A Population-Based Research Utilized a Risk Stratification Model to Forecast the Overall Survival of Young Women With Diagnosed Stage IV Breast Cancer

The goal of this study is to develop a risk prediction model for estimating overall survival (OS) in young females diagnosed with stage IV breast cancer. The clinical information was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. To identify the dependent risk factors, we utilized the Cox proportional hazards regression model in both single and multivariate analyses. We then created a new nomogram to predict the 1-, 3-, and 5-year overall survival probability for these patients based on the identified risk factors. Six hundred seventy-six patients who met the eligibility requirements were stochastically partitioned into training (n = 475) and validation (n = 201) groups in a 7:3 ratio. Histology, breast subtype, T classification, brain metastasis, bone metastasis, liver metastasis, and surgery were identified as independent prognostic factors for cancer. To predict the 1-, 3-, and 5-year overall survival (OS) probabilities, all of these independent factors were incorporated into nomograms. Our nomogram demonstrated a favorable discriminatory power, as evidenced by a C-index of 0.737 (95% CI: 0.708-0.766) and 0.717 (95% CI: 0.664-0.770) for the training and validation cohorts, respectively. The calibration curves showed satisfactory consistency in both cohorts. Using this nomogram, we developed a risk stratification model that categorized patients into low-, intermediate-, and high-risk groups. The prediction model was more precisely to predict the OS of young females with stage IV breast cancer and could enable individualized risk estimation that could be conducive to physicians exploring therapeutic strategies for effectiveness. Few studies on stage IV breast cancer in young females were published at present, therefore, we constructed a new model to forecast these patients’ overall survival. We extracted 676 samples from the SEER database, then used Univariate and multivariate Cox analyses to determine the independent prognostic factors. The model exhibited superior predictive power then the AJCC TNM staging system.