Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk.

We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo-DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists' visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48-55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5-4.4) and a 3.6 (95% CI 1.4-9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists' visual assessment as both point estimate and lower bound of 95% CI (AUC 0.589; 95% CI 0.580-0.597) were above the predefined visual assessment threshold (AUC 0.571). AUC for interval (AUC 0.631; 95% CI 0.623-0.639) cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists' visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe

Search for anomalous couplings in boosted WW/WZ -> l nu q(q)over-bar production in proton-proton collisions at root s=8TeV

Peer reviewe

Evaluation du risque de maladie : conception d'un processus et d'un système d'information permettant la construction d'un score de risque adapté au contexte, application au cancer du sein

Although there are many risk scores in the health field to predict disease risk, they are not as used as they could be to individualize and enhance prevention based on an estimated risk level. In order to facilitate the production of risk scores that are efficient in detecting high risk profiles and that fit to the context of use, we suggest a risk score building process. In order to conduct experiments, we build an information system architecture that supports the building and use process of risk scores. Thanks to the implementation of this architecture, we use our process to experiment the creation of breast cancer risk scores based on a publicly available american database and on the E3N French cohort study database. Using the breast cancer example, we show that it is possible to obtain comparable performances in terms of discrimination and better performances in calibration than available risk scores of the literature, using a readable k-nearest-neighbor algorithm and less attributes.Bien que de nombreux scores existent dans le domaine de la santé pour prédire un risque de maladie, ceux-ci sont peu utilisés alors qu'ils pourraient servir à individualiser la prévention pour la renforcer en fonction du niveau de risque estimé. Pour faciliter la production de scores performants dans la détection des profils à risque et adaptés au contexte d'utilisation, nous proposons un processus de construction de scores de risque. Afin de mener des expérimentations, nous spécifions l'architecture d'un système d'information qui supporte les processus de production et d'utilisation de scores de risque. Grâce à la mise en oeuvre d'une partie de cette architecture, nous utilisons notre processus pour expérimenter la création de scores de risque du cancer du sein basés sur une base de données américaine publique et sur les données françaises de l'étude de cohorte E3N. Sur l'exemple du cancer du sein, nous montrons qu'il est possible d'obtenir des performances comparables en termes de discrimination et supérieures en termes de calibration à celles de la littérature avec l'algorithme des plus proches voisins qui est compréhensible par les médecins et patients, tout en utilisant moins d'attributs

Evaluation du risque de maladie : conception d'un processus et d'un système d'information permettant la construction d'un score de risque adapté au contexte, application au cancer du sein

Although there are many risk scores in the health field to predict disease risk, they are not as used as they could be to individualize and enhance prevention based on an estimated risk level. In order to facilitate the production of risk scores that are efficient in detecting high risk profiles and that fit to the context of use, we suggest a risk score building process. In order to conduct experiments, we build an information system architecture that supports the building and use process of risk scores. Thanks to the implementation of this architecture, we use our process to experiment the creation of breast cancer risk scores based on a publicly available american database and on the E3N French cohort study database. Using the breast cancer example, we show that it is possible to obtain comparable performances in terms of discrimination and better performances in calibration than available risk scores of the literature, using a readable k-nearest-neighbor algorithm and less attributes.Bien que de nombreux scores existent dans le domaine de la santé pour prédire un risque de maladie, ceux-ci sont peu utilisés alors qu'ils pourraient servir à individualiser la prévention pour la renforcer en fonction du niveau de risque estimé. Pour faciliter la production de scores performants dans la détection des profils à risque et adaptés au contexte d'utilisation, nous proposons un processus de construction de scores de risque. Afin de mener des expérimentations, nous spécifions l'architecture d'un système d'information qui supporte les processus de production et d'utilisation de scores de risque. Grâce à la mise en oeuvre d'une partie de cette architecture, nous utilisons notre processus pour expérimenter la création de scores de risque du cancer du sein basés sur une base de données américaine publique et sur les données françaises de l'étude de cohorte E3N. Sur l'exemple du cancer du sein, nous montrons qu'il est possible d'obtenir des performances comparables en termes de discrimination et supérieures en termes de calibration à celles de la littérature avec l'algorithme des plus proches voisins qui est compréhensible par les médecins et patients, tout en utilisant moins d'attributs

A Nearest Neighbor Approach to Build a Readable Risk Score for Breast Cancer

International audienceAccording to the World Health Organization, starting from 2010, cancer has become the leading cause of death worldwide. Prevention of major cancer localizations through a quantified assessment of risk factors is a major concern in order to decrease their impact in our society. Our objective is to test the performances of a modeling method that answers to needs and constraints of end users. In this article, we follow a data mining process to build a reliable assessment tool for primary breast cancer risk. A k-nearest-neighbor algorithm is used to compute a risk score for different profiles from a public database. We empirically show that it is possible to achieve the same performances as logistic regressions with less attributes and a more easily readable model. The process includes the intervention of a domain expert, during an offline step of the process, who helps to select one of the numerous model variations by combining at best, physician expectations and performances. A risk score made of four parameters: age, breast density, number of affected first degree relatives and breast biopsy, is chosen. Detection performance measured with the area under the ROC curve is 0.637. A graphical user interface is presented to show how users will interact with this risk score