The annual meeting of the Radiological Society of North America was held from Nov. 26 to 30 in Chicago, drawing nearly 25,000 participants, including radiologists, radiation oncologists, physicists in medicine, radiologic technologists, and other health care professionals. The conference featured scientific papers from a number of subspecialties covering the newest trends in radiological research, as well as education and informatics exhibits.
In one study, Upasana Upadhyay Bharadwaj, M.D., of the University of California in San Francisco, and colleagues found that individuals with stronger quadriceps compared with their hamstrings have a lower risk for requiring total knee replacement surgery.
The authors focused on the relative muscle strength — using muscle volumes as a surrogate imaging marker — between quadriceps and hamstrings. Specifically, they evaluated thigh muscle volume among 134 individuals taking part in the National Institutes of Health Osteoarthritis Initiative, including 67 patients who underwent a total knee replacement and 67 who did not. The researchers found that a larger quadriceps volume relative to the volume of hamstrings was significantly associated with lower odds of total knee replacement surgery in two to four years.
“We hope these results can influence routine radiological interpretation and provide ratio-based quantitative biomarkers that characterize relative muscle volumes as surrogate markers for muscle strength,” Bharadwaj said. “While these results are most applicable for targeted therapy in a population at risk for knee osteoarthritis, even the general public may potentially benefit from our results to preventively incorporate strengthening exercises.”
In another study, Adam C. Zoga, M.D., of Jefferson Health in Philadelphia, and colleagues found that a computed tomography (CT)-guided stellate ganglion block is safe, quick, and a promising treatment option for patients with long-term post-COVID-19 parosmia.
The authors evaluated a CT-guided stellate ganglion block that aimed to reboot the olfactory system with a minimally invasive nerve injection. The researchers found that more than half of the patients treated experienced significant improvement of parosmia symptoms within one to two weeks.
“Percutaneous stellate ganglion blocks are already used in clinical practice for a myriad of disorders, including complex regional pain syndrome, cardiac arrhythmias, and tinnitus,” Zoga said. “We suggest that health care practitioners and facilities who treat patients with post-COVID-19 olfactory symptoms consider adding the stellate ganglion block to their treatment options.”
Mahsa Dolatshahi, M.D., of the Mallinckrodt Institute of Radiology at Washington University School of Medicine in St. Louis, and colleagues found that hidden belly fat is associated with a higher burden of Alzheimer disease pathology as early as midlife and is more prominent in men than women.
The authors evaluated hidden fat with abdominal magnetic resonance imaging (MRI). This type of fat has been shown to be linked to increased amyloid and brain atrophy on positron emission tomography (PET) and MRI brain scans. Data were evaluated for 54 healthy participants without any cognitive deficits (40 to 60 years of age) and an average body mass index of 32 kg/m2. The participants underwent glucose testing and MRI and PET scans. The researchers observed the hidden fat-Alzheimer biomarker connection in individuals at midlife (40s and 50s), 15 years on average before the earliest symptoms of Alzheimer disease typically appear.
“The findings of this study should be investigated longitudinally and with a larger sample size to be translated to the clinical practice,” Dolatshahi said. “The potential implications include using abdominal scans acquired for other reasons for opportunistic screening of visceral fat and its health sequences. Also, these results highlight the importance of maintaining healthy diet and physical activity.”
Anika S. Walia, a medical student at the Boston University School of Medicine, and colleagues found that an artificial intelligence (AI) tool can identify nonsmokers at high risk for developing lung cancer within six years.
The authors analyzed whether an AI tool could identify individuals who never smoked but had a high risk for lung cancer based on their chest X-rays from the electronic medical record (EMR). The researchers found that the AI tool can be used to identify nonsmokers at high risk for lung cancer, a population that is not being screened due to generally declining smoking rates in the United States, but in which there is an increasing proportion of new cancer diagnoses. The AI accomplished this by looking at existing chest X-ray images in the EMR obtained for cough, fever, or other routine indications.
“A clinical trial is necessary to tell whether high-risk people identified by the AI tool would benefit from further tests such as lung cancer screening CT. Lung cancer screening CT is much more accurate than chest X-ray for detecting lung cancer; however, it is not feasible or desirable for all nonsmokers to get CT,” Walia said. “This AI tool could help identify the nonsmokers at the highest risk who are most likely to benefit from CT.”
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