Dataset Information

Prospective deep learning-based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study.

ABSTRACT:

Aims

Coronary computed tomography angiography provides non-invasive assessment of coronary stenosis severity and flow impairment. Automated artificial intelligence (AI) analysis may assist in precise quantification and characterization of coronary atherosclerosis, enabling patient-specific risk determination and management strategies. This multicentre international study compared an automated deep learning-based method for segmenting coronary atherosclerosis in coronary computed tomography angiography (CCTA) against the reference standard of intravascular ultrasound (IVUS).

Methods and results

The study included clinically stable patients with known coronary artery disease from 15 centres in the USA and Japan. An AI-enabled plaque analysis was utilized to quantify and characterize total plaque (TPV), vessel, lumen, calcified plaque (CP), non-calcified plaque (NCP), and low-attenuation plaque (LAP) volumes derived from CCTA and compared with IVUS measurements in a blinded, core laboratory-adjudicated fashion. In 237 patients, 432 lesions were assessed; mean lesion length was 24.5 mm, and mean IVUS-TPV was 186.0 mm3. AI-enabled plaque analysis on CCTA showed strong correlation and high accuracy when compared with IVUS; correlation coefficient, slope, and Y intercept for TPV were 0.91, 0.99, and 1.87, respectively; for CP volume 0.91, 1.05, and 5.32, respectively; and for NCP volume 0.87, 0.98, and 15.24, respectively. Bland-Altman analysis demonstrated strong agreement with little bias for these measurements.

Conclusion

AI-enabled CCTA quantification and characterization of atherosclerosis demonstrated strong agreement with IVUS reference standard measurements. This tool may prove effective for accurate evaluation of coronary atherosclerotic burden and cardiovascular risk assessment.

SUBMITTER: Narula J

PROVIDER: S-EPMC11346368 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

Prospective deep learning-based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study.

Narula Jagat J Stuckey Thomas D TD Nakazawa Gaku G Ahmadi Amir A Matsumura Mitsuaki M Petersen Kersten K Mirza Saba S Ng Nicholas N Mullen Sarah S Schaap Michiel M Leipsic Jonathan J Rogers Campbell C Taylor Charles A CA Yacoub Harout H Gupta Himanshu H Matsuo Hitoshi H Rinehart Sarah S Maehara Akiko A

European heart journal. Cardiovascular Imaging 20240801 9

<h4>Aims</h4>Coronary computed tomography angiography provides non-invasive assessment of coronary stenosis severity and flow impairment. Automated artificial intelligence (AI) analysis may assist in precise quantification and characterization of coronary atherosclerosis, enabling patient-specific risk determination and management strategies. This multicentre international study compared an automated deep learning-based method for segmenting coronary atherosclerosis in coronary computed tomograp ...[more]

PMID: 38700097

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Project description:AimsCoronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS).Methods and resultsSeventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (ΔDL-NIRS-IVUS: -37.8 ± 89.0 vs. ΔConv-NIRS-IVUS: 243.3 ± 183.7 mm3, variance ratio: 4.262, P < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, P < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, P < 0.001) and quantified minimum lumen area (ΔDL-NIRS-IVUS: -0.35 ± 1.81 vs. ΔConv-NIRS-IVUS: 1.37 ± 2.32 mm2, variance ratio: 1.634, P < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, P = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, P < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s.ConclusionsThe DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644).

Project description:ObjectivesWe developed a novel human cadaveric perfusion model with continuous extracorporeal femoral perfusion suitable for performing intra-individual comparison studies, training of interventional procedures and preclinical testing of endovascular devices. Objective of this study was to introduce the techniques and evaluate the feasibility for realistic computed tomography angiography (CTA), digital subtraction angiography (DSA) including vascular interventions, and intravascular ultrasound (IVUS).MethodsThe establishment of the extracorporeal perfusion was attempted using one formalin-fixed and five fresh-frozen human cadavers. In all specimens, the common femoral and popliteal arteries were prepared, introducer sheaths inserted, and perfusion established by a peristaltic pump. Subsequently, we performed CTA and bilateral DSA in five cadavers and IVUS on both legs of four donors. Examination time without unintentional interruption was measured both with and without non-contrast planning CT. Percutaneous transluminal angioplasty and stenting was performed by two interventional radiologists on nine extremities (five donors) using a broad spectrum of different intravascular devices.ResultsThe perfusion of the upper leg arteries was successfully established in all fresh-frozen but not in the formalin-fixed cadaver. The experimental setup generated a stable circulation in each procedure (ten upper legs) for a period of more than six hours. Images acquired with CT, DSA and IVUS offered a realistic impression and enabled the sufficient visualization of all examined vessel segments. Arterial cannulating, percutaneous transluminal angioplasty as well as stent deployment were feasible in a way that is comparable to a vascular intervention in vivo. The perfusion model allowed for introduction and testing of previously not used devices.ConclusionsThe continuous femoral perfusion model can be established with moderate effort, works stable, and is utilizable for medical imaging of the peripheral arterial system using CTA, DSA and IVUS. Therefore, it appears suitable for research studies, developing skills in interventional procedures and testing of new or unfamiliar vascular devices.

Dataset Information

Prospective deep learning-based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study.

Aims

Methods and results

Conclusion

Publications

Prospective deep learning-based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study.

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