Quantitative Coronary Plaque Analysis (QCPA): promise, but also important pitfalls — a critical reading of the 2025 ACC Scientific Statement The 2025 ACC Scientific Statement on Quantitative Coronary Plaque Analysis (QCPA) is an important step toward integrating AI-driven plaque quantification into clinical cardiology. However, the document also implicitly reveals several major weaknesses and unresolved pitfalls that deserve explicit attention before widespread adoption  . 1. Reproducibility across platforms remains a major Achilles’ heel QCPA results are not interchangeable across vendors or software versions. Different algorithms use distinct lumen–wall segmentation methods, HU thresholds, training datasets, and post-processing rules. As a result, total plaque volume and plaque composition can differ substantially between platforms—even when analyzing the same scan. True cross-vendor standardization is still lacking. 2. CT acquisition parameters strongly influence plaque quantification Plaque volumes and composition are highly sensitive to scan parameters, including: Tube voltage (kVp) Reconstruction kernel and slice thickness Iterative vs deep-learning reconstruction Scanner generation (energy-integrating vs photon-counting CT) Small technical differences can translate into artificial changes in plaque volume or composition, particularly for non-calcified and low-attenuation plaque—precisely the components most often linked to risk. 3. Serial QCPA is biologically appealing but methodologically fragile The statement rightly discourages routine serial QCPA. Even with the same patient, apparent plaque “progression” may reflect technical variability rather than biology, unless acquisition and reconstruction are rigorously matched. Proposed progression thresholds (e.g. ΔTPV 10–20 mm³/year) remain expert opinion, not outcome-validated cut-offs. 4. Risk of overcalling subvisual plaque This is particularly tricky since conventional CT technology (with few exception in the current market namely Cleerly) is not able to properly set the lower threshold for normal coronary artery wall volume. AI-based QCPA frequently detects plaque volumes below visual thresholds. The clinical significance of minimal plaque (<20–30 mm³) is unknown and risks overdiagnosis, overtreatment, and patient anxiety, especially in low-risk populations. 5. Outcome evidence is still indirect While plaque burden correlates with risk, no randomized trial has yet demonstrated that QCPA-guided management improves outcomes beyond standard CTA interpretation, CAC scoring, and clinical risk assessment. Bottom line: QCPA is a powerful research and risk-phenotyping tool, but today it remains technically sensitive, platform-dependent, and insufficiently standardized. Until reproducibility, calibration, and outcome validation improve, QCPA should be used selectively, cautiously, and always interpreted in clinical context—not as a standalone decision-maker.