Clinico-pathological characteristics, increased incidence, and mortality associated with aging can be explained on one hand by biological changes of the breast such as increased estrogen sensitivity, epithelial cell alterations, immune senescence, and tumor microenvironment modifications. However, sociologic factors such as increased life expectancy, under-treatment, late diagnosis, and insufficient individual screening, are also involved.

PurposeWe explored the clinical utility of human epidermal growth factor receptor-2 extracellular domain (HER2/ECD) in patients treated for an invasive breast cancer with HER2 overexpression.MethodsWe prospectively studied HER2/ECD levels in the sera of 334 women included between 2007 and 2014, all treated with trastuzumab. HER2/ECD levels were measured at diagnosis, during treatments, and along the follow-up. We investigated the relationship of HER2/ECD with other clinicopathological parameters at diagnosis, its prognosis value, and its utility during the monitoring of a neoadjuvant treatment and the follow-up.ResultsElevated HER2/ECD at diagnosis correlated positively with parameters associated with tumor aggressiveness. Disease-free survival of non-metastatic patients was significantly shorter in patients with high HER2/ECD at diagnosis (HR = 13.6, 95 % CI 1.6–113.6, P < 0.0001). Progression-free survival of metastatic patients was better for patients with low HER2/ECD (HR = 2.6, 95 % CI 1.2–5.3, P = 0.033). A multivariate analysis revealed that HER2/ECD level at diagnosis was an independent prognosis factor. During neoadjuvant therapy, a significant decrease in HER2/ECD was reported only for the complete histological response group (P = 0.031). During the follow-up, HER2/ECD helped predict relapse, disease progression, and metastases before imaging in 18.6 % cases of the studied cohort.ConclusionsHER2/ECD is a prognosis factor that is valuable in evaluating the neoadjuvant treatment efficiency. HER2/ECD also appears to be a helpful surveillance biomarker for the early diagnosis of relapses and to predict the fate of metastases. This study brings evidences to support the use of HER2/ECD in the management of HER2-positive breast cancer.

BackgroundThe aim of this study was the evaluation of breast MRI in determining the size and focality of invasive non-metastatic breast cancers.MethodsThe prospective, single-centre study conducted in 2015 compared preoperative MRI with histological analysis of mastectomy.ResultsOne hundred one mastectomies from 98 patients were extensively analysed. The rates of false-positive and false-negative MRI were 2 and 4% respectively. The sensitivity of breast MRI was 84.7% for the detection of all invasive foci, 69% for single foci and 65.7% for multiple foci. In the evaluation of tumour size, the Spearman rank correlation coefficient r between the sizes obtained by MRI and histology was 0.62. The MRI-based prediction of a complete response to neoadjuvant chemotherapy was 75%.DiscussionMRI exhibits high sensitivity in the detection of invasive breast cancers. False positives were linked to the inflammatory nature of the tumour bed. False negatives were associated with small or low-grade tumours and their retro-areolar location. The size of T1 tumours was overestimated by an average of 7%, but MRI was the most efficient procedure. The sensitivity of MRI for the diagnosis of unifocal tumours was higher than that for multifocal sites. Our study confirmed the positive contribution of preoperative MRI for invasive lobular carcinomas and complete response predictions after neoadjuvant chemotherapy.

The origins of Big Data date back to 1941, when the first references were made to the notion of "information explosion" in the Oxford Dictionary of English. James Maar has highlighted in 1996 in a report of the National Academy of Sciences the concept of "massive data set" (1). But it was only in 1997 that the precise term 'Big Data' first appeared in an article in the Digital Library of the Association for Computing Machinery (2), referring to the technical challenge of analyzing large sets of data. It has since been used to designate "structured or unstructured data, whose very large volume requires adapted analysis tools". Web giants (Google, Amazon, Facebook, Apple, Twitter) have developed such tools over the past decade, ensuring a constant marginal cost of data exploitation, regardless of volume.

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