A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens. Issue 124 (February 2016)

• Record Type: Journal Article
• Authors: Araki, Tadashi; Ikeda, Nobutaka; Shukla, Devarshi; Londhe, Narendra D.; Shrivastava, Vimal K.; Banchhor, Sumit K.; Saba, Luca; Nicolaides, Andrew; Shafique, Shoaib; Laird, John R.; Suri, Jasjit S.
• Is Part Of: Computer methods and programs in biomedicine. Issue 124(2016)
• Page Start: 161
• Page End: 179
• Publication Date: 2016-02
• Subjects: Coronary artery -- IVUS -- Carotid disease -- B-mode ultrasound -- Machine learning -- Risk assessment; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.10.022
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Coronary risk stratification. Machine learning to link two arteries. 56 grayscale features. Coronary tissue characterization. Classification accuracy 94.95% and AUC 0.95. Abstract: Interventional cardiologists have a deep interest in risk stratification prior to stenting and percutaneous coronary intervention (PCI) procedures. Intravascular ultrasound (IVUS) is most commonly adapted for screening, but current tools lack the ability for risk stratification based on grayscale plaque morphology. Our hypothesis is based on the genetic makeup of the atherosclerosis disease, that there is evidence of a link between coronary atherosclerosis disease and carotid plaque built up. This novel idea is explored in this study for coronary risk assessment and its classification of patients between high risk and low risk. This paper presents a strategy for coronary risk assessment by combining the IVUS grayscale plaque morphology and carotid B-mode ultrasound carotid intima-media thickness (cIMT) – a marker of subclinical atherosclerosis. Support vector machine (SVM) learning paradigm is adapted for risk stratification, where both the learning and testing phases use tissue characteristics derived from six feature combinational spaces, which are then used by the SVM classifier with five different kernels sets. These six feature combinational spaces are designed using 56 novel feature sets. K -fold cross validation protocol with 10 trials per fold is used for optimization of best SVM-kernel and best feature combination set. IRB approved coronary IVUS and carotid B-mode ultrasound were jointly collected on 15 patients (2 days apart) via : (a) 40 MHz catheter utilizing iMap (Boston Scientific, Marlborough, MA, USA) with 2865 frames per patient (42, 975 frames) and (b) linear probe B-mode carotid ultrasound (Toshiba scanner, Japan). Using the above protocol, the system shows the classification accuracy of 94.95% and AUC of 0.95 using optimized feature combination. This is the first system of its kind for risk stratification as a screening tool to prevent excessive cost burden and better patients' cardiovascular disease management, while validating our two hypotheses.