Monday, December 2 | 10:30 a.m.-10:40 a.m. | SSC01-01 | Room S402AB
Accounting for fractional flow reserve CT (FFR-CT) when evaluating the coronary CT angiography (CCTA) scans of patients with chest pain could improve patient classification and lower the rate of follow-up examinations, according to this study to be presented on Monday.Clinicians are increasingly relying on Coronary Artery Disease Reporting and Data Systems (CAD-RADS) to decide whether emergency patients who present with acute chest pain might require additional testing beyond diagnostic CCTA, Dr. Simon Martin from the Medical University of South Carolina told AuntMinnie.com. FFR-CT analysis provides further support to that end, but the test is not currently included in CAD-RADS.
"The aim of this study was to investigate the impact of FFR-CT derived from CCTA on CAD-RADS stratifications in patients presenting with acute chest pain," he said.
Martin and colleagues examined the cases of 94 emergency patients with chest pain who underwent a CCTA exam at their institution, with subsequent FFR-CT analysis. They found that FFR-CT led to the reclassification of more than half of all patients initially categorized as CAD-RADS 3 (having moderate stenosis and requiring functional assessment) using CCTA alone. Several of the patients were reclassified as CAD-RADS 4 (having severe stenosis and requiring intervention), though most were reclassified as CAD-RADS 2 (having mild stenosis and not requiring further testing).
Overall, these changes may have decreased the rate of additional diagnostic testing in the patient cohort by roughly 45%.
"Adding FFR-CT analysis in these patients substantially decreases equivocality in CCTA interpretation, drastically reduces CAD-RADS 3 classifications, and has potential to obviate unnecessary downstream testing," Martin said. "One should consider an update to the CAD-RADS classification to account for the availability of FFR-CT."


















![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)

