Evidence for non-exponential elastic proton-proton differential cross-section at low |t| and sqrt(s) = 8 TeV by TOTEM

The TOTEM experiment has made a precise measurement of the elastic proton-proton differential cross-section at the centre-of-mass energy sqrt(s) = 8 TeV based on a high-statistics data sample obtained with the beta* = 90 optics. Both the statistical and systematic uncertainties remain below 1%, except for the t-independent contribution from the overall normalisation. This unprecedented precision allows to exclude a purely exponential differential cross-section in the range of four-momentum transfer squared 0.027 < |t| < 0.2 GeV^2 with a significance greater than 7 sigma. Two extended parametrisations, with quadratic and cubic polynomials in the exponent, are shown to be well compatible with the data. Using them for the differential cross-section extrapolation to t = 0, and further applying the optical theorem, yields total cross-section estimates of (101.5 +- 2.1) mb and (101.9 +- 2.1) mb, respectively, in agreement with previous TOTEM measurements.Comment: Final version published in Nuclear Physics

Rapidity gap studies in DPE events with the TOTEM-CMS combined apparatus at √s = 8 TeV

Diffractive scattering processes have two main signatures: one or both incoming protons remain intact after the interaction and one or more rapidity gaps appear as forbidden regions in the rapidity distribution of scattering products. Rapidity gaps are associated to the exchange of pomerons between the interacting protons, the pomeron being described in QCD in terms of an exchange of gluons in a colorless configuration. In the context of diffractive physics, the study of rapidity gaps is then of particular interest. In the work reported in this thesis a study of rapidity gaps has been conducted in Double Pomeron Exchange events produced in pp collisions at √s = 8 TeV, using a dataset collected in July 2012 by the TOTEM experiment at the LHC during a common data taking with the CMS experiment. In DPE events both incoming protons remain intact in the collision and a system of particles is generated in the central zone, separated from the two protons by two rapidity gaps. Thanks to the combined TOTEM-CMS apparatus, which provides an exceptionally large pseudorapidity coverage, the tagging of protons with the TOTEM Roman Pot detectors and the reconstruction of the central system with the CMS apparatus has been performed. The TOTEM T2 telescope also provided the reconstruction of charged particles in the forward region, where no information from CMS is available. The aim of this work was the development of an analysis to study the rapidity gaps in DPE events, and compare them with a sample of DPE events obtained by a Pythia8MBR Monte Carlo simulation. Since such MC sample is based on a pure 2-gluon colorless exchange during the interaction, a deviation of rapidity gap probability could represent an indication of additional exchange not related to pomerons. In this study, an important role was covered by the charged particle tracks reconstructed in the TOTEM T2 telescopes and by final-state stable particles reconstructed and identified by means of a particular algorithm, known as “particle-flow" (PF), combining the information from the CMS subdetectors. They allowed to define in a wide |η| range the two rapidity gaps in DPE event candidates. The evaluation of the size of the rapidity gaps was possible through the direct leading proton measurement by the RP detectors. As first step, an optimization in the selection of PF neutral particles (neutral hadrons and photons) at √s = 8 TeV has been performed in order to suppress most of the detector noise in data, since standard cuts were previously obtained by the CMS Collaboration for data at √s = 7 TeV. The new cuts have been found in each CMS sub-region by using a Zero-Bias sample, collected during the same data taking period of the dataset used for DPE event selection (triggered by the TOTEM RPs). Then, the typical variables for the identification of DPE processes have been introduced: the fractional longitudinal momentum loss of each scattered proton (ξ1,2) reconstructed from the proton tracks; the two values of pseudorapidity ηmin and ηmax (related to the central diffractive system) which characterize the two rapidity gaps; the mass MX of the diffractive system obtained from the RP measurements, and the central mass Mcentral measured from the PF objects in the CMS region. In the next step of this study a selection of DPE event was performed in order to remove/reduce the main sources of background. The dominant background due to elastic events overlapped with pile-up processes has been removed by vetoing on the diagonal (TB/BT) configurations for the protons in the RPs. In order to reduce the pile-up effects in the selected parallel (TT/BB) configurations, we have selected only events with one proton per arm, simultaneously tagged by the two vertical RPs in the 220-station, and with no more than one CMS vertex. Then, by requiring a value of mass of the diffractive system greater than the value of the central mass, it was possible to reject events affected by residual noise and pile-up (NP) effects. After the selection of DPE events, a data driven correction method has been applied bin per bin to the probability distributions of ηmin and ηmax as an iterative procedure in order to correct for NP effects. This procedure has been based on studies on the Zero-Bias sample. In the following step, it was necessary to also consider the contribution of another source of background: the single diffractive process. Detailed studies performed on MC simulation showed that this contribution is dominant (up to about 19%) and is due when a proton produced by the breaking of one of two incoming protons arrives to RP detectors, simulating a leading proton. This contribution has been reduced by requiring activity in the CMS region. Based on MC studies, the iterative method correction has been updated in order to account for the residual SD background. Then, a comparison with MC expectations given by the Pythia8MBR generator was made. Here, a clear enhancement in events with reduced or absent reconstructed rapidity gaps in the very forward regions is observed in DATA, leading to a discrepancy in global normalization of the probability distributions in the central region, where a substantial agreement is found for the shapes. A better agreement between DATA and MC expectations is indeed found in the subsample where forward RGs are required (T2 veto on both sides). This result cannot a priori exclude that the violation of the expected rapidity gaps is due to some other process not characterized by the exchange of colorless objects, or that the MC generator we are considering is not properly modeling the DPE processes. However, further investigation should be performed in order to be sure that the observed behaviour is not due to some subtle residual background effect, or to some detector simulation related effect as well. For instance, it should be interesting to perform a separate study of events with zero and one CMS vertex, which are expected to be characterized by a different background bias

La biopsia ecoguidata Elite con sistema TruVac e sonda da 13 G: risultati preliminari

OBIETTIVO: La tipizzazione di noduli mammari mediante prelievi con ago sottile è talora complicata, per varie ragioni da esiti inconclusivi. Queste problematiche dilatano i tempi diagnostici rendendo necessarie ulteriori biopsie con risultati spesso discordanti. Per questo, in molti centri si stanno utilizzando aghi di calibro maggiore sostituendo la citologia con la microistologia: i sistemi di biopsia vuoto-assistita (vacuum-assisted breast biopsy, VABB) evidenziano performance superiori, risultando, tuttavia, spesso meno maneggevoli e più costosi. Lo scopo di questo studio è di valutare le performance di sistema del prelievo microistologico con ago 13 G e tecnologia VABB senza cavi con una maneggiabilità vicina a quella di un ago tranciante. METODI: Da gennaio 2016 a febbraio 2018, due operatori hanno eseguito complessivamente 86 prelievi microistologici con ago 13 G Elite su lesioni BIRADS 3, 4 e 5, delle quali 30 ripetute dopo precedenti prelievi cito-istologici inconclusivi. Sono state biopsiate lesioni tra 5 e 43 mm di cui 70 noduli, 12 aree di alterazione ecostrutturale non-mass like e tre cisti complex. RISULTATI: Il sistema 13 G ha evidenziato 3,53% casi B1, 41,17% B2, 17,64% B3 e 37,64% B5. Nello stesso periodo i prelievi con ago tranciante 14-16 G con i medesimi operatori hanno evidenziato i seguenti risultati: 2,65% B1, 44,33% B2, 9% B3, 0,48% B4, 43,49% B5. Il prelievo da 13 G Elite ha permesso un cambio di classe istologica nell’83,33% delle procedure ripetute dopo prelievo non dirimente. CONCLUSIONI: La procedura bioptica con sistema TruVac si è dimostrata affidabile e potrebbe essere utilizzata per ridurre i casi con esito cito-istologico non dirimente

Anaplastic large-cell periprosthetic lymphoma of the breast: could fibrin be an early radiological indicator of the presence of disease?

The onset characteristics of the anaplastic large cell lymphoma (BI-ALCL) are non-specific and the diagnosis is often difficult and based on clinical suspicion and cytological sampling. The presence of non-pathognomonic radiological signs may delay the diagnosis of BI-ALCL, influencing patient prognosis. This could have an important social impact, considering that the incidence of BI-ALCL correlates with the number of prosthetic implants, which is in constant increase worldwide. The aim of this study was to verify if fibrin can represent a potential early radiological sign of the disease

Radiomics Analysis on Contrast-Enhanced Spectral Mammography Images for Breast Cancer Diagnosis: A Pilot Study

Contrast-enhanced spectral mammography is one of the latest diagnostic tool for breast care; therefore, the literature is poor in radiomics image analysis useful to drive the development of automatic diagnostic support systems. In this work, we propose a preliminary exploratory analysis to evaluate the impact of different sets of textural features in the discrimination of benign and malignant breast lesions. The analysis is performed on 55 ROIs extracted from 51 patients referred to Istituto Tumori &ldquo;Giovanni Paolo II&rdquo; of Bari (Italy) from the breast cancer screening phase between March 2017 and June 2018. We extracted feature sets by calculating statistical measures on original ROIs, gradiented images, Haar decompositions of the same original ROIs, and on gray-level co-occurrence matrices of the each sub-ROI obtained by Haar transform. First, we evaluated the overall impact of each feature set on the diagnosis through a principal component analysis by training a support vector machine classifier. Then, in order to identify a sub-set for each set of features with higher diagnostic power, we developed a feature importance analysis by means of wrapper and embedded methods. Finally, we trained an SVM classifier on each sub-set of previously selected features to compare their classification performances with respect to those of the overall set. We found a sub-set of significant features extracted from the original ROIs with a diagnostic accuracy greater than 80 % . The features extracted from each sub-ROI decomposed by two levels of Haar transform were predictive only when they were all used without any selection, reaching the best mean accuracy of about 80 % . Moreover, most of the significant features calculated by HAAR decompositions and their GLCMs were extracted from recombined CESM images. Our pilot study suggested that textural features could provide complementary information about the characterization of breast lesions. In particular, we found a sub-set of significant features extracted from the original ROIs, gradiented ROI images, and GLCMs calculated from each sub-ROI previously decomposed by the Haar transform

Ensemble Discrete Wavelet Transform and Gray-Level Co-Occurrence Matrix for Microcalcification Cluster Classification in Digital Mammography

The presence of clusters of microcalcifications is a primary sign of breast cancer. Their identification is still difficult today for radiologists, and the wrong evaluations involve unnecessary biopsies. In this paper, an automatic tool for characterizing and discriminating clusters of microcalcifications into benign/malignant in digital mammograms is proposed. A set of 104 digital mammograms including microcalcification clusters was randomly extracted from a public available database and manually labeled by our radiologists, obtaining 96 abnormal ROIs. For each so-identified ROI, a multi-scale image decomposition based on the Haar wavelet transform was performed. On the decomposition, a textural features extraction step was carried out both on each sub-image and on the corresponding gray-level co-occurrence matrix. Then, a random forest classifier was employed for classifying microcalcification clusters into benign and malignant. The study found that the most discriminant features extracted from the ROIs decomposition by Haar transform were variance and relative smoothness, whereas as regards the textural features calculated on the GLCMs corresponding to the Haar-decomposed ROI, it emerged that the relationship between the pixels of the sub-image in the diagonal direction had high discriminating power for the classification of microcalcification clusters into benign and malignant. The proposed method was evaluated in cross-validation and performed highly in the prediction of the benign/malignant ROIs, with a mean AUC value of 97.39 &plusmn; 0.01 %

Fully Automated Support System for Diagnosis of Breast Cancer in Contrast-Enhanced Spectral Mammography Images

Contrast-Enhanced Spectral Mammography (CESM) is a novelty instrumentation for diagnosing of breast cancer, but it can still be considered operator dependent. In this paper, we proposed a fully automatic system as a diagnostic support tool for the clinicians. For each Region Of Interest (ROI), a features set was extracted from low-energy and recombined images by using different techniques. A Random Forest classifier was trained on a selected subset of significant features by a sequential feature selection algorithm. The proposed Computer-Automated Diagnosis system is tested on 48 ROIs extracted from 53 patients referred to Istituto Tumori "Giovanni Paolo II" of Bari (Italy) from the breast cancer screening phase between March 2017 and June 2018. The present method resulted highly performing in the prediction of benign/malignant ROIs with median values of sensitivity and specificity of 87 . 5 % and 91 . 7 % , respectively. The performance was high compared to the state-of-the-art, even with a moderate/marked level of parenchymal background. Our classification model outperformed the human reader, by increasing the specificity over 8 % . Therefore, our system could represent a valid support tool for radiologists for interpreting CESM images, both reducing the false positive rate and limiting biopsies and surgeries

A combined approach of multiscale texture analysis and interest point/corner detectors for microcalcifications diagnosis

creening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic model for characterizing and discriminating tissue in normal/abnormal and benign/malign in digital mammograms, as support tool for the radiologists. We trained a Random Forest classifier on some textural features extracted on a multiscale image decomposition based on the Haar wavelet transform combined with the interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg), respectively. We tested the proposed model on 192 ROIs extracted from 176 digital mammograms of a public database. The model proposed was high performing in the prediction of the normal/abnormal and benign/malignant ROIs, with a median AUC value of 98.46% and 94.19%, respectively. The experimental result was comparable with related work performance

Double Diffractive Cross-Section Measurement in the Forward Region at the LHC

The first double diffractive cross-section measurement in the very forward region has been carried out by the TOTEM experiment at the LHC with a center-of-mass energy of root s = 7 TeV. By utilizing the very forward TOTEM tracking detectors T1 and T2, which extend up to vertical bar eta vertical bar = 6.5, a clean sample of double diffractive pp events was extracted. From these events, we determined the cross section sigma(DD) = (116 +/- 25) mu b for events where both diffractive systems have 4.7 &lt; vertical bar eta vertical bar(min) &lt; 6.5

PERFORMANCE OF THE TOTEM DETECTORS AT THE LHC

The TOTEM Experiment is designed to measure the total proton-proton cross-section with the luminosity-independent method and to study elastic and diffractive pp scattering at the LHC. To achieve optimum forward coverage for charged particles emitted by the pp collisions in the interaction point IP5, two tracking telescopes, T1 and T2, are installed on each side of the IP in the pseudorapidity region 3.1 &lt;= |eta| &lt;= 6.5, and special movable beam-pipe insertions - called Roman Pots (RPs) - are placed at distances of +/- 147m and +/- 220m from IP5. This article describes in detail the working of the TOTEM detector to produce physics results in the first three years of operation and data taking at the LHC