There is nothing incidental about the frequency of incidental findings seen in wide field-of-view 64-slice cardiac imaging. A study by Dr. Joshua Macatol, a radiology researcher at William Beaumont Medical Center in Royal Oak, MI, found that dozens of noncoronary findings may go undetected, however, as cardiologists focus on possible coronary artery disease.
There is nothing incidental about the frequency of incidental findings seen in wide field-of-view 64-slice cardiac imaging. A study by Dr. Joshua Macatol, a radiology researcher at William Beaumont Medical Center in Royal Oak, MI, found that dozens of noncoronary findings may go undetected, however, as cardiologists focus on possible coronary artery disease.
The frequency of incidental disease does not vary significantly between symptomatic and asymptomatic patients. A retrospective review uncovered incidental findings in 46% of the 64-slice cardiac CT studies performed on 224 symptomatic patients and 42% of 19 asymptomatic patients. Overall, 37% of the studies produced at least one noncoronary finding, Macatol said.
Major findings included:
Minor findings included:
Of the symptomatic patients, 10% had major findings and 36% had minor findings. Of the asymptomatic patients, 4% had major findings and 37% had minor findings, Macatol said.
These results bolster arguments in favor of radiologist involvement in cardiac CT interpretation, according to Dr. George Hartnell, director of cardiovascular and interventional radiology at Bay State Medical Center in Springfield, MA. It is important for radiologists to be involved because they will detect things nonradiologists will miss.
"Although the frequency of important abnormalities is relatively small, detecting them saves lives," he said.
At William Beaumont, the responsibility for reading cardiac CT is divided between cardiologists and radiologists. The cardiologist handles the coronary artery portion, then the radiology follows up, looking for other disease, Macatol said. The two groups have informally worked out a formula on how to divide fees from the readings.
When asked by Dr. David Levin, professor emeritus of radiology at Thomas Jefferson University, if he considered the arrangement equitable, Macatol answered, "No comment."
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
Could Lymph Node Distribution Patterns on CT Improve Staging for Colon Cancer?
April 11th 2025For patients with microsatellite instability-high colon cancer, distribution-based clinical lymph node staging (dCN) with computed tomography (CT) offered nearly double the accuracy rate of clinical lymph node staging in a recent study.