Risk factors, prognostic factors, and nomograms for distant metastasis in patients with triple-negative breast...

Triple-negative breast cancer (TNBC) has poor prognosis, high invasiveness, and limited treatments. This study analyzed TNBC patients' clinical-pathological features, identified prognostic risk factors, and developed a predictive model to guide treatment and improve outcomes. We conducted a retrospective study on patients with TNBC from 2010 to 2015 in the surveillance, epidemiology, and end results (SEER) database. We collected tumor characteristics and treatment data from these patients. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for distant metastasis in TNBC patients, whereas univariate and multivariate Cox proportional hazards regression analyses were utilized to determine independent prognostic factors for TNBC patients with distant metastasis. On the basis of these factors, a new nomogram was constructed, and patients were categorized into high- and low-risk groups according to the nomogram scores. The prognosis of the patients was analyzed via Kaplan-Meier (K-M) survival analysis. This study analyzed 16,959 TNBC patients, with 4.3% presenting distant metastasis at diagnosis. Key risk factors included marital status, invasive lobular carcinoma, advanced T/N stage, no radiotherapy/surgery, and tumor size > 50mm. A nomogram with ROC curves (AUC: 0.892/0.907 training/validation sets) demonstrated excellent discrimination. For metastatic patients, unmarried status, no surgery/chemotherapy, and neoadjuvant therapy were independent prognostic factors (P < .001). The same results were obtained on the extended test set. Calibration curves, decision curve analysis (DCA), and K-M survival curves confirmed that both models could accurately predict the occurrence of distant metastasis and patient prognosis in patients with TNBC. We constructed 2 nomograms to predict distant metastasis risk in TNBC patients and prognosis in patients with distant metastasis. Our findings suggest that they could serve as effective predictive tools and may assist in clinical decision-making.