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Noting that the machine learning model incorporating magnetic resonance imaging (MRI) had a higher mean area under the curve (AUC) than a model based solely on clinical features for predicting hepatocellular carcinoma recurrence, researchers said the study findings could have implications for refining liver transplant criteria.

In lieu of a “finishing school” for rads, this author says subtle course correction can prevent descent into rabbit holes of negativity.

Catch up on the top radiology content of the past week.

In comparison to neuroradiology assessment of brain magnetic resonance imaging (MRI) scans for tumor diagnosis, researchers found that adjunctive use of a deep learning system improved diagnostic accuracy by 12.4 percent and sensitivity by 33.5 percent in one test set of 300 patients.

Emerging research from a positron emission tomography (PET) study suggests that prior COVID-19 infection can lead to a 30 percent increased risk of lower myocardial flow reserve in patients with cardiovascular risk factors ranging from diabetes to coronary artery disease.

Emerging research revealed that Asian patients, Black patients, and those who identified their race as “other” were nearly 1.5 times more likely than White women to have more than two-month delays with follow-up imaging after BI-RADS 0 screening mammography.

In a recent survey of nearly 2,000 women in their 40s who had no history of breast cancer, researchers found that over 38 percent of survey respondents said they had no reason to get a mammogram or had never thought about it.

With ongoing gaps in mammography screening and patient anxiety that often accompanies screening exams and a possible diagnosis of breast cancer, patient education is critical. Accordingly, in a recent video interview, Amy K. Patel, M.D., discussed the potential impact of new patient-oriented breast cancer screening guidelines from the National Comprehensive Cancer Network.

The combination of magnetic resonance imaging (MRI) and computed tomography (CT) had a 63.83 percent sensitivity rate for tumor-infiltrated axillary lymph nodes in patients with breast cancer in comparison to a 36.11 percent sensitivity rate for the combination of mammography and sonography.

While a lack of detail with patient histories is a common challenge in radiology, a dash of humorous perspective can help build a sense of camaraderie among colleagues.

Catch up on the top radiology content of the past week.

Published by the American College of Cardiology, the expert recommendations offer insights on the role of intravascular ultrasound for diagnosing and treating peripheral vascular disease in the lower extremity.

The Maestro Brain Model reportedly provides automated identification, quantification and labeling of brain structures on magnetic resonance imaging (MRI).

Emerging research suggests the machine learning-based DiaBeats algorithm could facilitate early detection of prediabetes or diabetes.

In a new study, researchers examined trends with diversity in the radiology workforce, offering a closer look at the gender, race and ethnic makeup of radiology residency programs and academic faculty.

While recent recommendations from the United States Preventive Services Task Force (USPSTF) to lower lung cancer screening thresholds significantly expanded eligibility for screening tests such as low-dose computed tomography (CT), differences in education, health-care insurance and proximity to health-care facilities continue to be key drivers of racial and socioeconomic disparities limiting access to appropriate preventive care.

What is the diagnosis?

The ProstatID, an adjunctive artificial intelligence software that radiologists can utilize with traditional magnetic resonance imaging (MRI), reportedly measures prostate gland volume, and suggests PI-RADS scoring of suspicious lesions.

Consistently leveling with colleagues, patients and others can promote a relatively straightforward path to optimal goodwill.

Catch up on the top radiology content of the past week.

What is the diagnosis?

Researchers discuss key parameters for the assessment, implementation and post-implementation monitoring of emerging artificial intelligence (AI) tools in radiology practices large and small.

A recent study found the use of an alert and a request for more clinical information in a multisite health system’s electronic health record (EHR) system led to a 12 percent reduction in contrast-enhanced computed tomography (CT) exams per day and a 15.2 percent reduction in orders for CT with contrast media per day.

The Definium 656 HD fixed X-ray system reportedly features enhanced, artificial intelligence (AI)-driven image processing, facilitates radiology workflows, and reduces patient positioning time.

Preliminary research revealed an area under the curve (AUC) of 85 percent for an artificial intelligence (AI) algorithm in diagnosing COVID-19 on initial chest X-rays in comparison to a consensus 71 percent AUC for five radiologists.

Amid a sea of radiology staffing shortages, a global pandemic, and national issues, taking time out for ongoing self-care is more critical than ever to mitigate potential burnout.

Viz.ai said the Viz Subdural Hematoma (SDH) artificial intelligence (AI) algorithm provides automatic detection of acute and chronic subdural hemorrhages, facilitating timely triage and treatment of patients.

Has an overly critical approach from quality assurance committees led to an overly cautious approach in reports from teleradiologists?