The handheld Mammotome diminishes the shortcomings of the automated core biopsy device. It reduces the possibility of false-negatives and underestimation of disease. It eliminates the need for multiple insertions and reduces the likelihood of epithelial displacement. As a result, we now use this device for all sonographically guided biopsies of breast masses smaller than 1.5 cm and recommend that others consider it for such use.

Background Screening clinical breast examination (cbe) is controversial; the use of cbe is declining not only as a

In recent years, risk stratification has sparked interest as an innovative approach to disease screening and prevention. The approach effectively personalizes individual risk, opening the way to screening and prevention interventions that are adapted to subpopulations. The international perspective project, which is developing risk stratification for breast cancer, aims to support the integration of its screening approach into clinical practice through comprehensive tool-building. Policies and guidelines for risk stratification-unlike those for population screening programs, which are currently well regulated-are still under development. Indeed, the development of guidelines for risk stratification reflects the translational aspects of perspective.Here, we describe the risk stratification process that was devised in the context of perspective, and we then explain the consensus-based method used to develop recommendations for breast cancer screening and prevention in a risk-stratification approach. Lastly, we discuss how the recommendations might affect current screening policies.

Background - Decision scores and ethically mindful algorithms are being established to adjudicate mechanical ventilation in the context of potential resources shortage due to the current onslaught of COVID-19 cases. There is a need for a reproducible and objective method to provide quantitative information for those scores. Purpose - Towards this goal, we present a retrospective study testing the ability of a deep learning algorithm at extracting features from chest x-rays (CXR) to track and predict radiological evolution. Materials and Methods - We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from two open-source datasets (last accessed on April 9, 2020)(Italian Society for Medical and Interventional Radiology and MILA). Data collected form 60 pairs of sequential CXRs from 40 COVID patients (mean age +/- standard deviation: 56 +/- 13 years; 23 men, 10 women, seven not reported) and were categorized in three categories: Worse, Stable, or Improved on the basis of radiological evolution ascertained from images and reports. Receiver operating characteristic analyses, Mann-Whitney tests were performed. Results - On patients from the CheXnet dataset, the area under ROC curves ranged from 0.71 to 0.93 for seven imaging features and one diagnosis. Deep learning features between Worse and Improved outcome categories were significantly different for three radiological signs and one diagnostic (Consolidation, Lung Lesion, Pleural Effusion and Pneumonia; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between Worse and Improved cases with 82.7% accuracy. Conclusion - CXR deep learning features show promise for classifying the disease trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.