Background: Inflammatory breast cancer (IBC) is a rare but most aggressive breast cancer subtype. The impact of locoregional therapy on survival in IBC is controversial.
Methods: Patients with nonmetastatic IBC between 1988 and 2013 were identified in the Surveillance, Epidemiology, and End Results (SEER) registry.
Results: We identified 7,304 female patients with nonmetastatic inflammatory breast cancer (IBC) who underwent primary tumor surgery. Most patients underwent total mastectomy with only 409 (5.6%) undergoing a partial mastectomy. In addition, 4,559 (62.4%) were also treated with radiation therapy. The patients who underwent mastectomy had better survival compared to partial mastectomy (49% versus 43%, = 0.003). The addition of radiation therapy was also associated with improved 5-year survival (55% versus 40%, < 0.001). Multivariate analysis showed that black race HR (1.22, 95% CI 1.18-1.35), ER negative status (HR 1.22, 95% CI 1.16-1.28), and higher grade (HR 1.14, 95% CI 1.07-1.20) were associated with poor outcome. Cox proportional hazards model showed that total mastectomy (HR 0.75, 95% CI 0.65-0.85) and radiation (HR 0.64, 95% CI 0.61-0.69) were associated with improved survival.
Conclusions: Optimal locoregional therapy for women with nonmetastatic IBC continues to be mastectomy and radiation therapy. These data reinforce the prevailing treatment algorithm for nonmetastatic IBC.
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http://dx.doi.org/10.1155/2018/6438635 | DOI Listing |
Sci Rep
December 2024
Department of Clinical Pharmacy, Baoshan Hospital Affiliated to, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
This study investigates the potential treatment of breast cancer utilizing Gentiana robusta King ex Hook. f. (QJ) through an integrated approach involving network pharmacology, molecular docking, and molecular dynamics simulation.
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December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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