A n outbreak of coronavirus disease 2019 (COVID-19) began in Wuhan, China, in December 2019 and has rapidly spread around the world to become a pandemic (1). Italy was the second epicenter of the spread of the disease, and at the time of writing has a total of 222 104 cases and 31 106 deaths (1). Several studies have described the spectrum of chest imaging features of COVID-19 (2). However, to date, only a few case reports have described COVID-19-associated neurologic imaging findings (3-8). The purpose of our study was to systematically characterize neurologic symptoms and neuroimaging features in hospitalized patients with COVID-19 from multiple institutions in Italy.

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Introduction Over the last decade, the field of medical imaging experienced an exponential growth, leading to the development of radiomics, with which innumerable quantitative features are obtained from digital medical images, providing a comprehensive characterization of the tumor. This review aims to assess the role of this emerging diagnostic tool in breast cancer, focusing on the ability of radiomics to predict malignancy, response to neoadjuvant chemotherapy, prognostic factors, molecular subtypes, and risk of recurrence. Evidence Acquisition A literature search on PubMed and on Cochrane database websites to retrieve English-written systematic reviews, review articles, meta-analyses, and randomized clinical trials published from August 2013 up to July 2018 was carried out. Results Twenty papers (19 retrospective and 1 prospective studies) conducted with different conventional imaging modalities were included. Discussion The integration of quantitative information with clinical, histological, and genomic data could enable clinicians to provide personalized treatments for breast cancer patients. Current limitations of a routinely application of radiomics are represented by the limited knowledge of its basics concepts among radiologists and by the lack of efficient and standardized systems of feature extraction and data sharing.

The purpose of this study is to evaluate chest CT imaging features, clinical characteristics, laboratory values of COVID-19 patients who underwent CTA for suspected pulmonary embolism. We also examined whether clinical, laboratory or radiological characteristics could be associated with a higher rate of PE. Materials and methods: This retrospective study included 84 consecutive patients with laboratory-confirmed SARS-CoV-2 who underwent CTA for suspected PE. The presence and localization of PE as well as the type and extent of pulmonary opacities on chest CT exams were examined and correlated with the information on comorbidities and laboratory values for all patients. Results: Of the 84 patients, pulmonary embolism was discovered in 24 patients. We observed that 87% of PE was found to be in lung parenchyma affected by COVID-19 pneumonia. Compared with no-PE patients, PE patients showed an overall greater lung involvement by consolidation (p = 0.02) and GGO (p < 0.01) and a higher level of D-Dimer (p < 0,01). Moreover, the PE group showed a lower level of saturation (p = 0,01) and required more hospitalization (p < 0,01). Conclusion: Our study showed a high incidence of PE in COVID-19 pneumonia. In 87% of patients, PE was found in lung parenchyma affected by COVID-19 pneumonia with a worse CT severity score and a greater number of lung lobar involvement compared with non-PE patients. CT severity, lower level of saturation, and a rise in Ddimer levels could be an indication for a CTPA. Advances in knowledge: Certain findings of non-contrast chest CT could be an indication for a CTPA.

BackgroundThe human gleno-humeral joint is normally represented as a spherical hinge and its center of rotation is used to construct humerus anatomical axes and as reduction point for the computation of the internal joint moments. The position of the gleno-humeral joint center (GHJC) can be estimated by recording ad hoc shoulder joint movement following a functional approach. In the last years, extensive research has been conducted to improve GHJC estimate as obtained from positioning systems such as stereo-photogrammetry or electromagnetic tracking. Conversely, despite the growing interest for wearable technologies in the field of human movement analysis, no studies investigated the problem of GHJC estimation using miniaturized magneto-inertial measurement units (MIMUs). The aim of this study was to evaluate both accuracy and precision of the GHJC estimation as obtained using a MIMU-based methodology and a functional approach.MethodsFive different functional methods were implemented and comparatively assessed under different experimental conditions (two types of shoulder motions: cross and star type motion; two joint velocities: ωmax = 90°/s, 180°/s; two ranges of motion: Ɵ = 45°, 90°). Validation was conducted on five healthy subjects and true GHJC locations were obtained using magnetic resonance imaging.ResultsThe best performing methods (NAP and SAC) showed an accuracy in the estimate of the GHJC between 20.6 and 21.9 mm and repeatability values between 9.4 and 10.4 mm. Methods performance did not show significant differences for the type of arm motion analyzed or a reduction of the arm angular velocity (180°/s and 90°/s). In addition, a reduction of the joint range of motion (90° and 45°) did not seem to influence significantly the GHJC position estimate except in a few subject-method combinations.ConclusionsMIMU-based functional methods can be used to estimate the GHJC position in vivo with errors of the same order of magnitude than those obtained using traditionally stereo-photogrammetric techniques. The methodology proposed seemed to be robust under different experimental conditions. The present paper was awarded as “SIAMOC Best Methodological Paper 2016”.