Imaging of tumors and tumor-like lesions of the knee

International audienceTumors and tumor-like lesions of the knee are common conditions. Because the synovial membrane covers a large part of the knee, tumors and tumor-like lesions of the knee are mostly synovial. Magnetic resonance imaging (MRI) plays a major role in the assessment and characterization of these lesions. However, the diagnostic approach of these lesions must be performed systematically. First, the lesion must be precisely located, and then the anatomical structure involved must be determined. Finally, clinical background that includes the age of the patient, frequency of the disease and, if any, associated signs as well as MRI characteristics must be analyzed. In this review, we describe the anatomy of the knee and its compartments and provide a description of the main tumors and tumor-like lesions of the knee. We present a diagnostic approach based on the location within the knee of the lesions and the anatomical structures involved

Sarcopenia Screened by the SARC-F Questionnaire and Physical Performances of Elderly Women : a Cross-Sectional Study

OBJECTIVES: Screening for sarcopenia in daily practice can be challenging. Our objective was to explore whether the SARC-F questionnaire is a valid screening tool for sarcopenia (defined by the Foundation for the National Institutes of Health [FNIH] criteria). Moreover, we evaluated the physical performance of older women according to the SARC-F questionnaire. DESIGN: Cross-sectional study. PARTICIPANTS: Data from the Toulouse and Lyon EPID\ue9miologie de l'OSt\ue9oporose study (EPIDOS) on 3025 women living in the community (mean age: 80.5 \ub1 3.9 years), without a previous history of hip fracture, were assessed. MEASUREMENTS: The SARC-F self-report questionnaire score ranges from 0 to 10: a score 654 defines sarcopenia. The FNIH criteria uses handgrip strength (GS) and appendicular lean mass (ALM; assessed by DXA) divided by body mass index (BMI) to define sarcopenia. Outcome measures were the following performance-based tests: knee-extension strength, 6-m gait speed, and a repeated chair-stand test. The associations of sarcopenia with performance-based tests was examined using bootstrap multiple linear-regression models; adjusted R2 determined the percentage variation for each outcome explained by the model. RESULTS: Prevalence of sarcopenia was 16.7% (n = 504) according to the SARC-F questionnaire and 1.8% (n = 49) using the FNIH criteria. Sensibility and specificity of the SARC-F to diagnose sarcopenia (defined by FNIH criteria) were 34% and 85%, respectively. Sarcopenic women defined by SARC-F had significantly lower physical performance than nonsarcopenic women. The SARC-F improved the ability to predict poor physical performance. CONCLUSION: The validity of the SARC-F questionnaire to screen for sarcopenia, when compared with the FNIH criteria, was limited. However, sarcopenia defined by the SARC-F questionnaire substantially improved the predictive value of clinical characteristics of patients to predict poor physical performance

Caractéristiques cliniques et évolution des patients hospitalisés pour une infection au SARS-CoV-2 au CHU de Toulouse. Résultats de la cohorte Covid-clinic-Toul

International audienceBackground. – Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread world-wide from epicenter of Wuhan, China since December 2019. The aim of our study was to describe theclinical characteristics and outcome of hospitalized patients with SARS-CoV-2 pneumonia at the Toulouseuniversity hospital, France.Patients and methods. – We selected the patients included from March 7, 2020 to April 20, 2020 in theretrolective Covid-clinic-Toul cohort that follows all hospitalized patients with SARS-CoV-2 infectionat the Toulouse Hospital. Cases were confirmed by real-time reverse transcriptase polymerase chainreaction. We report demographics, clinical, biological and radiological features, as well as unfavorableoutcome at Day 14 after admission (admission in an intensive care unit, mechanical ventilation, death).Results. – Among 263 hospitalized patients, the median age was 65 years and 155 (58.9%) were males.Two hundred and twenty-seven patients (86.3%) had at least one comorbidity. The median time fromfirst symptom to hospital admission was 7.0 days (interquartile range: 4–10). On day 14 after admission,111 patients (42.2%) had been transferred to intensive care unit (ICU), including 50 (19.0%) on Day 1; 61(23.1%) needed mechanical ventilation and 19 patients (7.2%) had died. Patients admitted to ICU at Day 1of admission (n = 50) were more frequently men (66.0% vs 57.3%), smokers (25.0% vs 7.1%), with obesity(42.0% vs 24.7%) and had a higher mean level of C-reactive protein (median: 110.9 mg/L vs 46.2 mg/L).Conclusion. – This cohort provides epidemiological data on SARS-CoV-2 in hospitalized patients in aUniversity hospital in the South of Franc

Blunt Trauma

Blunt-force injuries are produced when the body is struck with or strikes a blunt object [1–5]. Both mechanisms result in a transfer of kinetic energy that is high enough to produce an injury. Blunt objects have a relatively large area. Examples of blunt objects are almost infinite: fists, shoes, pipes, bricks, bats, hammers, the ground, or parts of vehicles such as cars, trains, or airplanes. A blunt surface produces injuries by torsion, compression, scraping, tearing, shearing, or crushing. Blunt-force injuries occur in many kinds of medico-legal situations and contexts: criminal assaults, physical child abuse, traffic accidents, and falls (criminal, accidental, or suicidal). The severity of the injuries resulting from trauma is a balance between the amount of force, the area over which it is applied, and the duration of the force [2, 6]. In general, the greater the force, the smaller the area, or the shorter the duration over which the force is applied, the greater the injury will be

Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI

International audiencePurposeThe goal of this data challenge was to create a structured dynamic with the following objectives: (1) teach radiologists the new rules of General Data Protection Regulation (GDPR), while building a large multicentric prospective database of ultrasound, computed tomography (CT) and MRI patient images; (2) build a network including radiologists, researchers, start-ups, large companies, and students from engineering schools, and; (3) provide all French stakeholders working together during 5 data challenges with a secured framework, offering a realistic picture of the benefits and concerns in October 2018.Materials and methodsRelevant clinical questions were chosen by the Société Francaise de Radiologie. The challenge was designed to respect all French ethical and data protection constraints. Multidisciplinary teams with at least one radiologist, one engineering student, and a company and/or research lab were gathered using different networks, and clinical databases were created accordingly.ResultsFive challenges were launched: detection of meniscal tears on MRI, segmentation of renal cortex on CT, detection and characterization of liver lesions on ultrasound, detection of breast lesions on MRI, and characterization of thyroid cartilage lesions on CT. A total of 5,170 images within 4 months were provided for the challenge by 46 radiology services. Twenty-six multidisciplinary teams with 181 contestants worked for one month on the challenges. Three challenges, meniscal tears, renal cortex, and liver lesions, resulted in an accuracy > 90%. The fourth challenge (breast) reached 82% and the lastone (thyroid) 70%.ConclusionTheses five challenges were able to gather a large community of radiologists, engineers, researchers, and companies in a very short period of time. The accurate results of three of the five modalities suggest that artificial intelligence is a promising tool in these radiology modalities