 1-, 3-, and 5-year overall survival prediction. ROC curves of the FIGO Staging for 1-, 3- and 5-year overall survival prediction in the training cohort (C) and testing cohort (D)

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Fig. 5
figure 5

The calibration curves used for predicting patient OS at 1-, 3- and 5-years for the training cohort and 1-, 3- and 5-years for the testing cohort. Decision curve analysis evaluated the ability of the nomogram to predict 5-year overall survival in cervical cancer patients in the training (C) and testing (D) cohorts. The nomogram distinguished the risk of cervical cancer patients in the training cohort (E) and the testing cohort (F)

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Discussion

Despite the advancements in treatments for cervical cancer, leading to improved curative outcomes through surgery and concurrent chemoradiotherapy, a substantial proportion of patients (20–40%) still encounter metastasis or recurrence within two years. This recurrence rate escalates to over 70% three years post-radiation therapy. Accurate prognosis is indispensable for post-therapy follow-up and patient counseling. Precise prognosis prediction in cervical cancer patients is vital for delivering tailored consultations, conveying condition notifications, formulating diagnoses, devising treatment plans, and establishing follow-up programs. Research encompassing various FIGO staging indicates that patients with stage IB, IIA, IIB, III, and IV cervical cancer exhibit local treatment failure rates of 10%, 17%, 23%, 42%, and 74%, respectively [15]. Given the limitations of the current FIGO system, the development of a new prognostic prediction tool becomes imperative [16].

Various cervical cancer prediction models are currently available, each with its advantages and disadvantages. Nomograms are increasingly employed to construct cancer prediction models, simplifying complex components into a single, straightforward numerical estimation model for event likelihood forecasting. Nomogram models in studies by Wang et al. [17] and Zhang et al. [18], utilizing the Surveillance, Epidemiology, and End Results database, demonstrated commendable prediction accuracy. However, the data used were not comprehensive and belonged to multiple institutions, and it was difficult as well to control the treatment process and standards. Huang et al. established a nomogram to predict the 3- and 5-year survival rates of patients with cervical cancer based on the FIGO staging, lymph node metastasis, and systemic immune inflammation indicators [19]. However, the model was not validated and only three factors were included, which resulted in target errors in prediction in certain extreme cases. It could prove necessary to test the model with larger samples in order to verify its representativeness. With the objective of providing patients a highly accurate and individualized prognosis, we developed a nomogram that is based on a real-world case study to estimate the survival rate of cervical cancer patients. The characteristics of single-center source data include great consistency and a standardization of treatment approaches. This eliminates differences in surgical methods and technical and personal judgment standards of surgeons and pathologists across many institutions and countries, reducing the interference of human factors on research results and emphasizing the influence of various clinical and pathological factors on prognosis.

We established a single-center cervical database of patients with stage I to IV cervical cancer who were treated at the Chongqing University Cancer Hospital between January 1, 2015, and May 31, 2019. In our retrospective study, the clinical, pathological, treatment and hematological parameters and prognosis of 3982 cervical cancer patients were investigated. A clinical prediction model with fourteen variables for the OS of cervical cancer patients was developed in our study. age, medic insurance, marital status, ethnicity, FIGO staging, pathologic type, surgery, radiotherapy, chemotherapy, HPV infection status, β2-microglobulin, A/G ratio, NLR and PLR were included in the prognostic model, which were easily accessible in clinical practice, and improved the practicability of this model. Though univariable analysis and subsequent multivariable analysis, we identified age, medic insurance, pathologic type, HPV infection status, chemotherapy, β2-microglobulin, NLR and PLR as independent prognostic factors. The model demonstrated good calibration, discrimination, and prediction accuracy for clinical outcomes and potential clinical decision-making usefulness. A number of prognostic indicators for cervical cancer have been proposed, however agreement on some, including pathological factors, had still to be realized. Squamous cell carcinoma (SCC), adenocarcinoma (AC), adenosquamous carcinoma, and other types of cervical cancer (cervical villous tubular papillary adenocarcinoma) are various subtypes of cervical cancer. The first three subtypes are the most typical, with SCC accounting approximately over 85% of cases of cervical cancer. Grigsby et al. showed that the survival rates of patients with squamous cell carcinoma and adenocarcinoma were comparable by enrolling 1239 individuals with cervical cancer [20]. In contrast with SCC and AC, adenosquamous carcinoma and other are much more probable to metastasize, and this is usually considered as a significant prognostic factor. Polterauer et colleagues developed a nomogram for OS with an C-index of 0.723, and six characteristics were chosen as nomogram covariates: FIGO staging, tumor size, age, histologic subtype, lymph node ratio, and parametrial involvement [21].

However, in our analysis, 3982 patients with invasive cervical cancer ranged in stage from I to IV were enrolled. The nomogram of our investigation was involved 2788 cervical cancer patients. Oncological outcomes from treated cervical cancer patients were evaluated using data from clinical, pathological, and hematological parameters that independently affect prognosis. However, there are some limitations. First, due to the retrospective characteristics of this study and the lack of external validation, the predictive utility of the nomogram in cervical cancer patients must be prospectively evaluated in larger, independent cohorts. To address this limitation, we plan to collaborate with multiple medical institutions in the future to acquire datasets from diverse regions, ensuring robust external validation and broader applicability of our model across various populations and healthcare settings. Secondly, the model was developed and validated using data from a single medical center, which limits its generalizability to other regions. To overcome this limitation and enhance the model’s robustness, we plan to conduct a multi-center analysis to evaluate its applicability and accuracy across a broader and more diverse population. Third, we neglected to include certain well-known predictive markers such as tumor size, lymph vascular space invasion, stromal invasion, and lymph node statue. The study’s integrity will be strengthened by include these data in the analysis in the future.

Conclusion

In conclusion, we have formulated a prognostic nomogram for overall survival (OS) in cervical cancer patients, characterized by superior discrimination and calibration. The implementation of this innovative model furnishes a straightforward and dependable tool for predicting patient survival, potentially enhancing the customization of interventions to benefit individual patients.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

We express our gratitude to the individuals and experts who contributed their support at every stage of this study.

Funding

This study did not receive any funding.

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Author notes

  1. Li Yuan has first authorship.

Authors and Affiliations

  1. Chongqing University Cancer Hospital, Chongqing, 400030, China

    Li Yuan, Baogang Wen, Xiuying Li, Dongling Zou & Qi Zhou

  2. Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China

    Haike Lei

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  1. Li Yuan
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Contributions

LY contributed to the writing of the original draft. LY conducted formal analysis. LY and HL handled data curation. QZ, HL and DZ provided supervision and managed project administration. BW and XL performed the data collection. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to Haike Lei, Dongling Zou or Qi Zhou.

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Ethics approval and consent to participate

All procedures involving human participants adhered to the ethical standards of the institution or practice where the study was conducted. We followed relevant guidelines to ensure that the study was voluntary and confidential. The authors take responsibility for all aspects of the work, including addressing any inquiries about the accuracy or integrity of any part of the work by conducting thorough investigations and resolutions.

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The authors declare no competing interests.

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Yuan, L., Wen, B., Li, X. et al. A Chinese prospective cohort research developed and validated a risk prediction model for patients with cervical cancer. Cancer Cell Int 25, 142 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03744-8

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03744-8

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Cancer Cell International

ISSN: 1475-2867

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