Imaging method reduced non-confident reads, decreased time.
Coronary computed tomography angiography (CCTA) plus computed tomography-derived fractional flow reserve (FFRCT) improved assessment of coronary artery disease (CAD) severity, according to a study published in the journal European Radiology.
Researchers from the United States and Germany performed a quality-improvement study to assess the impact of FFRCT on reader confidence and interpretation reader time of CCTA.
Fifty patients, 23 of whom were women, participated in the study. Mean age was 67 years and body mass index 28.7 kg/m2. CCTA was acquired on 2nd and 3rd generation dual-source MDCT with use of beta-blockers and nitroglycerin. Four readers participated, two with experience level COCATS2 (Core- Cardiology-Training-Symposium) and two with COCATS3 assessed severity of epicardial CAD using CCTA alone and CCTA with FFRCT. They rated reader confidence for CAD and the four major epicardial coronary artery vessels were measured for hemodynamically significant stenosis (HS) on a 4-point Likert-scale, ranging from 1 being high and 4 being none. Time to interpret was also recorded.
Related article: Coronary CTA really works, but why isn't its use soaring?
The results showed the severity of CAD in the cohort population as per the CAD-RADS (Coronary-Artery-Disease Reporting-and-Data-System) was:
Sixty-three CA in 30 patients had minimal FFRCT values of 0.8 or less. Reader confidence when using FFRCT increased for CAD and HS. A reduction of non-confident patient read by 27% for rank 3 and 75% for rank 4. The change in confidence was associated with reader experience, but not CAD-RADS. The median time-to-read a CCTA study decreased by 5 minutes when FFRCT was available.
The researchers concluded that there was improved reader assessment for assessment of severity of CAD and HS when CCTA was used in conjunction with FFRCT. There was a reduction of non-confident reads and there was a decrease in the median time-to-interpretation of a CCTA.
AI Algorithm Comparable to Radiologists in Differentiating Small Renal Masses on CT
May 14th 2024An emerging deep learning algorithm had a lower AUC and sensitivity than urological radiologists for differentiating between small renal masses on computed tomography (CT) scans but had a 21 percent higher sensitivity rate than non-urological radiologists, 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.
FDA Clears AI-Powered Qualitative Perfusion Mapping for Cone-Beam CT
May 6th 2024Reportedly validated in more than 10 clinical trials, the AngioFlow perfusion imaging software enables timely identification of brain regions with cerebral blood flow reduction and those with significant hypoperfusion.
Can a CT-Based Radiomics Model Bolster Detection of Malignant Thyroid Nodules?
May 3rd 2024A computed tomography (CT)-based radiomics model that includes 28 radiomic features showed significantly higher accuracy, sensitivity, and specificity than conventional CT in differentiating benign and malignant thyroid nodules, according to newly published research.