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CardioNerds Dr. Joseph Kassab, Dr. Mariana Garcia-Arango, and Dr. Christopher Mason explore the technological revolution of Coronary CT Angiography (CCTA) with expert faculty Dr. Michael Gallagher. The discussion details how CCTA has evolved into a frontline diagnostic and preventive tool, moving beyond simple anatomy to incorporate physiology via CT-FFR and biology through AI-driven plaque quantification. The episode reviews landmark evidence like the SCOT-HEART and PROMISE trials, the nuances of CAD-RADS 2.0 reporting, and the emerging role of AI in monitoring treatment response and personalizing cardiovascular care. Critically, they also discuss some of the assumptions and limitations of these techniques.

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This episode was supported by an independent medical education grant from HeartFlow. All CardioNerds education is planned, produced, and reviewed solely by CardioNerds. 

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Pearls

1. Shift in Paradigm: CCTA is no longer just an anatomic test; with some key limitations, it can provide anatomy, physiology (CT-FFR), and plaque biology (AI-CPA) in a single non-invasive scan.
2. The “Power of Zero” vs. Plaque: While a normal CCTA has a >95% negative predictive value, future MIs often arise from non-obstructive plaque that traditional stress tests might miss.
3. CAD-RADS 2.0 Utility: The addition of plaque burden modifiers (P1–P4) is a “game changer,” allowing clinicians to identify high-risk patients who need aggressive lipid-lowering despite having only mild stenosis.
4. CT-FFR as a Virtual Stress Test: CT-FFR uses computational fluid dynamics to simulate blood flow, potentially reducing unnecessary invasive catheterizations by approximately 61% without sacrificing safety.
5. Seeing the Invisible: AI-based quantitative plaque analysis (QCPA) can identify “subvisual” plaque and low-attenuation (lipid-rich) components that are the primary drivers of acute coronary syndromes.

Show Notes

1. How has the role of CCTA changed compared to traditional functional testing?

• Historically, stress testing answered “is there ischemia today?”, which often reflects late-stage disease.
• CCTA identifies disease across the entire spectrum, asking “is there atherosclerosis and how much plaque is present?”.
• Landmark evidence: SCOT-HEART showed a 41% relative risk reduction in MI at 5 years attributed to intensified preventive therapies, and PROMISE showed CCTA was better at selecting patients who truly needed invasive angiography.
• Diagnostic CCTA imaging depends on the protocol, contrast timing, heart rate, heart rhythm, breathholding, scanner quality, and several patient factors (obesity, prior stents, heavy calcification, complex bypass anatomy, and motion artifact all may limit imaging). “CCTA is exceptional for the right patient, with the right scanner, and the right team.”

2. What are the key modifiers introduced in CAD-RADS 2.0, and why do they matter?

• CAD-RADS 2.0 moved beyond stenosis severity to include plaque burden (P0 to P4), high-risk plaque (HRP) features, and the presence of ischemia based on CT-FFR.
• It serves as a clinical decision support tool: a patient with mild (25-49%) stenosis but “extensive” (P4) plaque burden is considered high risk and warrants aggressive risk factor modification.

3. How is CT-FFR calculated, and when is it most useful in clinical practice?

• CT-FFR uses resting CCTA data and computational fluid dynamics to create a 3D model of coronary flow during simulated maximal hyperemia.
• It is often used for intermediate lesions (40–90% stenosis) to predict if they are  ischemia-producing, guiding the decision whether to proceed with invasive angiography. 
• The assumptions necessary for this computational modeling may not apply well to patients with microvascular dysfunction, significant myocardial scar or prior infarction, or ventricular hypertrophy. Still, data indicate that CT-FFR performs similarly to PET in predicting hemodynamically significant lesions. 
• CT-FFR performs well at the extremes (either clearly normal or clearly abnormal). Accuracy dips, however, in the intermediate range (~0.75-0.80), where decision-making is most critical. In this grey zone, additional factors can help guide the approach, including the amount of myocardium supplied, translesional gradient, and plaque features.  
• CT-FFR has not been validated in distal segments, stented segments, heavily calcified coronary arteries, or in patients with severe aortic stenosis. Caution with CT-FFR should be utilized in very calcified coronary segments. 

4. What is AI-based quantitative plaque analysis (QCPA), and what metrics are ready for clinical use?

• This is potentially a paradigm shift, moving away from stenosis-centric thinking to a more disease burden and plaque biology focus.
• QCPA uses deep learning algorithms to automatically segment the vessel wall and quantify plaque volume in mm³.
• Ready for “prime time” metrics include: Total Plaque Volume (TPV), non-calcified plaque volume, and Low-Attenuation Plaque (LAP) burden.

5. Can serial CCTA be used to monitor the effectiveness of medical therapies like statins?

• While not yet a routine guideline-driven practice, trials like PARADIGM and EVAPORATE show that therapies can stabilize plaque; notably, CCTA is better for monitoring than CAC scores, which can be misleading as statins often increase plaque calcification as part of the stabilization process.
• There are no randomized trials that serial CCTAs improve outcomes. Cost and radiation exposure will be notable limitations. Serial scan timing, scan acquisition and interpretation standardization would be key.
• Dr. Gallagher notes that we are moving toward a world in which plaque burden may become a “treatment biomarker,” similar to tumor burden in oncology. 

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References

1. Coronary Computed Tomography Angiography From Clinical Uses to Emerging Technologies: JACC State-of-the-Art Review. Abdelrahman KM, Chen MY, Dey AK, et al. Journal of the American College of Cardiology. 2020;76(10):1226-1243. doi:10.1016/j.jacc.2020.06.076.

2. Non-Invasive Imaging in Coronary Syndromes: Recommendations of the European Association of Cardiovascular Imaging and the American Society of Echocardiography, in Collaboration With the American Society of Nuclear Cardiology, Society of Cardiovascular Computed Tomography, and Society for Cardiovascular Magnetic Resonance. Edvardsen T, Asch FM, Davidson B, et al. Journal of the American Society of Echocardiography : Official Publication of the American Society of Echocardiography. 2022;35(4):329-354. doi:10.1016/j.echo.2021.12.012.

3. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Gulati M, Levy PD, Mukherjee D, et al. Journal of the American College of Cardiology. 2021;78(22):e187-e285. doi:10.1016/j.jacc.2021.07.053.

4. Contemporary, Non-Invasive Imaging Diagnosis of Chronic Coronary Artery Disease. van der Bijl P, Gulati M, Saraste A, et al. Lancet (London, England). 2025;406(10519):2577-2587. doi:10.1016/S0140-6736(25)01586-7.

5. State of the Art: Evaluation and Medical Management of Nonobstructive Coronary Artery Disease in Patients With Chest Pain: A Scientific Statement From the American Heart Association. Slipczuk L, Blankstein R, Bucciarelli-Ducci C, et al. Circulation. 2025;152(23):e443-e466. doi:10.1161/CIR.0000000000001394.

6. Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography: The ACCURATE-CT Study. Li C, Hu Y, Jiang J, et al. JACC. Cardiovascular Interventions. 2024;17(17):1980-1992. doi:10.1016/j.jcin.2024.06.027.

7. Clinical Outcomes Based on Coronary Computed Tomography-Derived Fractional Flow Reserve and Plaque Characterization. Sato Y, Motoyama S, Miyajima K, et al. JACC. Cardiovascular Imaging. 2024;17(3):284-297. doi:10.1016/j.jcmg.2023.07.013.

8. Clinical Use of Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve: Expert Consensus by an International Working Group. Tang CX, Leipsic JA, Nørgaard BL, et al. European Radiology. 2026;:10.1007/s00330-025-12313-6. doi:10.1007/s00330-025-12313-6.

9. Diagnostic accuracy of computed tomography–derived fractional flow reserve: a systematic review. Cook CM, Petraco R, Shun-Shin MJ, et al. JAMA Cardiol. 2017;2(7):803-810. Doi:10.1001/jamacardio.2017.1314

10. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). Nørgaard BL, Leipsic J, Gaur S, et al. J Am Coll Cardiol. 2014;63(12):1145-1155. Doi:10.1016/j.jacc.2013.11.043

11. Comparison of coronary computed tomography angiography, fractional flow reserve, and perfusion imaging for ischemia diagnosis. Driessen RS, Danad I, Stuijfzand WJ, et al. J Am Coll Cardiol. 2019;73(2):161-173. Doi:10.1016/j.jacc.2018.10.056.

12. 1-year outcomes of FFRCT-guided care in patients with suspected coronary disease: the PLATFORM study. Douglas PS, De Bruyne B, Pontone G, et al. J Am Coll Cardiol. 2016;68(5):435-445. Doi:10.1016/j.jacc.2016.05.057.

13. Comparison of an initial risk-based testing strategy vs usual testing in stable symptomatic patients with suspected coronary artery disease: the PRECISE randomized clinical trial. Douglas PS, Nanna MG, Kelsey MD, et al; PRECISE Investigators. JAMA Cardiol. 2023;8(10):904-914. Doi:10.1001/jamacardio.2023.2595.

14. Diagnostic and clinical value of FFRCT in stable chest pain patients with extensive coronary calcification: the FACC study. Mickley H, Veien KT, Gerke O, et al. JACC Cardiovasc Imaging. 2022;15(6):1046-1058. doi:10.1016/j.jcmg.2021.12.010.

15. Low-Attenuation Noncalcified Plaque on Coronary Computed Tomography Angiography Predicts Myocardial Infarction: Results From the Multicenter SCOT-HEART Trial (Scottish Computed Tomography of the HEART). Williams MC, Kwiecinski J, Doris M, et al. Circulation. 2020;141(18):1452-1462. doi:10.1161/CIRCULATIONAHA.119.044720.

16. AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD. Nurmohamed NS, Bom MJ, Jukema RA, et al. JACC. Cardiovascular Imaging. 2024;17(3):269-280. doi:10.1016/j.jcmg.2023.05.020.

17. Interaction of AI-Enabled Quantitative Coronary Plaque Volumes on Coronary CT Angiography, FFRCT, and Clinical Outcomes: A Retrospective Analysis of the ADVANCE Registry. Dundas J, Leipsic J, Fairbairn T, et al. Circulation. Cardiovascular Imaging. 2024;17(3):e016143. doi:10.1161/CIRCIMAGING.123.016143.

18. Prognostic Value of AI-Based Quantitative Coronary CTA vs Human Reader-Based Visual Assessment: Results From the CONFIRM2 Registry. van Rosendael A, Nakanishi R, Bax JJ, et al. JACC. Cardiovascular Imaging. 2026;19(3):345-359. doi:10.1016/j.jcmg.2025.09.021.
13. Pericoronary Adipose Tissue as a Marker of Cardiovascular Risk: JACC Review Topic of the Week. Tan N, Dey D, Marwick TH, Nerlekar N. Journal of the American College of Cardiology. 2023;81(9):913-923. doi:10.1016/j.jacc.2022.12.021.

19. Effect of Icosapent Ethyl on Progression of Coronary Atherosclerosis in Patients With Elevated Triglycerides on Statin Therapy: Final Results of the EVAPORATE Trial. Budoff MJ, Bhatt DL, Kinninger A, et al. European Heart Journal. 2020;41(40):3925-3932. doi:10.1093/eurheartj/ehaa652.

20. Coronary CT Angiography Evaluation With Artificial Intelligence for Individualized Medical Treatment of Atherosclerosis: A Consensus Statement From the QCI Study Group. Schulze K, Stantien AM, Williams MC, et al. Nature Reviews. Cardiology. 2026;23(2):100-115. doi:10.1038/s41569-025-01191-6.