While contrast-enhanced mammography and contrast-enhanced MRI (CE-MRI) showed comparable diagnostic performance for detecting breast cancer, CE-MRI had superior sensitivity and negative likelihood ... Background Contrast-enhanced mammography (CEM) is a more accessible alternative to contrast-enhanced MRI (CE-MRI) in breast imaging, but a summary comparison of published studies is lacking. Purpos...

Background: Contrast-enhanced mammography (CEM) is rapidly expanding as a credible alternative to MRI in various clinical settings. Objective: To compare CEM and MRI for neoadjuvant therapy (NAT) r...

COVID-19 diagnosis relies on molecular testing for SARS-CoV-2 via nasopharyngeal swab in the presence of suggestive clinical, radiological and laboratory findings. Since bronchoalveolar lavage liquid (BAL) collected during fibrobronchoscopy may increase test sensitivity compared to nasopharyngeal swabs, it was performed during the 2020 pandemic in clinically or radiologically suspected cases. Our aim was to determine whether clinical features, chest computed tomography (CT) findings or laboratory tests may predict patients testing positive for SARS-CoV-2 at BAL after a negative nasopharyngeal swab. We performed a retrospective cross-sectional study with multivariable analysis of suspected patients who were tested for SARS-CoV-2 at BAL after at least one negative nasopharyngeal swab. Univariable logistic regression for odds ratio and multivariate models was calculated to determine clinical, radiological and laboratory predictors. 32/198 (16%) patients had BAL positive for SARS-CoV-2, while 65/198 tested positive for other pathogens at BAL. Of the 32 patients positive for COVID, 4 had a coinfection at BAL, being thus positive both for COVID as well as for another pathogen while the remaining 105 patients were negative for COVID and other pathogens at BAL. COVID-19 patients had more often highly suggestive CT findings, higher number of involved lobes, more often ground glass opacity of more than 50% of lung parenchyma, and less frequently other radiologically suspected infections. At multivariate model, temperature also predicted BAL positivity. The procedure was well tolerated—with only one desaturation episode—while no healthcare worker was infected. In conclusion, when nasopharyngeal swabs are negative but there is clinical or imaging suspicion of COVID-19, BAL represents a complementary diagnostic tool, particularly in conjunction with suggestive/more extensive lung involvement at CT scan. The procedure did not carry increased risks for patients nor for operators, while allowing to free hospital resources, avoiding unnecessary isolations.

To assess the impact of abbreviated breast MRI protocols on patient throughput considering non-scanning time and differences between in- and out-of-hospital settings. A total of 143 breast MRI exams from four study sites (hospital, three radiology centers) were included in this retrospective study. Total exam time (TET), Table Time (TT), Scan Time (ST), Table Switch Time (TST) and Planning Time (PT) were determined from consecutive breast MRI examinations. Possible number of scans and exams per hour were calculated. Four scan protocols were compared: full diagnostic protocol (n = 34, hospital), split dynamic protocol (n = 109, all sites) and two abbreviated protocols (n = 109, calculated, all sites). Data were described as median and interquartile range (IQR) and compared by Mann-Whitney-U-Test. Non-scanning time increased from 50% to 74% of the TET with a TST of 46% and a PT of 28% in the shortest abbreviated protocol. Number of possible scans per hour increased from 4.7 to 18.8 while number of possible exams per hour only increased from 2.3 to 5.1. Absolute TST (4.7 vs. 5.7 min, p = 0.46) and TET (18 min each, p = 0.35) did not differ significantly between in- and out-of-hospital exams. Absolute (4.4 vs. 2.8 min, p < 0.001) and relative (23 vs. 13%, p < 0.001) PT and TT (13.3 vs. 11.5 min, p = 0.004) was longer and relative TST (27% vs. 34%, p = 0.047) was shorter in hospital. TST and PT significantly contribute to TET and challenge the effectiveness of abbreviated protocols for increasing patient throughput. These findings show only low setting-dependent differences.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the “most infected volume” composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.