Articles tagged with Radiology
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Dr. Mark S. Parker, MD, FACR, has been recognized as the 2022 ARRS Distinguished Educator for his outstanding contributions to radiological education. He will be formally recognized at the ARRS Annual Meeting in New Orleans, LA on May 1, 2022.
A new study reveals that radiologist fatigue impacts performance differently depending on experience level. Less-experienced radiologists are more likely to recommend additional imaging for women undergoing breast cancer screening when interpreting digital breast tomosynthesis (DBT) images later in the day.
Automated brain volumetry in memory-impaired patients shows significant differences and systematic biases between conventional and ultrafast 3D T1-weighted MRI sequences. Most regions demonstrated substantial agreement but also significantly different mean values and consistent biases.
Researchers from Egypt used 3D CT scanning to 'digitally unwrap' the mummy of Pharaoh Amenhotep I, dispelling a theory that 21st dynasty restorers reused old royal burial equipment. The study revealed that the priests lovingly repaired injuries inflicted by tomb robbers and preserved the pharaoh's jewelry and amulets.
A new study found that AI can help physicians interpret x-rays after an injury and suspected fracture. AI-assisted diagnosis reduced missed fractures by 29% and increased sensitivity by 16%, improving efficiency and accuracy.
Researchers have published an extensive 7T fMRI dataset to study how humans perceive and interpret naturalistic photographs. The Natural Scenes Dataset provides a massive scale of brain data for training complex deep-learning models that predict brain activity.
Researchers at KAUST have developed a nanocomposite that absorbs X-rays with near-perfect efficiency and re-emits the energy as light. This innovation improves high-resolution medical imaging and security screening, with detection limits up to 142 times lower than traditional methods.
A prospective study found that ultrasound alone adequately completed the diagnostic evaluation of 71.2% of noncalcified lesions, diagnosed 92.1% of cancers, and yielded a sensitivity of 94.9%. Radiologists should consider performing ultrasound first for digital breast tomosynthesis-recalled noncalcified masses.
Researchers simulated an attack that falsified mammogram images, fooling both AI breast cancer diagnosis models and human radiologist experts. The study highlights the need to develop ways to make AI models more robust to adversarial attacks, which could lead to incorrect cancer diagnoses.
A prospective study found that MRI outperformed conventional tests and FDG PET/CT for various staging endpoints in SCLC. Whole-body MRI and FDG PET/MRI were more accurate than FDG PET/CT for assessing local invasion extent.
A recent review reveals alarming increase in physician burnout among diagnostic radiologists, with causes including lack of control, social isolation and technological demands. Possible solutions include heightened engagement, dedicated reading room assistants and mindfulness techniques.
A study found that racial disparities in mammography screening worsened after COVID-19 closures, with non-white patients facing reduced access to facilities. Early interventions aimed at expanding access to these facilities helped to recover late recovery volumes for patients of races other than white.
Convolutional neural networks trained to identify abnormalities on upper extremity radiographs are susceptible to a ubiquitous confounding image feature: radiograph labels. Covering these labels increases accuracy, while using them alone leads to decreased performance.
A retrospective study found that Lung-RADS performed well in assessing lung cancer risk on follow-up CT scans, with a weighted cancer risk of 5% for new nodules. Strict application of Lung-RADS criteria downgraded some malignant nodules, highlighting the need for category 4X for suspicious features.
A study of 5.9 million radiological examinations found no significant difference in major and minor discrepancy rates for acute community setting exams between concordant and discordant with radiologists' fellowship training. However, advanced examination discrepancies were higher when concordant with radiologists' fellowship training.
A recent study published in the American Journal of Roentgenology found a link between COVID-19 mRNA vaccination and myocarditis in adolescent males. Cardiac MRI was used to assess suspected myocarditis post-vaccination, with late gadolinium enhancement persisting in two patients undergoing repeat MRI.
Researchers have demonstrated a new technique for cross-sectional medical images without the need for tomography, enabling faster and more accurate imaging. The breakthrough is made possible by ultrafast photon detectors that can precisely determine the arrival times of photons, allowing for reconstruction-free positron emission imaging.
A deep learning algorithm from VUNO improved radiologists' sensitivity for detecting reticular opacity on chest radiographs in patients with mild to severe interstitial lung disease. The algorithm accuracy reached 98.5% and improved interobserver agreement, suggesting its potential for screening suspected ILD patients.
Researchers at UC San Diego will document brain development from birth through early childhood, focusing on environmental factors and mental disorders. The HBCD study aims to optimize brain imaging technologies and predict future behavioral problems.
Left atrium emptying fraction derived from cardiac CTA improves predictive performance of established clinical risk scores. A reduced LAEF independently predicts all-cause mortality within 24 months post-procedure.
A retrospective cohort study of 27 patients with sarcoma lung metastases found high primary technical success rates for percutaneous image-guided microwave and cryoablation. The treatment modality and tumor location did not affect local progression, and smaller tumors showed lower cumulative incidence of local progression.
A prospective study found that liver stiffness measurements obtained by shear-wave elastography decreased significantly after image-guided intervention for chronic Budd-Chiari syndrome in children. Disease recurrence was typically associated with an increase in liver stiffness measurements compared to prior assessments.
A new AI system developed by NYU and NYU Abu Dhabi researchers achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. The system helps decrease false-positive findings and requested biopsies while maintaining sensitivity.
A recent study published in AJR found that gallbladder polyps can fluctuate significantly in size and number over time. The authors suggest revising guidelines to reflect this variability, with no cases of gallbladder carcinoma identified in patients who underwent cholecystectomy.
A multidisciplinary organization has reached consensus on guidelines for performing, interpreting, and reporting MR defecography. The consensus templates aim to standardize care for patients with evacuation disorders of the pelvic floor.
A new paradigm in breast cancer screening has been established with immediate-read screenings during the pandemic. This approach reduces racial and ethnic disparities in same-day diagnostic imaging, while speeding up diagnoses.
A new study uses deep learning to predict breast cancer risk from mammograms, outperforming clinical risk factors in distinguishing between women who will develop cancer and those who won't. The findings have significant implications for breast cancer management, suggesting a potential role for AI as a second reader for radiologists.
The new QIBA profile provides a framework for evaluating the reliability of cartilage compositional imaging measurements, including T1rho and T2 mapping. It establishes minimum detectable changes in these values, enabling early detection of cartilage abnormalities and potentially preventing osteoarthritis.
A single-center retrospective study found that preoperative thoracic CT findings can complement preoperative clinical risk factors to improve risk assessment for the need of postoperative mechanical ventilation. Bronchial wall thickening, pericardial effusion, and anteroposterior chest diameter were identified as independent predictors...
A new 3D spiral gradient recalled echo (GRE) sequence has been developed to overcome conventional challenges in head and neck MRI. This sequence achieves improved image quality and contrast-to-noise ratio compared to traditional Cartesian sequences, while reducing scan time by up to 44.6%.
A deep learning tool for sarcopenia assessment on CT scans shows similar utility in predicting hip fractures and death at both L1 and L3 vertebral levels. Muscle attenuation measurements performed better than muscle area assessments, likely due to the inclusion of intramuscular fat.
A single-center retrospective study found that AI-CAD reduced recall rates and improved accuracy compared to digital mammography (DM) and DBT. The study showed lower recall rates and higher accuracy for DM with AI-CAD, indicating a practical addition to lowering false-positive findings.
Researchers found that a mobile interventional stroke team treated patients faster and with better outcomes than standard care. Patients treated by the MIST team were more likely to be functionally independent at 90 days post-stroke.
A retrospective study of over 3,900 patients found that warming iohexol 350 contrast media did not significantly reduce adverse reactions or extravasations. The results suggest that maintaining the agent at room temperature is non-inferior to warming it to body temperature before injection.
King's College London researchers have automated brain MRI image labelling by leveraging radiology reports, enabling the labelling of over 100,000 exams in less than half an hour. This breakthrough tackles a bottleneck in deep learning image recognition tasks for complex datasets like MRI.
A new AI algorithm developed by TUM and Imperial College London can detect pneumonia in pediatric x-ray images while maintaining patient privacy. The algorithm uses federated learning, secure aggregation, and differential privacy to keep data onsite and prevent individual identification.
A new AI algorithm accurately predicts lung cancer risk from low-dose CT scans, outperforming established models and human clinicians. The algorithm may aid in optimizing follow-up recommendations for lung cancer screening participants.
A retrospective study found that CT features can help select patients with stage IA non-small cell lung cancer for sublobar resection. Peritumoral interstitial thickening and pleural contact were independently associated with pathologic lymphovascular invasion, predicting recurrence-free survival after the procedure.
A new AI tool developed by NYU Langone Health can accurately predict which COVID-19 patients are most likely to develop life-threatening complications within four days, using chest X-ray images and other clinical data. The tool achieved an 80% accuracy rate in predicting patients who required intensive care or died from their infections.
The American Roentgen Ray Society (ARRS) honors all members with the 2021 ARRS Gold Medal for their selfless service on the frontlines of the COVID-19 pandemic. The award recognizes their distinguished contributions to radiology and its impact on patient care.
A study found that a chest x-ray scoring system can predict patient outcomes, including death, intubation, and chronic renal replacement therapy, in COVID-19 patients with high pretest probability or rapid test results. The scoring system was accurate and reliable, even among junior residents.
A study found that radiology residency program directors will likely rely on USMLE Step 2 Clinical Knowledge scores as an objective metric for applicant screening. Program directors are unsure whether to require Step 2 CK scores at application or before interviewing applicants.
A large randomized controlled trial found that low-dose IV contrast-enhanced 2-millisievert CT is comparable to conventional-dose CT for diagnosing right colonic diverticulitis. The study included 3,074 patients and demonstrated similar diagnostic performance between the two groups.
A study published in Radiology found that chest CT scans can assess body composition and predict mortality risk in people with chronic obstructive pulmonary disease (COPD). Higher intermuscular fat was linked to higher mortality rates, while subcutaneous adipose tissue was associated with lower risks.
The COVID-19 'Safer at Home' order led to a 40% decline in total radiology orders, with outpatient volumes decreasing by 67% and ED volumes declining by 21%. Subspecialty imaging saw significant declines, especially in dexa and breast imaging.
A study reviewed 286 patients who underwent ICD implantation between 2013 and 2016, assessing appropriateness based on AUC. The results showed a high rate of appropriate ICD implantations and very low prevalence of rarely appropriate implantations.
A large-scale study found that the introduction of RADLogics' AI-Powered solution into radiology workflow reduced report turnaround time by an average of 30 percent, equivalent to 7 minutes per case. The study also showed a significant improvement in productivity and report accuracy.
Researchers found that people searching for objects in 3D image stacks are less successful than those searching for the same in single 2D images. This is due to a bottleneck in human vision and cognition, where humans under-explore 3D search areas and rely on peripheral visual processing.
Genicular artery embolization (GAE) significantly reduces inflammation, improving quality of life for people with moderate to severe knee pain. The procedure provides long-term, safe pain relief, with benefits seen as early as three days post-treatment and lasting up to one year.
A new study found that hysterectomies are used more frequently to treat significant postpartum bleeding than uterine artery embolization, a minimally invasive procedure. The procedure resulted in longer hospital stays and higher costs.
Researchers have developed Immuno-PET, a technique that visualizes changes in different cancer receptors within tumors during targeted therapies, enabling early evaluation of treatment effectiveness and prediction of response. This can help physicians select the most effective treatment for patients with cancer.
A new study published in the American Journal of Roentgenology found that CT colonography with a 10 mm threshold is the most effective and efficient non-invasive screening test for colorectal cancer prevention and detection. This method targets advanced neoplasia while reducing unnecessary colonoscopies compared to other tests.
Swollen lymph nodes after COVID-19 vaccination can be managed with proper documentation and follow-up care. Imaging centers should document vaccination information to avoid unnecessary biopsies.
A machine learning algorithm helps differentiate between benign and premalignant polyps in CT colonography data, with a sensitivity of 82% and specificity of 85%. The findings suggest a role for machine learning-derived algorithms in boosting the effectiveness of CT colonography as a screening tool for colorectal cancer.
Radiological images confirm that COVID-19 can trigger autoimmune reactions leading to rheumatoid arthritis flares and other musculoskeletal disorders. Imaging helps explain prolonged symptoms and directs patients to the right physician for treatment.
Sanjeev Bhalla, MD, has been recognized as the 2021 ARRS Distinguished Educator for his proven record of improving radiological education. He is a dedicated teacher and innovator in medical imaging.
Research suggests that radiologists' diagnoses of lumbar spine pain generators are highly accurate when using patient-reported symptom information from brief questionnaires. The study found almost perfect agreement between radiologists' diagnoses using symptom information and those without it, improving diagnostic certainty levels.
Radiologists question the status quo to spark positive dialog and improve patient care. Featured articles debate the use of physician extenders and incorporation of AI algorithms into daily practice.
Drs. Kelil and Jaimes will investigate personalized breast cancer risk assessment and fetal diffusion tensor imaging to improve conventional risk prediction models and characterize normal brain development in fetuses with congenital heart disease. Their studies aim to harness cutting-edge MRI processing tools to correct for fetal motion.
A review article highlights the need for improved understanding and prevention of adverse events in interventional radiology. Most procedures are successful, but complications can lead to significant morbidity and increased risk of harm.