Nomograms predicting the overall survival and cancer-specific survival of patients with stage IIIC1 cervical cancer

Development and validation of prognostic nomographs for patients with cervical cancer: SEER-based Asian population study

To develop and validate a nomograph to predict the long-term survival probability of cervical cancer (CC) patients in Asia, Surveillance, Epidemiology, and End Results (SEER) were used to collect information about CC patients in Asia. The patient data were randomly sampled and divided into a training group and a validation group by 7:3. Least absolute shrinkage and selection operator (LASSO) regression was used to screen key indicators, and multivariate Cox regression model was used to establish a prognostic risk prediction model for CC patients. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were adopted to comprehensively evaluate the nomogram model. LASSO regression and multivariate Cox proportional hazards model analysis showed that age, American Joint Committee on Cancer (AJCC) Stage, AJCC T, tumor size, and surgery were independent risk factors for prognosis. The ROC curve results proved that the area under curve (AUC) values of the training group in 3 and 5 years were 0.837 and 0.818, The AUC values of the validation group in 3 and 5 years were 0.796 and 0.783. DCA showed that the 3- and 5-year overall survival (OS) nomograms had good clinical potential value. The nomogram model developed in this study can effectively predict the prognosis of Asian patients with CC, and the risk stratification system based on this nomogram prediction model has some clinical value for discriminating high-risk patients.

Establishment of prediction models to predict survival among patients with cervical cancer based on socioeconomic factors: a retrospective cohort study based on the SEER Database

OBJECTIVE

To construct and validate predictive models based on socioeconomic factors for predicting overall survival (OS) in cervical cancer and compare them with the American Joint Council on Cancer (AJCC) staging system.

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

We extracted data from 5954 patients who were diagnosed with cervical cancer between 2007 and 2011 from the Surveillance, Epidemiology, and End Results Database. This database holds data related to cancer incidence from 18 population-based cancer registries in the USA.

OUTCOME MEASURES

1-year and 5-year OS.

RESULTS

Of the total 5954 patients, 5820 patients had 1-year mortality and 5460 patients had 5-year mortality. Lower local education level [Hazard ratios (HR): 1.15, 95% confidence interval (CI): 1.04 to 1.27, p= 0.005] and being widowed (HR 1.28, 95% CI 1.06 to 1.55, p=0.009) were associated with a worse OS for patients with cervical cancer. Having insurance (HR 0.75, 95% CI 0.62 to 0.90, p=0.002), earning a local median annual income of ≥US$56 270 (HR 0.83, 95% CI 0.75 to 0.92, p<0.001) and being married (HR 0.79, 95% CI 0.69 to 0.89, p<0.001) were related to better OS in patients with cervical cancer. The predictive models based on socioeconomic factors and the AJCC staging system had a favourable performance for predicting OS in cervical cancer compared with the AJCC staging system alone.

CONCLUSION

Our proposed predictive models exhibit superior predictive performance, which may highlight the potential clinical application of incorporating socioeconomic factors in predicting OS in cervical cancer.

The distribution pattern of pelvic lymph nodal metastases in cervical cancer

PURPOSE

Depiction of pelvic lymph node metastasis (LNM) sites among patients with cervical cancer facilitates accurate determination of the extent of dissection and radiotherapy regimens.

METHODS

A retrospective study of 1182 cervical cancer patients who underwent radical hysterectomy and pelvic lymph node dissection between 2008 and 2018 was performed. The number of removed pelvic lymph nodes and metastasis status in different anatomical regions was analyzed. The prognostic difference of patients with lymph node involvement stratified by various factors was analyzed by Kaplan-Meier method.

RESULTS

The median number of pelvic lymph nodes detected was 22, mainly from obturator (29.54%) and inguinal (21.14%) sites. Metastatic pelvic lymph nodes were found in 192 patients, with obturator accounting for the highest percentage (42.86%). The patients with lymph node involvement in single site had better prognosis that those in multiple sites. The overall- (P = 0.021) (OS) and progression-free (P < 0.001) survival (PFS) curves of patients with inguinal lymph node metastases were worse compared to those with obturator site. There was no difference in the OS and PFS among patients with 2 and more than 2 lymph nodes involvement.

CONCLUSION

An explicit map of LNM in patients with cervical cancer was presented in this study. Obturator lymph nodes tended to be involved. The prognosis of patients with inguinal lymph node involvement was poor in contrast to that with obturator LNM. In patients with inguinal lymph node metastases, clinical staging needs to be reconsidered and extended radiotherapy to the inguinal region needs to be strengthened.

A prognostic model using FIGO 2018 staging and MRI-derived tumor volume to predict long-term outcomes in patients with uterine cervical squamous cell carcinoma who received definitive radiotherapy

BACKGROUND

Uterine cervical carcinoma is a severe health threat worldwide, especially in China. The International Federation of Gynecology and Obstetrics (FIGO) has revised the staging system, emphasizing the strength of magnetic resonance imaging (MRI). We aimed to investigate long-term prognostic factors for FIGO 2018 stage II-IIIC2r uterine cervical squamous cell carcinoma following definitive radiotherapy and establish a prognostic model using MRI-derived tumor volume.

METHODS

Patients were restaged according to the FIGO 2018 staging system and randomly grouped into training and validation cohorts (7:3 ratio). Optimal cutoff values of squamous cell carcinoma antigen (SCC-Ag) and tumor volume derived from MRI were generated for the training cohort. A nomogram was constructed based on overall survival (OS) predictors, which were selected using univariate and multivariate analyses. The performance of the nomogram was validated and compared with the FIGO 2018 staging system. Risk stratification cutoff points were generated, and survival curves of low-risk and high-risk groups were compared.

RESULTS

We enrolled 396 patients (training set, 277; validation set, 119). The SCC-Ag and MRI-derived tumor volume cutoff values were 11.5 ng/mL and 28.85 cm³, respectively. A nomogram was established based on significant prognostic factors, including SCC-Ag, poor differentiation, tumor volume, chemotherapy, and FIGO 2018 stage. Decision curve analysis indicated that the net benefits of our model were higher. The high-risk group had significantly shorter OS than the low-risk group in both the training (p < 0.0001) and validation sets (p = 0.00055).

CONCLUSIONS

Our nomogram predicted long-term outcomes of patients with FIGO 2018 stage II-IIIC2r uterine cervical squamous cell carcinoma. This tool can assist gynecologic oncologists and patients in treatment planning and prognosis.

A Bayesian network predicting survival of cervical cancer patients-Based on surveillance, epidemiology, and end results

This study aimed to build a comprehensive model for predicting the overall survival (OS) of cervical cancer patients who received standard treatments and to build a series of new stages based on the International Federation of Gynecologists and Obstetricians (FIGO) stages for better such predictions. We collected the cervical cancer patients diagnosed since the year 2000 from the Surveillance, Epidemiology, and End Results (SEER) database. Cervical cancer patients who received radiotherapy or surgery were included. Log-rank tests and Cox regression were used to identify potential factors of OS. Bayesian networks (BNs) were built to predict 3- and 5-year survival. We also grouped the patients into new stages by clustering their 5-year survival probabilities based on FIGO stage, age, and tumor differentiation. Cox regression suggested black ethnicity, adenocarcinoma, and single status as risks for poorer prognosis, in addition to age and stage. A total of 43,749 and 39,333 cases were finally eligible for the 3- and 5-year BNs, respectively, with 11 variables included. Cluster analysis and Kaplan-Meier curves indicated that it was best to divide the patients into nine modified stages. The BNs had excellent performance, with area under the curve and maximum accuracy of 0.855 and 0.804 for 3-year survival, and 0.851 and 0.787 for 5-year survival, respectively. Thus, BNs are excellent candidates for predicting cervical cancer survival. It is necessary to consider age and tumor differentiation when estimating the prognosis of cervical cancer using FIGO stages.

Novel prognostic nomograms in cervical cancer based on analysis of 1075 patients

OBJECTIVE

To explore the factors affecting the prognosis of cervical cancer (CC), and to construct and evaluate predictive nomograms to guide individualized clinical treatment.

METHODS

The clinicopathological and follow-up data of CC patients from June 2013 to December 2019 in Sun Yat-sen Memorial Hospital of Sun Yat-sen University were retrospectively analyzed. Log-rank test was used for univariate survival analysis, and Cox multivariate regression was used to identify independent prognostic factors, based on which nomogram models were established and evaluated in multiple aspects.

RESULTS

Patients were randomly assigned into the training (n = 746) and validation sets (n = 329). Survival analysis of the training set identified cervical myometrial invasion, parametrial involvement, and malignant tumor history as prognosticators of postoperative DFS and pathological type, cervical myometrial invasion, and history of STD for OS. C-index was 0.799 and 0.839 for the nomograms for DFS and OS, respectively. Calibration curves and Brier scores also indicated high performance. Importantly, decision curve analysis suggested great clinical applicability of these nomograms.

CONCLUSIONS

In this study, we analyzed a cohort of 1075 CC patients and identified DFS- or OS-associated clinicohistologic characteristics. Two nomograms were subsequently constructed for DFS and OS prognostication, respectively, and showed high performance in terms of discrimination, calibration, and clinical applicability. These models may facilitate individualized treatment and patient selection for clinical trials. Future investigations with larger cohorts and prospective designs are warranted for validating these prognostic models.

Dynamic Nomogram Based on the Metastatic Number and Sites and Therapy Strategies Predicting the Prognosis of Patients with Metastatic Cervical Cancer

Background

Individual survival prediction is of vital importance to optimize the individualized treatment of metastatic cervical cancer (mCC) patients. The goal of this study was to identify the potential risk factors for the survival of mCC patients and construct a nomogram for their prognosis.

Methods

Medical records of patients with newly diagnosed mCC at the First Affiliated Hospital of Xi'an Jiaotong University were reviewed retrospectively. Risk factors were identified using Cox proportional hazards analysis and Kaplan-Meier curves. Random forest was used to identify factors associated with therapy strategy. Nomogram and dynamic nomogram were established using 'rms' and "DynNom" R package.

Results

A total of 98 patients with mCC were finally identified. In Cox analyses, multiple metastases and concurrent chemoradiotherapy (CCRT) were identified as independent predictors for overall survival (OS). We further explored the prognostic value of metastatic number and sites and therapy strategies for mCC patients by Kaplan-Meier curves. A dynamic nomogram including metastases number and sites (multiple metastases, liver and lymph node (LN) above diaphragm metastases) and chemoradiotherapy strategies (CCRT, postradiotherapy chemotherapy, and radiotherapy to metastatic sites) was constructed for predicting the prognosis of mCC patients. For newly diagnosed patients, we strongly recommended the combination of chemotherapy and definitive pelvic radiotherapy and, if possible, radiation to metastatic site, but CCRT should be implemented with caution. We constructed a dynamic nomogram indicating that patients with younger age, shorter symptom duration, and better laboratory test results are suitable for CCRT.

Conclusion

Survival analyses showed that the metastatic number and sites and therapy strategies are associated with the prognosis of mCC patients. The CCRT and prognostic nomograms may help clinicians to make better clinical decisions and effectively predict the prognosis for newly diagnosed mCC patients.

Multi-Omics Approaches Identify Necroptosis‑Related Prognostic Signature and Associated Regulatory Axis in Cervical Cancer

Background

Cervical cancer is the fourth most frequent malignancy among women globally, with approximately 604,000 new cases and 341,000 deaths per year. Necroptosis is a newly discovered mechanism of cell death involved in biological behaviors of cancer.

Methods

LASSO Cox regression analysis was conducted to construct a prognostic necroptosis-related signature. lncRNA–miRNA–mRNA regulatory axis was constructed with a ceRNA network. qRT-PCR was performed to verify our result.

Results

A total of 54 necroptosis-related genes were differentially expressed in cervical cancer (all p < 0.05). We also summarized genetic mutation landscape of necroptosis-related genes in cervical cancer. We then developed a necroptosis-related prognostic signature including 13 necroptosis-related genes (ATRX, AXL, DDX58, IDH1, ITPK1, MAP3K7, SLC39A7, TARDBP, TNF, TNFRSF1A, TNFRSF1B, TNFSF10, TRIM11) for cervical cancer. Cervical cancer patients with high riskscore had a poor overall survival (HR = 2.128, p = 0.00194) with an AUC of 0.725, 0.763 and 0.637 in 3-year, 5-year, and 10-year ROC curve. Consensus clustering analysis revealed that all cervical cancer cohort could be divided into three subtypes, which was correlated with different prognosis and immune infiltration (p < 0.05). A PPI network revealed TNF as the hub gene and TNF expression was correlated with immune infiltration (all p < 0.05), microsatellite instability (p < 0.012) and drug sensitivity (p < 0.05). The ceRNA network was performed and identified a lncRNA NUTM2B-AS1/miR-361-5p/TNF regulatory axis for cervical cancer. qRT-PCR result also suggested that TNF was upregulated in cervical cancer (p < 0.001) and associated with a poor overall survival (p = 0.007). Univariate and multivariate analysis demonstrated TNF expression, lymph node metastasis and clinical stage were prognosis factors of cervical cancer patients (p < 0.05).

Conclusion

We developed a necroptosis-related prognostic signature including 13 necroptosis-related genes for cervical cancer. Moreover, we also identified a lncRNA NUTM2B-AS1/miR-361-5p/TNF regulatory axis, which may play a vital role in the progression of cervical cancer. Further studies should be conducted to verify these results.

Increased prognostic value of clinical–reproductive model in Chinese female patients with esophageal squamous cell carcinoma

BACKGROUND

In China, it has been well recognized that some female patients with esophageal squamous cell carcinoma (ESCC) have different overall survival (OS) time, even with the same tumor-node-metastasis (TNM) stage, challenging the prognostic value of the TNM system alone. An effective predictive model is needed to accurately evaluate the prognosis of female ESCC patients.

AIM

To construct a novel prognostic model with clinical and reproductive data for Chinese female patients with ESCC, and to assess the incremental prognostic value of the full model compared with the clinical model and TNM stage.

METHODS

A new prognostic nomogram incorporating clinical and reproductive features was constructed based on univariatie and Cox proportional hazards survival analysis from a training cohort (n = 175). The results were recognized using the internal (n = 111) and independent external (n = 85) validation cohorts. The capability of the clinical–reproductive model was evaluated by Harrell’s concordance index (C-index), Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC), calibration curve and decision curve analysis. The correlations between estrogen response and immune-related pathways and some gene markers of immune cells were analyzed using the TIMER 2.0 database.

RESULTS

A clinical–reproductive model including incidence area, age, tumor differentiation, lymph node metastasis (N) stage, estrogen receptor alpha (ESR1) and beta (ESR2) expression, menopausal age, and pregnancy number was constructed to predict OS in female ESCC patients. Compared to the clinical model and TNM stage, the time-dependent ROC and C-index of the clinical–reproductive model showed a good discriminative ability for predicting 1-, 3-, and 5-years OS in the primary training, internal and external validation sets. Based on the optimal cut-off value of total prognostic scores, patients were classified into high- and low-risk groups with significantly different OS. The estrogen response was significantly associated with p53 and apoptosis pathways in esophageal cancer.

CONCLUSION

The clinical–reproductive prognostic nomogram has an incremental prognostic value compared with the clinical model and TNM stage in predicting OS in Chinese female ESCC patients.

Development and Validation of Novel Nomograms to Predict the Overall Survival and Cancer-Specific Survival of Cervical Cancer Patients With Lymph Node Metastasis

Objective

The objective of this study was to establish and validate novel individualized nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in cervical cancer patients with lymph node metastasis.

Methods

A total of 2,956 cervical cancer patients diagnosed with lymph node metastasis (American Joint Committee on Cancer, AJCC N stage=N1) between 2000 and 2018 were included in this study. Univariate and multivariate Cox regression models were applied to identify independent prognostic predictors, and the nomograms were established to predict the OS and CSS. The concordance index (C-index), calibration curves, and receiver operating characteristic (ROC) curves were applied to estimate the precision and discriminability of the nomograms. Decision-curve analysis (DCA) was used to assess the clinical utility of the nomograms.

Results

Tumor size, log odds of positive lymph nodes (LODDS), radiotherapy, surgery, T stage, histology, and grade resulted as significant independent predictors both for OS and CSS. The C-index value of the prognostic nomogram for predicting OS was 0.788 (95% CI, 0.762–0.814) and 0.777 (95% CI, 0.758–0.796) in the training and validation cohorts, respectively. Meanwhile, the C-index value of the prognostic nomogram for predicting CSS was 0.792 (95% CI, 0.767–0.817) and 0.781 (95% CI, 0.764–0.798) in the training and validation cohorts, respectively. The calibration curves for the nomograms revealed gratifying consistency between predictions and actual observations for both 3- and 5-year OS and CSS. The 3- and 5-year area under the curves (AUCs) for the nomogram of OS and CSS ranged from 0.781 to 0.828. Finally, the DCA curves emerged as robust positive net benefits across a wide scale of threshold probabilities.

Conclusion

We have successfully constructed nomograms that could predict 3- and 5-year OS and CSS of cervical cancer patients with lymph node metastasis and may assist clinicians in decision-making and personalized treatment planning.

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.