A Fully Automated Deep Learning Model for Quantifying Coronary Plaque at Coronary CT Angiography

Background Deep learning (DL) models for quantifying plaques at coronary CT angiography (CCTA) are rarely used in routine clinical care. Purpose To develop a fully automated DL model for coronary plaque quantification and to evaluate its prognostic value. Materials and Methods Patients who underwent CCTA were retrospectively enrolled from 17 Chinese hospitals between June 2009 and May 2024. The imaging data of these patients were randomly split into training and validation sets at a 7:3 ratio to develop a fully automated DL model for quantifying plaque volume (PV), PlaqueSegNet, which was subsequently externally tested with four independent datasets: a paired CCTA and intravascular US (IVUS) dataset, a subset of the China CT-derived fractional flow reserve (CT-FFR) study 3 dataset collected with different CT scanners, a serial CCTA dataset within a 3-month interval, and a photon-counting CT dataset. The prognostic value of PlaqueSegNet was evaluated using the Harrell C-index in three cohorts: China CT-FFR study 2, China CT-FFR study 1.1, and a serial CCTA cohort. Results The training dataset included 1409 patients (mean age, 63 years ± 10 SD; 795 male), and the internal validation dataset included 604 patients (mean age, 63 years ± 10; 329 male). PlaqueSegNet demonstrated excellent agreement and reproducibility for quantifying PV against IVUS and expert readers across the four external datasets (all intraclass correlation coefficients, >0.90), albeit with wide limits of agreement in Bland-Altman analysis. The C-index of PlaqueSegNet for predicting major adverse cardiac events (MACEs) was 0.64 (95% CI: 0.62, 0.67) in the China CT-FFR study 2 (median follow-up, 2.3 years), 0.65 (95% CI: 0.60, 0.69) in the China CT-FFR study 1.1 (median follow-up, 5.3 years), and 0.74 (95% CI: 0.66, 0.84) in the serial CCTA cohort (median follow-up, 3.6 years). Conclusion PlaqueSegNet provided fully automated measurements of PV from CCTA that closely agreed with expert readers and IVUS and carried prognostic value for future MACEs. Clinical trial registration no. NCT06025305 © RSNA, 2026 Supplemental material is available for this article. See also the editorial by Williams in this issue.