Dataset Information

Evaluation and Prediction Analysis of 3- and 5-Year Relative Survival Rates of Patients with Cervical Cancer: A Model-Based Period Analysis.

ABSTRACT:

Background

Cervical cancer remains a threat to female health due to high mortality. Clarification of the long-term trend of survival rate over time and the associated risk factors would be greatly informative to improve the prognosis of cervical cancer patients.

Methods

This retrospective study was based on data extracted from the Surveillance, Epidemiology, and End Results (SEER) database of the United States. The 3-year and 5-year overall survival rates of patients with cervical cancer during 2002-2006, 2007-2011, and 2012-2016 were analyzed. Period analysis was used to assess the variation in survival rate stratified by age, race, and socioeconomic status during the 15-year study period and then predicted the relative survival rate in the following period from 2017 to 2021.

Results

During 2002-2016, the 3-year relative survival rate of cervical cancer patients increased from 73.1% to 73.5% with a high jump between 2007 and 2011. This upward trend is expected to continue to 74.3% between 2017 and 2021. Patients older than 60 years, black ethnicity, or medium and high poverty status were likely to have a lower relative survival rate.

Conclusion

This study confirmed the increased relative survival rate of cervical cancer patients over years and identified relevant risk factors. Targeted initiatives for elderly and socially underprivileged individuals may be able to mitigate inequality.

SUBMITTER: Fan X

PROVIDER: S-EPMC10898299 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation and Prediction Analysis of 3- and 5-Year Relative Survival Rates of Patients with Cervical Cancer: A Model-Based Period Analysis.

Fan Xiaodong X He Wei W Zhang Qing Q Zhang Binjie B Dong Li L Li Li L Liu Xiaochun X

Cancer control : journal of the Moffitt Cancer Center 20240101

<h4>Background</h4>Cervical cancer remains a threat to female health due to high mortality. Clarification of the long-term trend of survival rate over time and the associated risk factors would be greatly informative to improve the prognosis of cervical cancer patients.<h4>Methods</h4>This retrospective study was based on data extracted from the Surveillance, Epidemiology, and End Results (SEER) database of the United States. The 3-year and 5-year overall survival rates of patients with cervical ...[more]

PMID: 38408865

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Project description:Colorectal cancer (CRC) has high incidence and is associated with high case fatality. In France, the 5-year survival, pooled across all cancer stages at diagnosis, ranges from 57% in men to 60% in women. About one third of patients diagnosed with CRC will develop a metachronous recurrence during the following years. It is of paramount importance to accurately identify factors associated with the increased risk of progression and death, in order to develop effective follow-up and treatment strategies. However, to accurately assess the role of patients’ specific characteristics in the progression of cancer several methodological challenges need to be overcome. One difficulty, common to prognostic studies of cancer, concerns the need to separate the effects of prognostic factors on different clinical endpoints, such as disease recurrence vs recurrence-free death. Another difficulty, encountered in prognostic studies, is that the cause of death is not available or not accurately coded. Yet, some patients are likely to die of causes not related to the disease of primary interest, especially in cancers with longer survival and in those that affect older subjects. Until recently, the existing statistical methodology was not able to simultaneously, deal with both difficulties, i.e. to account for (i) possibly different effects of prognostic factors on death vs recurrence, and (ii) unknown causes of death. However, this challenge has been addressed by the recent development of the Markov relative survival model (MRS) , which extends the Markov multi-state model to incorporate relative survival modelling. Simulations demonstrate that MRS is able to accurately estimate different effects of prognostic factors on the risk of each of several events, including separate effects on disease-specific vs other causes of death. To date, the MRS had not been applied in clinical or epidemiological studies. The aim of this study was to assess the potential advantages of the new multi-state relative survival model (MRS), proposed by Huszti et al. (2012), in a prognostic cancer study. To this end, we compared the MRS results with those obtained with two more conventional analyses, based on Cox’s proportional hazards model, and the multi-state Markov model proposed by Alioum and Commenges (2001). The three models were applied to explore the impact of prognostic factors on cancer-specific mortality and recurrence, in a large population-based French registry of colorectal cancer, with up to 25 years of follow-up.

Project description:BackgroundHistorically, the Cox proportional hazard regression model has been the mainstay for survival analyses in oncologic research. The Cox proportional hazard regression model generally is used based on an assumption of linear association. However, it is likely that, in reality, there are many clinicopathologic features that exhibit a nonlinear association in biomedicine.ObjectiveThe purpose of this study was to compare the deep-learning neural network model and the Cox proportional hazard regression model in the prediction of survival in women with cervical cancer.Study designThis was a retrospective pilot study of consecutive cases of newly diagnosed stage I-IV cervical cancer from 2000-2014. A total of 40 features that included patient demographics, vital signs, laboratory test results, tumor characteristics, and treatment types were assessed for analysis and grouped into 3 feature sets. The deep-learning neural network model was compared with the Cox proportional hazard regression model and 3 other survival analysis models for progression-free survival and overall survival. Mean absolute error and concordance index were used to assess the performance of these 5 models.ResultsThere were 768 women included in the analysis. The median age was 49 years, and the majority were Hispanic (71.7%). The majority of tumors were squamous (75.3%) and stage I (48.7%). The median follow-up time was 40.2 months; there were 241 events for recurrence and progression and 170 deaths during the follow-up period. The deep-learning model showed promising results in the prediction of progression-free survival when compared with the Cox proportional hazard regression model (mean absolute error, 29.3 vs 316.2). The deep-learning model also outperformed all the other models, including the Cox proportional hazard regression model, for overall survival (mean absolute error, Cox proportional hazard regression vs deep-learning, 43.6 vs 30.7). The performance of the deep-learning model further improved when more features were included (concordance index for progression-free survival: 0.695 for 20 features, 0.787 for 36 features, and 0.795 for 40 features). There were 10 features for progression-free survival and 3 features for overall survival that demonstrated significance only in the deep-learning model, but not in the Cox proportional hazard regression model. There were no features for progression-free survival and 3 features for overall survival that demonstrated significance only in the Cox proportional hazard regression model, but not in the deep-learning model.ConclusionOur study suggests that the deep-learning neural network model may be a useful analytic tool for survival prediction in women with cervical cancer because it exhibited superior performance compared with the Cox proportional hazard regression model. This novel analytic approach may provide clinicians with meaningful survival information that potentially could be integrated into treatment decision-making and planning. Further validation studies are necessary to support this pilot study.

Dataset Information

Evaluation and Prediction Analysis of 3- and 5-Year Relative Survival Rates of Patients with Cervical Cancer: A Model-Based Period Analysis.

Background

Methods

Results

Conclusion

Publications

Evaluation and Prediction Analysis of 3- and 5-Year Relative Survival Rates of Patients with Cervical Cancer: A Model-Based Period Analysis.

Similar Datasets

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