Evaluating overall survival and competing risks of survival in patients with early-stage breast cancer using a comprehensive nomogram

Relative Burden of Cancer and Noncancer Mortality Among Long-Term Survivors of Breast, Prostate, and Colorectal Cancer in the US

Importance

Improvements in cancer outcomes have led to a need to better understand long-term oncologic and nononcologic outcomes and quantify cancer-specific vs noncancer-specific mortality risks among long-term survivors.

Objective

To assess absolute and relative cancer-specific vs noncancer-specific mortality rates among long-term survivors of cancer, as well as associated risk factors.

Design, Setting, and Participants

This cohort study included 627 702 patients in the Surveillance, Epidemiology, and End Results cancer registry with breast, prostate, or colorectal cancer who received a diagnosis between January 1, 2003, and December 31, 2014, who received definitive treatment for localized disease and who were alive 5 years after their initial diagnosis (ie, long-term survivors of cancer). Statistical analysis was conducted from November 2022 to January 2023.

Main Outcomes and Measures

Survival time ratios (TRs) were calculated using accelerated failure time models, and the primary outcome of interest examined was death from index cancer vs alternative (nonindex cancer) mortality across breast, prostate, colon, and rectal cancer cohorts. Secondary outcomes included subgroup mortality in cancer-specific risk groups, categorized based on prognostic factors, and proportion of deaths due to cancer-specific vs noncancer-specific causes. Independent variables included age, sex, race and ethnicity, income, residence, stage, grade, estrogen receptor status, progesterone receptor status, prostate-specific antigen level, and Gleason score. Follow-up ended in 2019.

Results

The study included 627 702 patients (mean [SD] age, 61.1 [12.3] years; 434 848 women [69.3%]): 364 230 with breast cancer, 118 839 with prostate cancer, and 144 633 with colorectal cancer who survived 5 years or more from an initial diagnosis of early-stage cancer. Factors associated with shorter median cancer-specific survival included stage III disease for breast cancer (TR, 0.54; 95% CI, 0.53-0.55) and colorectal cancer (colon: TR, 0.60; 95% CI, 0.58-0.62; rectal: TR, 0.71; 95% CI, 0.69-0.74), as well as a Gleason score of 8 or higher for prostate cancer (TR, 0.61; 95% CI, 0.58-0.63). For all cancer cohorts, patients at low risk had at least a 3-fold higher noncancer-specific mortality compared with cancer-specific mortality at 10 years of diagnosis. Patients at high risk had a higher cumulative incidence of cancer-specific mortality than noncancer-specific mortality in all cancer cohorts except prostate.

Conclusions and Relevance

This study is the first to date to examine competing oncologic and nononcologic risks focusing on long-term adult survivors of cancer. Knowledge of the relative risks facing long-term survivors may help provide pragmatic guidance to patients and clinicians regarding the importance of ongoing primary and oncologic-focused care.

Predicting specific mortality from laryngeal cancer based on competing risk model: a retrospective analysis based on the SEER database

Background

Laryngeal carcinoma is one of the most common types of head and neck tumors. The mortality rate in patients with laryngeal cancer has not declined in recent years. Previous studies have shown that laryngeal cancer mortality is related to the extent of laryngeal cancer, the proportion of lymph node metastases, treatment modalities, and postoperative lifestyle habits. Thus, early identifying patients at high risk of laryngeal cancer-specific death is of great clinical importance. However, in the presence of competing risk, the existing survival models based on Cox proportional hazards model may be biased in estimating tumor-specific mortality. In this study, we developed and validated a nomogram based on competitive risk analysis for patients with laryngeal cancer.

Methods

We used SEER*Stat (Version 4.6.1) software to identify patients in the Surveillance, Epidemiology, and End Results (SEER) database who were diagnosed with laryngeal cancer between 2000 and 2019 as study subjects. The collected data included demographic data, the primary site of laryngeal cancer, the histological type of tumor, tumor size, and other variables. After excluding cases with missing information, the entire cohort was randomly split into a training cohort and a validation cohort at a 7:3 ratio. The training cohort was used in building the model while the validation cohort was used to validate the model. Univariate and multivariate Fine&Gray regression analyses were used to screen statistically significant variables, and the model performance was measured by establishing a consistency index, receiver operating characteristic curve (ROC), and calibration curves.

Results

After excluding cases with missing information, 3,805 patients (2,264 in the training cohort and 1,141 in the validation cohort) were included in the study and followed for a median of 16 months. A total of 411 died of laryngeal cancer, and 2,104 patients died from other causes. Among 3,805 patients, the vast majority was male (80.9%), and Caucasian (77.2%), and aged 60-80 years old (58.4%).

Conclusions

Advanced age and keratinized SCC are risk factors for laryngeal cancer-specific death. These high-risk patients should be given more attention and closer monitoring in clinical practice.

Ten-Year Probabilities of Death Due to Cancer and Cardiovascular Disease among Breast Cancer Patients Diagnosed in North-Eastern Spain

Mortality from cardiovascular disease (CVD), second tumours, and other causes is of clinical interest in the long-term follow-up of breast cancer (BC) patients. Using a cohort of BC patients (N = 6758) from the cancer registries of Girona and Tarragona (north-eastern Spain), we studied the 10-year probabilities of death due to BC, other cancers, and CVD according to stage at diagnosis and hormone receptor (HR) status. Among the non-BC causes of death (N = 720), CVD (N = 218) surpassed other cancers (N = 196). The BC cohort presented a significantly higher risk of death due to endometrial and ovarian cancers than the general population. In Stage I, HR- patients showed a 1.72-fold higher probability of all-cause death and a 6.11-fold higher probability of breast cancer death than HR+ patients. In Stages II-III, the probability of CVD death (range 3.11% to 3.86%) surpassed that of other cancers (range 0.54% to 3.11%). In Stage IV patients, the probability of death from any cancer drove the mortality risk. Promoting screening and preventive measures in BC patients are warranted, since long-term control should encompass early detection of second neoplasms, ruling out the possibility of late recurrence. In patients diagnosed in Stages II-III at an older age, surveillance for preventing late cardiotoxicity is crucial.

Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study

Background

For patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making.

Methods

A retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Results

The LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751-0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756-0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812-0.904), the CSS was 0.866 (95% CI: 0.817-0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821-0.851), 0.769 (95% CI: 0.759-0.780), and 0.750 (95% CI: 0.738-0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811-0.847), 0.769 (95% CI: 0.757-0.780), and 0.745 (95% CI: 0.732-0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging.

Conclusion

Two prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.

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.

Evaluating overall survival and competing risks of survival in patients with early-stage breast cancer using a comprehensive nomogram

BACKGROUND

Patients with early-stage breast cancer (BC) live long but have competing comorbidities. This study aimed to estimate the effect of cancer and other causes of death in patients with early-stage BC and further quantify the survival differences.

MATERIALS AND METHODS

Data of patients diagnosed with BC between 2010 and 2016 were collected from the Surveillance, Epidemiology, and End Results database. The cumulative incidence function for breast cancer-specific mortality (BCSM) and other cause-specific mortality (OCSM) was estimated, and the differences were tested using the Gray test. The nomogram for estimating 3-, 4-, and 5-year overall survival (OS), breast cancer-specific survival, and other cause-specific survival was established based on Cox regression analysis and Fine and Gray competing risk analysis. The discriminative ability, calibration, and precision of the nomogram were evaluated and compared using C statistics, calibration plots, and area under the receiver operating characteristic curve.

RESULTS

A total of 196 304 eligible patients with early-stage BC were identified in this study. Of these, 12 417 (6.3%) patients died: 5628 (45.3%) due to BC and 6789 (54.7%) due to other causes. Five validated variables were incorporated to develop the prognostic nomogram: age, grade, tumor size, subtype, and surgery of primary site (Figure 3). Age was a strong predictive factor, which was more obvious in OCSM. The effect of surgery was more prominent in BCSM. Increased tumor size was correlated with OS and BCSM and slightly correlated with OCSM. Grade and subtype differences were more predominant in BCSM than in OCSM. The established nomogram was well calibrated and displayed good discrimination.

CONCLUSIONS

We evaluate OS and competing risks of death in patients with early-stage BC, establishing the first comprehensive prognostic nomogram.