Development and Validation of Nomograms for Predicting Overall and Breast Cancer-Specific Survival in Young Women with Breast Cancer: A Population-Based Study

Development and Validation of Web-Based Nomograms for Predicting Cause-Specific Mortality in Surgically Resected Nonmetastatic Invasive Breast Cancer: A Population-Based Study

BACKGROUND

This study aims to build nomograms to predict overall survival (OS) and breast cancer-specific death (BCSD) in resected nonmetastatic invasive breast cancer.

PATIENTS AND METHODS

Patients extracted from surveillance, epidemiology, and end results database between 2010 and 2014 were analyzed. Through multivariate Cox regression and Fine and Gray competing risks regression, independent predictive factors were identified and integrated to build nomograms for predicting OS and BCSD. The models were validated by bootstrap resampling and an independent cohort. Additionally, the models' performance was measured by the Harrell's C-index, calibrate curve, and time-dependent receiver operating characteristic (ROC) curves.

RESULTS

In total, 110,180 cases were identified and enrolled in the analysis, with 83,450 in the training cohort and 26,730 in the validation cohort. Several independent predictive factors for OS and BCSD were identified and integrated to construct the nomograms. The C-indexes in the training cohort and validation cohort were 0.759 and 0.772 for predicting OS, and 0.857 and 0.856 for predicting BCSD, respectively. The nomogram models were well calibrated, and the time-dependent ROC curves verified the superiority of our models for clinical usefulness. Significant differences in the OS and BCSD curves were also observed when stratifying patients into several different risk groups. For convenient access, we deployed these proposed nomograms into web-based calculators.

CONCLUSIONS

We established and validated novel nomograms for individualized prediction of OS and BCSD in resected nonmetastatic invasive breast cancer. These nomograms perform better than previous models and could be easily accessed easily by clinicians.

Development and validation of a novel prognostic model for long-term overall survival in liposarcoma patients: a population-based study

Objective

To construct and validate a clinically accurate and histology-specific nomogram to predict overall survival (OS) among liposarcoma (LPS) patients.

Methods

We retrospectively screened eligible patients with LPS diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results database. We screened independent predictors for the nomogram using univariate and multivariate analyses. We then evaluated the prognostic accuracy of the nomogram by receiver operating characteristic (ROC) curve analysis and Harrell’s concordance index. The prognostic performances of the nomogram and the American Joint Committee on Cancer (AJCC) seventh edition staging system were compared using integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analyses (DCA).

Results

A novel nomogram was developed using independent prognostic variables, which exhibited excellent predictive performances for 3- and 5-year OS according to ROC curves. The C-index proved that the proposed nomogram had better prognostic accuracy for LPS than the traditional AJCC system, while the NRI, IDI, and DCA of the nomogram indicated better clinical net benefit.

Conclusions

The proposed nomogram can predict 3- and 5-year OS of LPS patients with reliable accuracy and may thus help clinicians to develop appropriate clinical therapies and counseling strategies to prolong the life expectancy of these patients.

Competing-risks nomograms for predicting cause-specific mortality in parotid-gland carcinoma: A population-based analysis

INTRODUCTION

Parotid-gland carcinoma (PGC) is a relatively rare tumor that comprises a group of heterogeneous histologic subtypes. We used the Surveillance, Epidemiology, and End Results (SEER) program database to apply a competing-risks analysis to PGC patients, and then established and validated predictive nomograms for PGC.

METHODS

Specific screening criteria were applied to identify PGC patients and extract their clinical and other characteristics from the SEER database. We used the cumulative incidence function to estimate the cumulative incidence rates of PGC-specific death (GCD) and other cause-specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C-index) were employed to validate the nomograms.

RESULTS

We finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1-, 3-, and 5-year cumulative incidence rates of GCD were 10.1%, 21.6%, and 25.7%, respectively, while those of OCD were 2.9%, 6.6%, and 9.0%. Age, race, World Health Organization histologic risk classification, differentiation grade, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, AJCC M stage, and RS (radiotherapy and surgery status) were independent predictors of GCD, while those of OCD were age, sex, marital status, AJCC T stage, AJCC M stage, and RS. These factors were integrated for constructing predictive nomograms. The results for calibration curves and the C-index suggested that the nomograms were well calibrated and had good discrimination ability.

CONCLUSION

We have used the SEER database to establish-to the best of our knowledge-the first competing-risks nomograms for predicting the 1-, 3-, and 5-year cause-specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision-making.

Construction and Validation of Nomograms Predicting Survival in Triple-Negative Breast Cancer Patients of Childbearing Age

Background

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer with poorest clinical outcomes. Patients of childbearing age have a higher probability of TNBC diagnosis, with more demands on maintenance and restoration of physical and psychosocial function. This study aimed to design effective and comprehensive nomograms to predict survival in these patients.

Methods

We used the SEER database to identify patients with TNBC aged between 18 and 45 and randomly classified these patients into a training (n=2,296) and a validation (n=2,297) cohort. Nomograms for estimating overall survival (OS) and breast cancer-specific survival (BCSS) were generated based on multivariate Cox proportional hazards models and competing-risk models in the training cohort. The performances of the nomograms were quantified in the validation cohort using calibration curves, time-dependent receiver operating characteristic (ROC) curves and Harrell’s concordance index (C-index).

Results

A total of 4,593 TNBC patients of childbearing age were enrolled. Four prognostic factors for OS and six for BCSS were identified and incorporated to construct nomograms. In the validation cohort, calibration curves showed excellent agreement between nomogram-predicted and actual survival data. The nomograms also achieved relatively high Harrell’s C-indexes and areas under the time-dependent ROC curves for estimating OS and BCSS in both training and validation cohorts.

Conclusions

Independent prognostic factors were identified, and used to develop nomograms to predict OS and BCSS in childbearing-age patients with TNBC. These models could enable individualized risk estimation and risk-adapted treatment for these patients.

Nomogram for predicting cancer-specific survival in undifferentiated pleomorphic sarcoma: A Surveillance, Epidemiology, and End Results -based study

INTRODUCTION

The purpose of this study was to construct and validate a nomogram for predicting cancer-specific survival (CSS) in undifferentiated pleomorphic sarcoma (UPS) patients at 3, 5, and 8 years after the diagnosis.

METHODS

Data for UPS patients were extracted from the SEER (Surveillance, Epidemiology, and End Results) database. The patients were randomly divided into a training cohort (70%) and a validation cohort (30%). The backward stepwise Cox regression model was used to select independent prognostic factors. All of the factors were integrated into the nomogram to predict the CSS rates in UPS patients at 3, 5, and 8 years after the diagnosis. The nomogram' s performance was then validated using multiple indicators, including the area under the time-dependent receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, decision-curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

RESULTS

This study included 2,009 UPS patients. Ten prognostic factors were identified after analysis of the Cox regression model in the training cohort, which were year of diagnosis, age, race, primary site, histological grade, T, N, M stage, surgery status, and insurance status. The nomogram was then constructed and validated internally and externally. The relatively high C-indexes and AUC values indicated that the nomogram has good discrimination ability. The calibration curves revealed that the nomogram was well calibrated. NRI and IDI values were both improved, indicating that our nomogram was superior to the AJCC (American Joint Committee on Cancer) system. DCA curves demonstrated that the nomogram was clinically useful.

CONCLUSIONS

The first nomogram for predicting the prognosis of UPS patients has been constructed and validated. Its usability and performance showed that the nomogram can be applied to clinical practice. However, further external validation is still needed.

Development and validation of a nomogram to predict the prognosis of patients with squamous cell carcinoma of the bladder

Background: The present study aimed to develop and validate a nomogram based on expanded TNM staging to predict the prognosis for patients with squamous cell carcinoma of the bladder (SCCB).

Methods: A total of 595 eligible patients with SCCB identified in the Surveillance, Epidemiology, and End Results (SEER) dataset were randomly divided into training set (n = 416) and validation set (n = 179). The likelihood ratio test was used to select potentially relevant factors for developing the nomogram. The performance of the nomogram was validated on the training and validation sets using a C-index with 95% confidence interval (95% CI) and calibration curve, and was further compared with TNM staging system.

Results: The nomogram included six factors: age, T stage, N stage, M stage, the method of surgery and tumor size. The C-indexes of the nomogram were 0.768 (0.741–0.795) and 0.717 (0.671–0.763) in the training and validation sets, respectively, which were higher than the TNM staging system with C-indexes of 0.580 (0.543–0.617) and 0.540 (0.484–0.596) in the training and validation sets, respectively. Furthermore, the decision curve analysis (DCA) proved that the nomogram provided superior clinical effectiveness.

Conclusions: We developed a nomogram that help predict individualized prognosis for patients with SCCB.

A nomogram from the SEER database for predicting the prognosis of patients with non-small cell lung cancer

OBJECTIVE

The purpose of this study was to establish and validate a nomogram to predict the prognosis in patients with non-small cell lung cancer (NSCLC) from multiple perspectives.

RESULTS

A total of 98,640 eligible patients were randomly divided into a training set (n = 69,048) and a validation set (n = 29,592). The baseline characteristics of the two sets were similar. We used clinical data from patients in the training set for univariate and multivariate Cox regression analyses. Twelve independent risk factors were incorporated for constructed a prognostic nomogram. And the nomogram with a concordance index of 0.777 (95 % CI, 0.775 to 0.779) for overall survival. The calibration curve results showed that the actual survival rate was consistent with the predicted survival rate. The area under curve of the receiver operating characteristic curves demonstrated that the nomogram has a high prediction of the overall survival rate in patients with NSCLC.

CONCLUSION

We have developed a nomogram with high prediction accuracy and discrimination ability, which can help clinicians making personalized survival predictions for NSCLC patients.

Nomograms for prediction of overall and cancer-specific survival in young breast cancer

PURPOSE

To assess the prognostic risk factors and establish prognostic nomograms based on lymph node ratio (LNR) to predict the survival of young patients with breast cancer (BC).

METHODS

Patients aged < 40 years and diagnosed with BC between 2010 and 2016 from the Surveillance, Epidemiology and End Results database were assessed. Nomograms incorporating LNR were constructed to predict overall survival (OS) and breast cancer-specific survival (BCSS) based on Cox proportional hazards model. The performance of the nomograms was assessed by C-index, calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and risk group stratification and compared with the TNM staging system.

RESULTS

Based on the univariate and multivariate Cox regression analysis, significant prognostic factors were identified and integrated to create the nomograms for OS and BCSS. The calibration curves indicated optimal agreement between model predictions and actual observations. The nomograms showed favorable sensitivity with a C-index of 0.8351 (95% CI 0.8234-0.8469) for OS and 0.8474 (95% CI 0.8355-0.8594) for BCSS. The ROC curves of the nomograms showed better predictive ability than those of the TNM staging system for OS (AUC: 0.8503 vs. 0.7819) and BSCC (AUC: 0.8607 vs. 0.8081). Significant differences in Kaplan-Meier curves were observed in patients stratified into different risk groups (p < 0.001).

CONCLUSIONS

These nomograms provided more accurate individualized risk prediction of OS and BCSS and may assist clinicians in making decisions for young patients with BC.

Genome Instability Profiles Predict Disease Outcome in a Cohort of 4,003 Patients with Breast Cancer

PURPOSE

The choice of therapy for patients with breast cancer is often based on clinicopathologic parameters, hormone receptor status, and HER2 amplification. To improve individual prognostication and tailored treatment decisions, we combined clinicopathologic prognostic data with genome instabilty profiles established by quantitative measurements of the DNA content.

EXPERIMENTAL DESIGN

We retrospectively assessed clinical data of 4,003 patients with breast cancer with a minimum postoperative follow-up period of 10 years. For the entire cohort, we established genome instability profiles. We applied statistical methods, including correlation matrices, Kaplan-Meier curves, and multivariable Cox proportional hazard models, to ascertain the potential of standard clinicopathologic data and genome instability profiles as independent predictors of disease-specific survival in distinct subgroups, defined clinically or with respect to treatment.

RESULTS

In Cox regression analyses, two parameters of the genome instability profiles, the S-phase fraction and the stemline scatter index, emerged as independent predictors in premenopausal women, outperforming all clinicopathologic parameters. In postmenopausal women, age and hormone receptor status were the predominant prognostic factors. However, by including S-phase fraction and 2.5c exceeding rate, we could improve disease outcome prediction in pT1 tumors irrespective of the lymph node status. In pT3-pT4 tumors, a higher S-phase fraction led to poorer prognosis. In patients who received adjuvant endocrine therapy, chemotherapy or radiotherapy, or a combination, the ploidy profiles improved prognostication.

CONCLUSIONS

Genome instability profiles predict disease outcome in patients with breast cancer independent of clinicopathologic parameters. This applies especially to premenopausal patients. In patients receiving adjuvant therapy, the profiles improve identification of high-risk patients.

The E3 Ubiquitin Ligase Cbl-b Predicts Favorable Prognosis in Breast Cancer

Background: Casitas B-lineage lymphoma proto-oncogene-b (Cbl-b) is an E3 ubiquitin-protein ligase and a signal-transducing adaptor protein involved in the development and progression of cancer. Despite the known functions of Cbl-b, its role in breast cancer remains unclear. The aim of this study is to explore the prognostic value of Cbl-b in breast cancer. Methods: Cbl-b expression was analyzed by immunohistochemistry in 292 breast cancer patients from the First Hospital of China Medical University between 1999 and 2008. Kaplan-Meier curve and Cox proportional hazards regression were used to analyze the independent prognostic factors for overall survival (OS) and disease-free survival (DFS). Nomogram was constructed based on these prognostic factors. Results: Cbl-b expression was detected in 54.1% (158/292) breast cancer tissue samples. Cbl-b expression was correlated with DFS (p = 0.033), but was not significantly associated with the known clinic-pathological factors in this study. Log-rank analysis indicated that Cbl-b expression was correlated with better OS (p = 0.013) and DFS (p = 0.016). Multivariate analysis showed that Cbl-b expression was an independent prognostic factor in breast cancer. The nomogram we built for predicting OS was integrated with Cbl-b expression, age, tumor size, lymph node metastasis and histological grade. Except tumor size, all the above factors and date of diagnosis were used to construct the DFS nomogram. The C-indexes of the nomograms were 0.735 and 0.678, respectively. Our new clinical model was superior to the TNM staging for prediction of OS. Conclusion: Cbl-b expression independently predicts favorable prognosis in breast cancer. Cbl-b expression, combined with other variables could be more precise clinical predictive models for predicting OS and DFS in patients with breast cancer.