Construction and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Nonmetastatic Inflammatory Breast Cancer Patients Receiving Tri-Modality Therapy: A Population-Based Study

BACKGROUND: As the most aggressive breast cancer, inflammatory breast cancer (IBC) has a poor prognosis. However, analyzing the prognostic factors of IBC is challenging due to its rarity. We identified the prognostic factors to establish predictive tools for survival in nonmetastatic IBC patients who received tri-modality therapy. MATERIAL/METHODS: The data of 893 nonmetastatic IBC patients were acquired from the Surveillance, Epidemiology, and End Results (SEER) database. IBC was identified by “ICD-O-3=8530” or “AJCC T, 7(th)=T4d”). Patients were randomized to the training (n=668) and validation (n=225) cohorts. Prognostic factors were identified in the training cohort. Factors in the nomogram for overall survival (OS) were filtered by the least absolute shrinkage selection operator (LASSO) regression model. Factors selected by the competing-risk models were integrated to construct nomograms for breast cancer-specific survival (BCSS). Nomogram validation was performed in both cohorts. RESULTS: The number of positive lymph nodes contributed the most to both nomograms. In the validation cohort, the C-indexes for OS and BCSS were 0.724 and 0.727, respectively. Calibration curves demonstrated acceptable agreement between predicted and actual survival. Risk scores were calculated from the nomograms and used to split patients into the low-risk and high-risk groups. Smooth hazard ratio (HR) curves and Kaplan-Meier curves showed a statistically significant difference in prognosis between the high-risk group and low-risk group (log-rank P<0.001). CONCLUSIONS: We unveiled the prognostic factors of nonmetastatic IBC and formulated nomograms to predict survival. In these models, the likelihood of individual survival can be easily calculated, which may assist clinicians in selecting treatment regimens.