Development and Validation of A Nomogram for Predicting the Overall Survival of Patients with Testicular Cancer

Abstract

Background

The purpose of this study was to develop and validate a nomogram containing multiple predictors for the survival of testicular cancer patients.

Methods

Testicular cancer patients diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were selected for this study. A random sampling method was used to divide patients into training and validation cohorts, which accounted for 30% and 70% of the total sample, respectively. The nomogram was developed using the training cohort and evaluated using the C index, calibration chart, and area under the receiver operating characteristic curve (AUC). The same method was applied to the validation cohort to verify the nomogram.

Results

Seven risk factors that affect the survival of testicular cancer patients (AJCC stage, marital status, age at diagnosis, race, SEER historic stage A, surgery status, and origin) were identified using Cox proportional hazard regression analysis. The training cohort was used to construct a nomogram. The nomogram has a higher C index (0.897) and AUC when compared with the AJCC staging system. The validation cohort was used to verify the nomogram, which has a higher C index and AUC when compared with the AJCC staging system. The results of the calibration chart of the nomogram show that the predicted survival of testicular cancer patients at 3, 5, and 10 years after diagnosis is very close to their actual survival.

Conclusions

We developed and validated a nomogram for predicting the survival rate of testicular cancer patients at 3, 5, and 10 years after diagnosis. This nomogram has better discrimination, calibration, and clinical validity than the AJCC staging system. This indicates that the nomogram can be used to predict the survival of testicular cancer patients effectively, and provide a reference for patient treatment strategies.

Background

Testicular cancer accounts for about 0.4% of all cancers.^{[1, 2]} There were about 71,000 new testicular cancer patients and 9,500 deaths globally in 2018, mostly in developed regions such as Europe, Australia, and North America.^[2] There are approximately 9,300 new testicular cancer patients in the US every year, making it the 18th most common tumor in males.^{[3, 4]} Testicular cancer is located in the male reproductive system, and is the second most common after prostate cancer.^{[5, 6]} Testicular cancer is most prevalent in males younger than 45 years, and mainly consists of germ cell tumors (more than 90% of cases).^[7–9]

Testicular cancer is a disease caused by multiple factors, such as genetics, diet, and lifestyle.^[10–12] The incidence of testicular cancer reportedly differs between ethnic groups, being highest in whites .^{[13, 14]} Cryptorchidism is also a risk factor for testicular cancer.^[15] Cryptorchidism patients have an incidence 10 times that of other patients.^[16] Testicular cancer is mainly treated through surgical removal of the tumor combined with radiotherapy and chemotherapy, with most patients having a good prognosis.^[17] However, some patients respond poorly to the treatment.

At present, the TNM staging system is often used to predict the survival outcome of testicular cancer patients.^[18] However, the TNM staging system only accounts for the impact of some tumor characteristics such as size, invasion status, and metastasis on the survival of patients, and does not consider individual differences such as sex, age, and race for its prognosis.^[19] With the discovery of additional cancer risk factors and scientific and technological developments, it is necessary to develop a new method for predicting patient survival that includes tumor characteristics, demographic, and treatment methods. A nomogram is a graphical representation of a model that combines a variety of risk factors, and has been widely used to predict the overall survival of different types of cancer patients.^{[19, 20]}

This study explored risk factors that potentially affect the prognosis of testicular cancer patients based on the Surveillance, Epidemiology, and End Results (SEER) 18 database, and developed a nomogram to predict their overall survival.

Methods

Data source

In this study, the data of testicular cancer patients between 2004 and 2015 were extracted from the SEER 18 database. The SEER database covers approximately 30% of the US population and contains a large amount of cancer research data. The SEER 18 database collected data related to various patient tumors between 1973 and 2015, including demographics, tumor characteristics, treatment methods, and survival data. Signing the SEER Research Data Agreement provided us with access to the SEER database.

Study population and inclusion criteria

The SEER*Stat software (version 8.3.6) was used to identify testicular cancer data from the SEER 18 database using its code from the International Taxonomy of Tumors (third edition). The main site codes of testicular cancer are C62.0–C62.9, and are classified according to location, including undescended testis (C62.0), descended testis (C62.1), and testis (C62.9). Testicular cancer patients with incomplete data on race, AJCC stage, and survival, as well as those younger than 20 years were excluded. We extracted 25,468 testicular cancer patients from the SEER 18 database, who were divided into a training cohort (70%) and a validation cohort (30%) using random sampling. The patient screening process is shown in eFigure 1 in the Supplement. The data collected from all patients included race, age at diagnosis, AJCC stage, tumor size, year of diagnosis, surgery status, chemotherapy status, radiotherapy status, marital status, survival time, and tumor extension.

Statistical analysis

We used univariable and multivariable Cox proportional hazard regression analyses to calculate the hazard ratio (HR) and 95% confidence interval (CI) of each risk factor for testicular cancer. The Akaike Information Criteria (AIC) was used as the selection criteria, and selected the variable with the lowest AIC in the model as the final predictor variable. Random sampling was used to divide patients into training and validation cohorts, of which the training cohort accounts for 70% of the sample. By screening the significant predictor variables, we used the training cohort to construct a nomogram and predict the survival of each patient 3, 5, and 10 years after diagnosis. The performance of the nomogram was internally compared with the C index of the AJCC staging system (sixth edition), and the accuracy of the nomogram was identified using 1,000 iterations of bootstrap resampling. We evaluated the accuracy of the 3-, 5- and 10-year survival predictions using the area under the receiver operating characteristic curve (AUC), and evaluated the performance of the nomogram using calibration graphs. The same method was applied to the validation cohort to validate the nomogram. The accuracy of the nomogram was compared with that of the AJCC staging system through their net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) values. The clinical validity of the nomogram was evaluated by decision-curve analysis (DCA). All statistical analyses were performed using R software (version 3.6.3). The significance level was set to P<0.05.

Results

Discussion

Testicular cancer is a cancer of the reproductive system that is common in young males and is the 29th most common new type of cancer in the world.^[2] There are approximately 9,300 new testicular cancer patients in the US each year, making it the 18th most common new cancer for male patients.^[4] Testicular cancers mostly consist of germ cell tumors, which often have a serious impact on health.^[21,22] Therefore, early detection and treatment are of great importance in improving therapeutic effects and prolonging survival of patients with testicular cancer.^[23] At present, the AJCC staging system is the most common tool used by clinicians in predicting the survival of cancer patients. It cannot predict the survival of the individual as it only contains the relevant characteristics of the tumor.^[24] Therefore, the development of a new method that individualizes survival predictions is of great importance for testicular cancer patients.

Due to the limitations of the AJCC staging system, nomograms have become a new method for predicting the overall survival of cancer patients in recent years.^[19] A nomogram is a predictive model that includes a variety of predictors, such as tumor and demographic characteristics, and types of therapy.^[25–27] It displays the predicted survival of patients in a graphical manner based on a complex mathematical formula. Using the nomogram, we can calculate the score of each predictor variable and its cumulative score that matches with the results list to predict the survival of each patient.^[28,29] Given that nomograms can contain diverse predictors and provide accurate predictions, they have been widely used to predict the survival of many other cancers, such as lung, breast, liver, stomach, and prostate cancer.^[30–33]

The present study constructed a nomogram for predicting the survival of testicular cancer patients. We first extracted data on 25,468 testicular cancer patients from the SEER database, and analyzed the risk factors that affect their survival using multivariable COX regression analysis. We identified the seven predictors most relevant to survival (P<0.05) based on the AIC criteria, which were AJCC stage, race, SEER historic stage A, age at diagnosis, surgery status, marital status, and origin. We next clarified the impact of these factors on the long-term survival of testicular cancer patients using multivariable COX regression analysis. Therefore, we decided to include these in the final forecast nomogram. We then constructed a nomogram for the training cohort based on the filtered predictors. The nomogram for the training cohort has a higher C index (0.898) than the AJCC staging system (0.834). The nomogram has a higher AUC than the AJCC staging system for 3, 5, and 10 years after diagnosis. According to the results of the calibration curve of the training cohort at 3, 5, and 10 years after diagnosis, we found that the nomogram’s predictions on testicular cancer survival are very close to the actual survival. This indicates that the nomogram is more accurate in predicting the survival of testicular cancer patients than the AJCC staging system.

We used the validation cohort to validate the nomogram for testicular cancer patient survival. The C index of the nomogram for the validation cohort (0.872) was similar to that for the training cohort, but higher than that of the AJCC staging system (0.797). The AUC values of the validation cohort for 3, 5, and 10 years after diagnosis are similar to the nomogram of the training cohort. This indicates that the nomogram for the validation cohort has similar testicular cancer survival predictions to the nomogram of the training cohort. Meanwhile, we constructed calibration curves for the validation cohort at 3, 5, and 10 years after diagnosis, which confirmed this conclusion. We then evaluated the clinical significance of the nomogram for predicting the survival of testicular cancer patients. We found that the nomogram had higher NRI and IDI values for 3, 5, and 10 years after diagnosis than the AJCC staging system. This indicates that the nomogram has more accurate predictions for the overall survival of testicular cancer patients. DCA is often considered to be useful for verifying the benefits and clinical validity of a model.^[19,34,35] In our research, the nomogram had better DCA results than the AJCC staging system at 3, 5, and 10 years after diagnosis. This indicates that, compared to the AJCC staging system, the nomogram is more clinically effective and accurate in predicting the survival of testicular cancer patients. In summary, the nomogram we constructed is better than the AJCC staging system at predicting the survival of testicular cancer patients, and provides a reference for patient treatment strategies.

Our study had several limitations. First, the research data comes from the SEER database which lacks some information, such as basic disease status, education level, drug treatments, religious beliefs, and family history, which may have an impact on the survival of testicular cancer patients. Second, cohort studies have inherent limitations, such as possible selection and information bias. Third, there are inherent limitations for any nomogram, such as the assumption that the data collected and analyzed are static in time, and there are no recognized reporting standards for performance.^[19] In addition, our study only included testicular cancer patients in some regions of the US, therefore external data verification needs to be added for it to be applied to other regions. Future studies should include testicular cancer patients from other countries or regions to further verify the nomogram.

Conclusions

We screened and identified seven predictors that were most relevant to the survival of testicular cancer patients, which were AJCC stage, race, SEER historic stage A, age at diagnosis, surgery status, marital status, and origin. We developed and validated a nomogram based on these predictors, and used it to predict the survival rate of testicular cancer patients at 3, 5, and 10 years after diagnosis. The nomogram we constructed has improved discrimination, calibration, and clinical validity compared to the AJCC staging system. This indicates that the nomogram can be used to predict the overall survival of testicular cancer patients accurately, and can provide a reference for patient treatment strategies.

Declarations

Ethical approval:

The SEER database is a tumor-related database developed by the National Cancer Institute of the United States, providing research data for researchers free of charge. All patients participating in the study received the ethical approval sought by the National Cancer Institute.

Consent for publication:

Consent for publication was obtained from all participants.

Availability of data and materials:

We obtained permission to access the database after signing and submitting the SEER Research Data Agreement form via email.

Data sharing:

The datasets generated and analyzed during the current study are available in the SEER database repository (https://seer.cancer.gov) .

Conflicts of Interest: None

Funding: None.

Author Contributions: 

All authors had full access to all of the data in the study.

Haohui Yu, and Jun Lyu take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Jun Lyu, Haohui Yu.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Haohui Yu, Bin Feng.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Haohui Yu, Bin Feng, Yunrui Zhang.

Administrative, technical, or material support: Jun Lyu.

Supervision: Jun Lyu.

All authors contributed to writing of the manuscript and approved the final version.

Acknowledgements:

For the completion of my research, first, I wish to express my deepest gratitude to Prof. Lyu, who has given me the most valuable suggestions and advices, and made necessary corrections. Then I am greatly indebeted to Mr Feng for his advice on the format and help with my computerization. Finally, I would like to express my thanks to my colleagues Miss Zhang, who have generously offered their help with my study.

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