Literature DB >> 24998960

Comparison of outcomes between women with de novo stage IV and relapsed breast cancer.

Dai Kitagawa1, Shin-ichiro Horiguchi, Toshinari Yamashita, Katsumasa Kuroi, Kazuo Shimizu.   

Abstract

BACKGROUND: Patients with de novo stage IV and relapsed breast cancer are often treated with the same strategy. However, survival differences have recently been reported between the disease types.
PURPOSE: The aim of this study was to compare outcomes between de novo stage IV disease and relapsed disease and to discuss any differences in prognostic factors between them. PATIENTS AND METHODS: The subjects were 79 patients with de novo stage IV disease and 213 patients with relapsed disease treated at the Tokyo Metropolitan Cancer and Infectious Diseases Center, Komagome Hospital, from October 2001 through November 2010. The Kaplan-Meier method was used to estimate overall survival (OS), and the Cox proportional hazards model was used to examine the association between metastatic disease and OS.
RESULTS: The median follow-up period was 32 months for de novo stage IV disease and 34 months for relapsed disease. The median OS was 46 months and 43 months, respectively. No significant differences were evident. Identified prognostic factors were performance status and liver metastasis for de novo stage IV disease, and performance status, hormone receptor status, solitary bone metastasis, and disease-free interval for relapsed disease.
CONCLUSION: No differences in outcome were found between de novo stage IV disease and relapsed disease. However, their prognostic factors differed substantially and suggest that different treatment strategies may be warranted for metastatic disease in each type of breast cancer.

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Year:  2014        PMID: 24998960     DOI: 10.1272/jnms.81.139

Source DB:  PubMed          Journal:  J Nippon Med Sch        ISSN: 1345-4676            Impact factor:   0.920


  4 in total

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4.  Factors associated with de novo metastatic disease in invasive breast cancer: comparison of artificial neural network and logistic regression models.

Authors:  Chunyan Qiu; Lingong Jiang; Yangsen Cao; Can Hu; Yiyi Yu; Huojun Zhang
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  4 in total

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