Impact of familial risk factors on management and survival of early-onset breast cancer: a population-based study

This population-based study evaluates the impact of a strong family history of breast cancer on management and survival of women with early-onset disease. We identified all breast cancer patients ⩽50 years, recorded between 1990 and 2001 at the Geneva familial breast cancer registry. We compared patients at high familial risk and low familial risk in terms of tumour characteristics, method of detection, treatment, survival and breast cancer mortality risk. Compared to patients at low familial risk (n=575), those at high familial risk (n=58) received significantly more often systemic therapy, especially for node-negative or receptor-positive disease. Five-year disease-specific survival rates of patients at high vs low familial risk were 86 and 90%, respectively. After adjustment, there was no difference in breast cancer mortality in general. A strong family history nonsignificantly increased breast cancer mortality in patients ⩽40 years (adjusted hazard ratio (HR) 4.0, 95% CI 0.8–19.7) and in patients treated without chemotherapy (adjusted HR 2.7, 95% CI 0.6–12.5). A strong family history of breast cancer is associated with an increased use of systemic therapy in early-onset patients. Although a strong family history does not seem to affect survival in general, it may impair survival of very young patients and patients treated without adjuvant chemotherapy. Owing to the limited number of patients in this study, these results should be used only to generate hypotheses

A Novel Medical E-Nose Signal Analysis System

It has been proven that certain biomarkers in people’s breath have a relationship with diseases and blood glucose levels (BGLs). As a result, it is possible to detect diseases and predict BGLs by analysis of breath samples captured by e-noses. In this paper, a novel optimized medical e-nose system specified for disease diagnosis and BGL prediction is proposed. A large-scale breath dataset has been collected using the proposed system. Experiments have been organized on the collected dataset and the experimental results have shown that the proposed system can well solve the problems of existing systems. The methods have effectively improved the classification accuracy