Evaluating Overall Survival and Competing Risks of Survival in Patients With Early Stage Breast Cancer Using a Comprehensive Nomogram

Abstract

BackgroundPatients with early stage breast cancer (BC) live long but accompany with competing comorbidities. This study aims to estimate the impact of cancer and non-cancer causes of death in early stage BC patients, and further quantify the survival differences. MethodsPatients diagnosed with breast cancer between 2010 and 2016 from the Surveillance, Epidemiology, and End Results database were enrolled in the study. Cumulative incidence function (CIF) for cause-specific death and other causes of death were estimated, and the differences were tested by Gray’s test. Nomogram for estimating 3-, 4-, and 5-year overall survival, cancer-specific survival and other-cause-specific survival was established based on Cox regression analysis and Fine and Gray’s competing risk analysis. The discriminative ability, calibration and precision of the nomogram was evaluated and compared using C statistics, calibration plots and the area under receiver operating characteristic curve. Results196304 eligible patients with early-stage BC patients were enrolled in this study. Prolonged overall survival (OS) was associated with younger age, well differentiation, smaller tumor size, Luminal subtype and presence of surgery ( p <0.001). For competing events, Fine and Gray's competing risk analysis was used to validate the predictors: i ncreasing age, poorer differentiation, larger tumor size, triple negative subtype, HER2 enriched subtype and absence of surgery for cancer-specific mortality (CSM); and increasing age, larger tumor size and absence of surgery for other-cause-specific mortality (OCSM). The established nomogram was well calibrated, and displayed good discrimination in both training cohort and validation cohort by calibration plots (Figure 4), with a concordance index of 0.801 (95% CI, 0.795-0.806; p =0.003) for OS prediction; 0.830 (95% CI, 0.824-0.836; p =0.003) for CSM prediction and 0.806 (95% CI, 0.798-0.814; p =0.004) for OCSM prediction. Furthermore, the AUC values for predicting survival and death were: OS, 80.2% (3-year), 79.5% (4-year), and 78.7% (5-year); CSM, 83.0% (3-year), 81.7% (4-year), and 80.3% (5-year); OCSM 81.3% (3-year), 80.8% (4-year), and 81.7% (5-year) (Figure 5). ConclusionsWe evaluated OS and CIF of cancer-specific death and other-cause-specific death in patients with early stage BC based on Cox regression analysis and Fine and Gray’s competing risk analysis and developed the first comprehensive prognostic nomogram.

Prognostic models for predicting overall and cancer-specific survival in hepatocellular carcinoma: A competing risk analysis

“…The Fine and Gray model based on the sub-distribution hazard is recommended to overcome this problem [5,6,8] . Several studies have analysed the independent predictive factors of related malignant diseases by utilizing competing risk analysis and established models with good predictive performance for cancer-speci c death [9][10][11][12] . However, no studies have analysed and constructed a prognostic model for cancer-speci c death from HCC using the competing risk method.…”

Prognostic models for predicting overall survival and cancer-specific mortality in hepatocellular carcinoma: A competing risk analysis

“…The Fine and Gray model based on the sub-distribution hazard has been recommended to overcome this problem [5,6,8]. Several studies have analysed the independent predictive factors that predict related malignant diseases by utilizing competing risk analysis and have established models with good predictive performance for cancer-speci c death [9][10][11][12]. However, little studies have analysed and constructed a prognostic model for cancer-speci c mortality (CSM) in patients with HCC using the competing risk method.…”