Nomogram for predicting cancer specific survival in inflammatory breast carcinoma: a SEER population-based study

用于预测炎性乳腺癌癌症特异性生存率的列线图:一项基于SEER人群的研究

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Abstract

The clinicopathological features of inflammatory breast carcinoma (IBC), the effect of therapeutic options on survival outcome and the identification of prognostic factors were investigated in this study. Information on IBC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Cox proportional hazard regression was used to determine potential significant prognostic factors of IBC. A nomogram was then constructed to evaluate patient survival based on certain variables. Univariate and multivariate analyses revealed that race (p < 0.001), M stage (p < 0.001), surgery (p = 0.010), chemotherapy (CT) (p < 0.001), tumor size (p = 0.010), estrogen receptor (p < 0.001), progesterone receptor (p = 0.04), and human epidermal growth factor receptor 2 (p < 0.001) were all independent risk factors. The concordance index (C-index) of the nomogram was 0.735, which showed good predictive efficiency. Survival analysis indicated that IBC patients without CT had poorer survival than those with CT (p < 0.001). Stratified analyses showed that modified radical mastectomy (MRM) had significant survival advantages over non-MRM in patients with stage IV IBC (p = 0.031). Patients treated with or without CT stratified by stage III and stage IV showed better survival than those without stage III and IV (p < 0.001). Trimodality therapy resulted in better survival than surgery combined with CT or CT alone (p < 0.001). Competing risk analysis also showed the same results. The nomogram was effectively applied to predict the 1, 3 and 5-year survival of IBC. Our nomogram showed relatively good accuracy with a C-index of 0.735 and is a visualized individually predictive tool for prognosis. Treatment strategy greatly affected the survival of patients. Trimodality therapy was the preferable therapeutic strategy for IBC. Further prospective studies are needed to validate these findings.

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