Explore the Human Insight and Visionary Medicine Featured at RSNA 2020
Press releases showcase the latest research, education and technological advancements to be featured at the annual meeting
At RSNA 2020, innovative medical imaging research and new technologies will make their debut. Explore highlights from this year's annual meeting in these preview press releases.
A novel outpatient procedure offers lasting pain relief for patients suffering from moderate to severe arthritis in their hip and shoulder joints, according to a study Emory University in Atlanta, Georgia. Researchers said the procedure could help reduce reliance on addictive opiates. For the study, 23 people with osteoarthritis underwent treatment, including 12 with shoulder pain and 11 with hip pain that had become unresponsive to anti-inflammatory pain control and intra-articular lidocaine-steroid injections. Treatment was performed two to three weeks after the patients received diagnostic anesthetic nerve blocks. The patients then completed surveys to measure their function, range of motion and degree of pain before and at three months after the ablation procedures. There were no procedure-related complications, and both the hip and shoulder pain groups reported statistically significant decrease in the degree of pain with corresponding increase in dynamic function after the treatment.
Racket sports like tennis and racquetball appear to increase knee joint degeneration in overweight people with osteoarthritis, according to a study from the University of California San Francisco. Researchers used high-powered MRI to assess the rate of degeneration of the knee joint in 415 overweight and/or obese patients, average age 59, drawn from the Osteoarthritis Initiative. Study participants kept detailed records of their participation in six different types of physical activity, including ball sports, bicycling, jogging/running, elliptical trainer, racket sports and swimming. The researchers performed baseline MRIs and then measured changes in the patients’ knees over four years. Patients regularly participating in racket sports had accelerated joint degeneration over the study period compared to other activities.
Some patients with COVID-19 are at higher risk of neurological complications like bleeding in the brain and stroke, according to a study from the University of Pennsylvania and Penn Medicine. These potentially life-threatening findings were more common in patients with hypertension and diabetes. Of the 1,357 patients with COVID-19 admitted to the system in those four months, 81 had a brain scan performed. Out of 81 patients with brain scans, 18, had findings that were considered emergency or critical, including strokes, brain bleeds and blocked blood vessels. At least half the patients had pre-existing histories of high blood pressure and/or type 2 diabetes. Three patients with emergent/critical findings died while admitted.
Women who experience food or housing insecurity may be at risk for undiagnosed breast cancer due to lapses in follow-up appointments. Researchers retrospectively reviewed the medical records of two groups of women undergoing breast imaging at Boston Medical Center from January 2015 to December 2018. The first group included 4,959 women who underwent screening mammography and, based on a BI-RADS score of 0, were recommended for diagnostic imaging. The second group included 3,028 women who underwent diagnostic breast imaging and were recommended for a breast biopsy based on a BI-RADS score of 4 or 5. Results of the statistical analysis demonstrated that having food or housing insecurity was associated with longer lapses between diagnostic imaging and breast biopsy compared to interval times for women without those unmet social needs.
A simple eye exam combined with powerful artificial intelligence (AI) machine learning technology could provide early detection of Parkinson’s disease. Researchers from the University of Florida deployed a type of AI called support vector machine (SVM) learning. Using pictures of the back of the eye from both patients with Parkinson’s disease and control participants, they trained the SVM to detect signs on the images suggestive of disease. The results indicated that the machine learning networks can classify Parkinson’s disease based on retina vasculature, with the key features being smaller blood vessels. The proposed methods further support the idea that changes in brain physiology can be observed in the eye.
In response to the critical shortage of nasopharyngeal swabs early in the COVID-19 pandemic, researchers at University of South Florida (USF) Health in Tampa designed and created swabs using a point-of-care 3D printer and now present the results of the first clinical trial. At the three trial sites, 291 patients (ages 14-94) who were hospitalized or seen in the emergency room were tested for COVID-19 using both the flocked swab and 3D swab. The 3D swab displayed statistically identical results to the flocked swab in the head-to-head trial. Hospitals around the world have used the files to print tens of millions of 3D swabs.
A common weight loss surgery for adolescents with obesity called sleeve gastrectomy has harmful effects on bones, according to a study from researchers at Harvard Medical School and Massachusetts General Hospital in Boston. The study examined 52 adolescents with moderate to severe obesity, 26 of whom underwent sleeve gastrectomy. The other 26 were in the control group. Mean age was 17.5 years, and mean body mass index (BMI) was 45. Thirty-eight of study participants were girls. Before and 12 months after sleeve gastrectomy (or no surgery), the patients underwent quantitative CT of the lumbar spine, to quantify volumetric bone mineral density. One year following surgery, the adolescents who underwent sleeve gastrectomy lost 34 (+/-13) kg, or 75 (+/-28) pounds, while there was no significant change in weight in the control group. Compared to the controls, sleeve gastrectomy patients had a significant increase in bone marrow fat and a decrease in bone density in the lumbar spine.
Anxiety is associated with an increased rate of progression from mild cognitive impairment to Alzheimer’s disease, according to a new study from the Medical University of South Carolina in Charleston. The study group included 339 patients with an average age of 72 years, from the Alzheimer’s Disease Neuroimaging Initiative 2 cohort. Each person had a baseline diagnosis of mild cognitive impairment; 72 progressed to Alzheimer’s disease while 267 remained stable. The researchers obtained brain MRIs to determine the baseline volumes of the hippocampus and the entorhinal cortex and tested for the presence of the ApoE4 allele, the most prevalent genetic risk factor for Alzheimer’s disease. Anxiety was measured with established clinical surveys. Patients who progressed to Alzheimer’s disease had significantly lower volumes in the hippocampus and the entorhinal cortex and greater frequency of the ApoE4 allele. Anxiety was independently associated with cognitive decline.
Quantitative CT shows that people who cook with biomass fuels like wood are at risk of suffering considerable damage to their lungs. A multidisciplinary team from the University of Iowa, in collaboration with researchers from Periyar Maniammai Institute of Science and Technology, investigated the impact of cookstove pollutants in 23 people cooking with liquefied petroleum gas or wood biomass in Thanjavur, India. The researchers measured the concentrations of pollutants in the homes and then studied the lung function of the individuals, using traditional tests such as spirometry. They also used CT to make quantitative measurements. Analysis showed that people who cooked with wood biomass were exposed to greater concentrations of pollutants and bacterial endotoxins and had a significantly higher level of air trapping in their lungs, a condition associated with lung diseases. The researchers said the findings also have implications for people exposed to biomass smoke from wildfires.
Researchers at Massachusetts General Hospital (MGH) have developed a deep learning model that identifies imaging biomarkers on screening mammograms to predict a patient’s risk for developing breast cancer with greater accuracy than traditional risk assessment tools. Currently available risk assessment models incorporate only a small fraction of patient data and only one feature from the screening mammogram itself—breast density. The new deep learning algorithm was developed using data from five MGH breast cancer screening sites with a population that included women with a personal history of breast cancer, implants or prior biopsies. The study included 245,753 consecutive 2D digital bilateral screening mammograms performed in 80,818 patients between 2009 and 2016. The deep learning model achieved a predictive rate for breast cancer of 0.71, significantly outperforming the traditional risk model, which achieved a rate of 0.61.
Up to one-third of adult women who sustain a non-displaced fracture to the ulna bone of the forearm may be victims of intimate partner violence, according to a study from Brigham and Women’s Hospital and Boston Medical Center. Fractures to the ulna, the bone on the pinkie side of the forearm, often occur when people hold up their hands to protect their faces from being struck with an object. Researchers searched electronic medical records from six hospitals for isolated ulnar fractures in women ages 18 to 50. They identified 62 patients, average age 31. Of those, 12 were confirmed for intimate partner violence and another eight were suspected of intimate partner violence. Analysis of the X-rays demonstrated that intimate partner violence was strongly associated with minimally displaced fractures. The study results suggest that intimate partner violence screening may be underutilized.
Automated deep learning analysis of abdominal CT images produces a more precise measurement of body composition and predicts major cardiovascular events better than overall weight or body mass index (BMI), according to a study from Brigham and Women’s Hospital in Boston. A single axial CT slice of the abdomen visualizes the volume of subcutaneous fat area, visceral fat area and skeletal muscle area. Researchers developed a fully automated method using deep learning to determine body composition metrics from abdominal CT images. The retrospective study group included 12,128 patients free of major cardiovascular and cancer diagnoses at the time of imaging. Mean age of the patients was 52 years, and 57% of patients were women. Statistical analysis demonstrated that visceral fat area was independently associated with future heart attack and stroke. BMI was not associated with heart attack or stroke
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