A novel prognostic nomogram utilizing the 2018 FIGO staging system for cervical cancer: A large multicenter study

Stage migration and survival outcomes in patients with cervical cancer at Stage IIIC according to the 2018 FIGO staging system: a systematic review and meta-analysis

Objective

To summarize stage migration and survival outcomes in patients with cervical cancer at Stage IIIC according to the 2018 FIGO staging system, and to investigate prognostic factors influencing Stage IIIC1.

Methods

PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), International Clinical Trials Registry Platform (ICTRP), and Clinical Trials.gov were searched from inception to February 6, 2024. The analysis was conducted using STATA 16.0.

Results

A total of 25 studies with 82954 cervical cancer patients were included in the analysis. The migration rates to FIGO 2018 Stage IIIC ranged from 18% to 37% for early-stage tumors (Stage IB to IIA) in FIGO 2009, and from 32% to 52% for advanced stage tumors (Stage IIB to IIIB). The overall survival (OS) for Stage IIIC1 is poorer compared to Stage IB1 (HR 0.53, 95% CI 0.35-0.80, p=0.003) and Stage IB2 (HR 0.61, 95% CI 0.43-0.85, p=0.004). It is comparable to Stage IB3, yet it shows better survival outcomes than Stages IIB (HR 2.91, 95% CI 1.01-8.39, p=0.047), IIIA (HR 1.96, 95% CI 1.78-2.17, p=0.000), and IIIB (HR 1.56, 95% CI 1.04-2.35, p=0.031). Tumors size ≥4cm (HR 1.45, 95% CI 1.10-1.92, p=0.00), metastatic lymph node ≥ 3 (HR 2.21, 95% CI 1.56-3.15, p=0.000) and T stage are prognostic factors for OS of Stage IIIC1.

Conclusions

The migration rates to FIGO 2018 Stage IIIC varied between 18% and 52% for patients initially classified under FIGO 2009 Stages IB1 to IIIB. The FIGO 2018 staging system underscores the pivotal role of lymph node metastasis in predicting prognosis and provides valuable insights into the distinct prognostic implications associated with different stages, particularly for early stages. For advanced stages, incorporation of tumor-related factors such as T stage might better elucidate survival differences and guide clinical treatment decisions.

Protocol registration

CRD 42023451793.

Rationality of FIGO 2018 IIIC restaging of cervical cancer according to local tumor size: A cohort study

INTRODUCTION

FIGO 2018 IIIC remains controversial for the heterogeneity of its prognoses. To ensure a better management of cervical cancer patients in Stage IIIC, a revision of the FIGO IIIC version classification is required according to local tumor size.

MATERIAL AND METHODS

We retrospectively enrolled cervical cancer patients of FIGO 2018 Stages I-IIIC who had undergone radical surgery or chemoradiotherapy. Based on the tumor factors from the Tumor Node Metastasis staging system, IIIC cases were divided into IIIC-T1, IIIC-T2a, IIIC-T2b, and IIIC-(T3a+T3b). Oncologcial outcomes of all stages were compared.

RESULTS

A total of 63 926 cervical cancer cases were identified, among which 9452 fulfilled the inclusion criteria and were included in this study. Kaplan-Meier pairwise analysis showed that: the oncology outcomes of I and IIA were significantly better than of IIB, IIIA+IIIB, and IIIC; the oncology outcome of IIIC-(T1-T2b) was significantly better than of IIIA+IIIB and IIIC-(T3a+T3b); no significant difference was noted between IIB and IIIC-(T1-T2b), or IIIC-(T3a+T3b) and IIIA+IIIB. Multivariate analysis indicated that, compared with IIIC-T1, Stages T2a, T2b, IIIA+IIIB and IIIC-(T3a+T3b) were associated with a higher risk of death and recurrence/death. There was no significant difference in the risk of death or recurrence/death between patients with IIIC-(T1-T2b) and IIB. Also, compared with IIB, IIIC-(T3a+T3b) was associated with a higher risk of death and recurrence/death. No significant differences in the risk of death and recurrence/death were noted between IIIC-(T3a+T3b) and IIIA+IIIB.

CONCLUSIONS

In terms of oncology outcomes of the study, FIGO 2018 Stage IIIC of cervical cancer is unreasonable. Stages IIIC-T1, T2a, and T2b may be integrated as IIC, and it might be unnecessary for T3a/T3b cases to be subdivided by lymph node status.

Nomogram models for the prognosis of cervical cancer: A SEER-based study

Background

Cervical cancer (CC) is one of the most common cancers in women. This study aimed to investigate the clinical and non-clinical features that may affect the prognosis of patients with CC and to develop accurate prognostic models with respect to overall survival (OS) and cancer-specific survival (CSS).

Methods

We identified 11,148 patients with CC from the SEER (Surveillance, Epidemiology, and End Results) database from 2010 to 2016. Univariate and multivariate Cox regression models were used to identify potential predictors of patients’ survival outcomes (OS and CSS). We selected meaningful independent parameters and developed nomogram models for 1-, 3-, and 5-year OS and CSS via R tools. Model performance was evaluated by C-index and receiver operating characteristic curve. Furthermore, calibration curves were plotted to compare the predictions of nomograms with observed outcomes, and decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical effectiveness of the nomograms.

Results

All eligible patients (n=11148) were randomized at a 7:3 ratio into training (n=7803) and validation (n=3345) groups. Ten variables were identified as common independent predictors of OS and CSS: insurance status, grade, histology, chemotherapy, metastasis number, tumor size, regional nodes examined, International Federation of Obstetrics and Gynecology stage, lymph vascular space invasion (LVSI), and radiation. The C-index values for OS (0.831 and 0.824) and CSS (0.844 and 0.841) in the training cohorts and validation cohorts, respectively, indicated excellent discrimination performance of the nomograms. The internal and external calibration plots indicated excellent agreement between nomogram prediction and actual survival, and the DCA and CICs reflected favorable potential clinical effects.

Conclusions

We constructed nomograms that could predict 1-, 3-, and 5-year OS and CSS in patients with CC. These tools showed near-perfect accuracy and clinical utility; thus, they could lead to better patient counseling and personalized and tailored treatment to improve clinical prognosis.

Predicting the Recurrence of Operable Cervical Cancer Patients Based on Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score and Classical Clinicopathological Parameters

Objective

The purpose of this study was to evaluate the prognostic value of hemoglobin, albumin, lymphocyte, and platelet (HALP) score in patients with operable cervical cancer, and on this basis, combined with classical clinicopathological parameters to predict the recurrence of patients.

Methods

A total of 1580 patients with stage IA-IIA cervical cancer were randomly divided into training cohort (n=1054) and validation cohort (n=526) according to the predefined ratio of 2:1. In the training cohort, the receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of HALP score for predicting cervical cancer recurrence. On this basis, the independent related factors with cervical cancer recurrence were screened through univariate and multivariate Cox regression analysis, and then a nomogram model was further established. The internal and external validation of the model was carried out in the training cohort and the validation cohort respectively through the consistency index (C-index) and calibration curve.

Results

ROC curve and Youden index showed that the optimal threshold of HALP score for predicting cervical cancer recurrence was 39.50. Multivariate analysis confirmed that HALP score and some other classic clinicopathological parameters were independently associated with cervical cancer recurrence. Based on the results of multivariate analysis, a nomogram model for predicting cervical cancer recurrence was successfully constructed. The internal and external calibration curves showed that the fitting degree of the model was good, and the C-index (the C-index of the training cohort and the validation cohort were 0.862 and 0.847, respectively) showed that the prediction accuracy of the model proposed in this study was better than other similar models.

Conclusion

HALP score may be a novel predictor for predicting the cervical cancer recurrence. Nomogram model based on HALP score and classical clinicopathological parameters can better predict the recurrence of cervical cancer.

Nomograms Predicting Survival of Cervical Cancer Patients Treated With Concurrent Chemoradiotherapy Based on the 2018 FIGO Staging System

Background

In 2018, a revised staging system was released for cervical cancer, which defined pelvic and paraaortic lymph node metastasis as stages IIIC1 and IIIC2, respectively. In this study, we constructed and validated nomograms to predict the 3- and 5-year survival of patients with cervical cancer based on the revised International Federation of Gynecology and Obstetrics (FIGO) staging system.

Methods

We retrospectively examined patients with 2009 FIGO stage IB–IVA cervical cancer who were treated at our institute between 2011 and 2015. Patients were randomized into the model development and validation cohorts (2:1). Univariate and multivariate analyses were conducted for the model development cohort to identify prognostic factors. In the multivariate analysis, nomograms were built to predict overall survival (OS) and disease-free survival (DFS) using significant variables. The nomograms were assessed based on the discrimination and calibration in both cohorts. Discrimination was assessed using the concordance index. Calibration was performed by comparing the mean nomogram estimated survival and the mean observed survival.

Results

We included 1,192 patients, with 795 and 397 patients in the model development and validation cohorts, respectively. In the model development cohort, the median follow-up period was 49.2 months. After multivariate analysis, age, histology, 2018 FIGO stage, and pelvic lymph node number were independent factors for OS. Histology, 2018 FIGO stage, squamous cell carcinoma antigen, and pelvic lymph node number were significant predictors of DFS. The nomograms constructed to predict OS and DFS were based on these factors. In both model cohorts, the concordance index for the nomogram-predicted OS and DFS was 0.78 and 0.75 and 0.74 and 0.67, respectively. The calibration curve revealed good agreement between the nomogram predictions and actual values.

Conclusion

We constructed robust nomograms to predict the OS and DFS of patients with cervical cancer undergoing treatment with concurrent chemoradiotherapy based on the 2018 FIGO staging system.

A Research Study to Measure the Efficacy of Terminating Cervical Cancer via Customized Optimum Pathway

Background

To develop a precise prognostic model of overall survival in patients with terminating cervical cancer based on surveillance, epidemiology, and end results (SEER) program.

Methods

The patients were retrieved from SEER data who are diagnosed with terminating cervical cancer from 2004 to 2016. The data were performed using univariate and multivariate analyses and constructed nomograms for predicting survival. Use C-index to validate the model accuracy.

Results

Totally 15839 patients diagnosed with cervical cancer were independently allocated into the training set (n = 11088) and validation set (n = 4751). The multivariate analysis results indicated that age, race, stage_T, stage_M, and stage_N were confirmed as independent risk predictors, and those factors are applied to construct this clinical model. The C-index of overall survival in the training set was 0.6816 (95% confidence intervene (CI), 0.694-0.763) and that in the validation set was 0.6931(95% CI, 0.613-0.779). All calibration curves of various factors were consistent with predicted and actual survival.

Conclusion

The nomogram provides a novel method for predicting the survival of patients with terminating cervical cancer, assisting in accurate therapeutic methods for patients with primary terminating cervical cancer.

Nomogram Predicting Overall Survival in Patients with FIGO II to III Squamous Cell Cervical Carcinoma Under Radical Radiotherapy: A Retrospective Analysis Based on 2018 FIGO Staging

Purpose

To present a nomogram to predict overall survival in patients with FIGO-2018 II to III squamous cell cervical carcinoma undergoing radical radiotherapy.

Patients and Methods

Patients diagnosed with FIGO-2018 II to III squamous cell cervical cancer between December 2013 and December 2014 were analyzed retrospectively. The optimal cutoff point for tumor length and width were determined by R package. We identified prognostic factors by univariate and multivariate Cox proportional-hazard regression, then built a nomogram to visualize the prediction model. Our model was compared to the 2018 FIGO staging prediction model. Harrell’s concordance index, receiver operating characteristic curve, calibration plot were used to evaluate the discriminability and accuracy of the predictive models, and decision curve analysis (DCA) was used to show the net benefits.

Results

Data from 469 patients were included in the final analyses. The cutoff values of tumor length and width were 5.10 cm and 4.13 cm, respectively. Four independent prognostic variables—tumor length, tumor width, lower one-third vaginal involvement, and lymph node metastases—were used to establish the nomogram. The C-index of the nomogram was 0.71 (95%, CI = 0.66–0.77), which was better than that of the 2018 FIGO stage prediction model (C-index: 0.62, 95% CI = 0.58–0.66, p = 0.009). The calibration plot of the nomogram was a good fit for both 3-year and 5-year overall survival predictions. And DCA curves showed that net benefits for our model were higher than FIGO-2018 staging system.

Conclusion

A clinically useful nomogram for calculating overall survival probability in FIGO-2018 II to III squamous cell cervical cancer patients who had received radical radiotherapy was developed. Tumor length, tumor width, lower one-third vaginal involvement, and lymph node metastases were found to be independent prognostic factors. Our model performed better than the 2018 FIGO staging model. The findings could help clinicians in China to predict the survival of these patients in clinical care and research.

Diagnostic Value of Combined Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging with Diffusion Tensor Imaging in Predicting Parametrial Infiltration in Cervical Cancer

Objective

This study sought to determine the diagnostic value of combined intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) in predicting parametrial infiltration (PMI) in patients with cervical cancer.

Materials and Methods

We enrolled 65 patients with cervical cancer confirmed by radical hysterectomy (25 PMI-negative and 40 PMI-positive) who underwent IVIM and DTI pretreatment. The parameters of IVIM (ADC, D, D  ^∗ , and f) and DTI (average diffusion coefficient (DCavg) and fractional anisotropy (FA)) were recorded by two observers. All parameter differences were tested, and the receiver operating characteristic (ROC) curves were generated to estimate the diagnostic performance of significant metrics and their combinations.

Results

Compared to the PMI-negative group, the PMI-positive group had significantly lower D (0.632 ± 0.017 vs. 0.773 ± 0.024, p < 0.001) and lower FA (0.073 ± 0.002 vs. 0.085 ± 0.003, p=0.003). The area under the ROC curve (AUC) of D and FA was 0.801 and 0.726, respectively, and the combination of D and FA improved the AUC to 0.931, with a sensitivity and specificity of 80.0% and 97.5%, respectively.

Conclusion

D and FA values could be used to help diagnose PMI in patients with cervical cancer. The combination of IVIM and DTI was more valuable than either option alone.