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Maggie L. Westfal David C. Chang Cassandra M. Kelleher 《Journal of pediatric surgery》2019,54(1):140-144
Purpose
The purpose of this study was to evaluate trends in demographics and outcomes of pediatric breast cancer in a United States population-based cohort.Methods
The Surveillance, Epidemiology, and End Results (SEER) database was utilized to identify all pediatric patients with malignant breast tumors between 1973 and 2014. Analysis was performed using Stata Statistical Software version 13.1. Associations between categorical variables were made using X2 test. Log-rank test was used for univariate survival analysis. Kaplan–Meier analysis investigated five-year survival rates across several variables. Adjusted analysis was performed using a Cox Proportional-Hazards regression.Results
134 patients with breast malignancies were identified. Carcinoma was the most prevalent histology (48.5%), followed by fibroepithelial tumors (FETs) (35.1%), and sarcoma (14.2%). FETs were twice as common in black compared to nonblack patients (56.3% vs. 29.0%, p?<?0.01). Analyzing histology by stage revealed that 100% of FETs were early stage disease (p?<?0.0001). 46.7% of the tumors tested were ER/PR negative, more than twice as many compared to the published adult estimate of 20.0%. Unadjusted survival analysis revealed worse survival for patients with adenocarcinoma/sarcomas, advanced stage, and high grade disease, without a survival difference between races.Conclusion
Breast cancer remains a rare malignancy among pediatric patients. Although black patients were found to have more noncarcinomatous tumors with less advanced disease, this did not confer a survival advantage.Type of study
Retrospective cohort study.Level of evidence
Level III. 相似文献109.
James I. Geller MD Joseph G. Pressey MD Malcolm A. Smith MD Rachel A. Kudgus PhD Mariana Cajaiba MD Joel M. Reid PhD David Hall PhD Donald A. Barkauskas PhD Stephen D. Voss MD Steve Y. Cho MD Stacey L. Berg MD Jeffrey S. Dome MD PhD Elizabeth Fox MD Brenda J. Weigel MD 《Cancer》2020,126(24):5303-5310
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Julie A. Schmidt Georgina K. Fensom Sabina Rinaldi Augustin Scalbert Paul N. Appleby David Achaintre Audrey Gicquiau Marc J. Gunter Pietro Ferrari Rudolf Kaaks Tilman Kühn Heiner Boeing Antonia Trichopoulou Anna Karakatsani Eleni Peppa Domenico Palli Sabina Sieri Rosario Tumino Bas Bueno-de-Mesquita Antonio Agudo Maria-Jose Sánchez María-Dolores Chirlaque Eva Ardanaz Nerea Larrañaga Aurora Perez-Cornago Nada Assi Elio Riboli Konstantinos K. Tsilidis Timothy J. Key Ruth C. Travis 《International journal of cancer. Journal international du cancer》2020,146(3):720-730
Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case–control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR1SD) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR1SD = 0.77, 95% confidence interval 0.66–0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR1SD = 0.72, 0.57–0.90), or lysophosphatidylcholines (OR1SD = 0.81, 0.69–0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR1SD = 0.77, 0.61–0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer. 相似文献