Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm

BACKGROUND Planning of percutaneous interventional procedures involves a pre-screening and risk stratification of the coronary artery disease. Current screening tools use stand-alone plaque texture-based features and therefore lack the ability to stratify the risk. METHOD This IRB approved study presents a novel strategy for coronary artery disease risk stratification using an amalgamation of IVUS plaque texture-based and wall-based measurement features. Due to common genetic plaque makeup, carotid plaque burden was chosen as a gold standard for risk labels during training-phase of machine learning (ML) paradigm. Cross-validation protocol was adopted to compute the accuracy of the ML framework. A set of 59 plaque texture-based features was padded with six wall-based measurement features to show the improvement in stratification accuracy. The ML system was executed using principle component analysis-based framework for dimensionality reduction and uses support vector machine classifier for training and testing-phases. RESULTS The ML system produced a stratification accuracy of 91.28%, demonstrating an improvement of 5.69% when wall-based measurement features were combined with plaque texture-based features. The fused system showed an improvement in mean sensitivity, specificity, positive predictive value, and area under the curve by: 6.39%, 4.59%, 3.31% and 5.48%, respectively when compared to the stand-alone system. While meeting the stability criteria of 5%, the ML system also showed a high average feature retaining power and mean reliability of 89.32% and 98.24%, respectively. CONCLUSIONS The ML system showed an improvement in risk stratification accuracy when the wall-based measurement features were fused with the plaque texture-based features.

[1]  U. Rajendra Acharya,et al.  Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound , 2012, Journal of Medical Systems.

[2]  J. Suri,et al.  Improved correlation between carotid and coronary atherosclerosis SYNTAX score using automated ultrasound carotid bulb plaque IMT measurement. , 2015, Ultrasound in medicine & biology.

[3]  J. Suri,et al.  Advanced algorithmic approaches to medical image segmentation: state-of-the-art application in cardiology, neurology, mammography and pathology , 2001 .

[4]  Mark D. Huffman,et al.  Executive Summary: Heart Disease and Stroke Statistics—2015 Update A Report From the American Heart Association , 2011, Circulation.

[5]  J. Farmer,et al.  Strategies for Multivessel Revascularization in Patients with Diabetes: The Freedom Trial , 2014, Current Atherosclerosis Reports.

[6]  Renu Virmani,et al.  Pathologic validation of a new method to quantify coronary calcific deposits in vivo using intravascular ultrasound , 2000 .

[7]  V. Aboyans,et al.  Carotid intima-media thickness as predictor of secondary events after coronary angioplasty. , 2003, International angiology : a journal of the International Union of Angiology.

[8]  Filippo Molinari,et al.  An automated technique for carotid far wall classification using grayscale features and wall thickness variability , 2015, Journal of clinical ultrasound : JCU.

[9]  Marios S. Pattichis,et al.  Classification of atherosclerotic carotid plaques using morphological analysis on ultrasound images , 2009, Applied Intelligence.

[10]  U. Rajendra Acharya,et al.  Inter- and intra-observer variability analysis of completely automated cIMT measurement software (AtheroEdge™) and its benchmarking against commercial ultrasound scanner and expert Readers , 2013, Comput. Biol. Medicine.

[11]  Petia Radeva,et al.  Well-balanced system for coronary calcium detection and volume measurement in a low resolution intravascular ultrasound videos , 2017, Comput. Biol. Medicine.

[12]  Wei Liu,et al.  Use of intravascular ultrasound vs. optical coherence tomography for mechanism and patterns of in-stent restenosis among bare metal stents and drug eluting stents. , 2016, Journal of thoracic disease.

[13]  Milan Sonka,et al.  Coronary plaque analysis by multimodality fusion. , 2005, Studies in health technology and informatics.

[14]  Ajay Gupta,et al.  Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: A web-based point-of-care tool for multicenter clinical trial , 2016, Comput. Biol. Medicine.

[15]  Filippo Molinari,et al.  Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement , 2010, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[16]  Petia Radeva,et al.  Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos , 2016, Journal of Medical Systems.

[17]  D. Mozaffarian,et al.  Executive summary: heart disease and stroke statistics--2012 update: a report from the American Heart Association. , 2012, Circulation.

[18]  Petia Radeva,et al.  Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach , 2016, Comput. Methods Programs Biomed..

[19]  Filippo Molinari,et al.  Association of automated carotid IMT measurement and HbA1c in Japanese patients with coronary artery disease. , 2013, Diabetes research and clinical practice.

[20]  Jasjit S. Suri,et al.  Ankle–Brachial Index and Its Link to Automated Carotid Ultrasound Measurement of Intima–Media Thickness Variability in 500 Japanese Coronary Artery Disease Patients , 2014, Current Atherosclerosis Reports.

[21]  P. Serruys,et al.  Ex vivo validation of 45 MHz intravascular ultrasound backscatter tissue characterization. , 2015, European heart journal cardiovascular Imaging.

[22]  R. Ross,et al.  Cell biology of atherosclerosis. , 1995, Annual review of physiology.

[23]  Andrew Nicolaides,et al.  Ultrasound imaging in the analysis of carotid plaque morphology for the assessment of stroke. , 2005, Studies in health technology and informatics.

[24]  M. Matsumoto,et al.  Carotid Intima-Media Thickness for Atherosclerosis. , 2016, Journal of atherosclerosis and thrombosis.

[25]  Jasjit S. Suri,et al.  Multi-Modality Atherosclerosis Imaging and Diagnosis , 2013 .

[26]  Osamu Seguchi,et al.  Atherosclerosis Found on Carotid Ultrasonography Is Associated With Atherosclerosis on Coronary Intravascular Ultrasonography , 2005, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[27]  Paul Schoenhagen,et al.  Understanding coronary artery disease: tomographic imaging with intravascular ultrasound , 2002, Heart.

[28]  Jasjit S Suri,et al.  Asymptomatic Carotid Disease—A New Tool for Assessing Neurological Risk , 2014, Echocardiography.

[29]  Nobutaka Ikeda,et al.  Impact of carotid artery ultrasound and ankle-brachial index on prediction of severity of SYNTAX score. , 2013, Circulation journal : official journal of the Japanese Circulation Society.

[30]  Ayman El-Baz,et al.  Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm , 2017, Comput. Biol. Medicine.

[31]  Ajay Gupta,et al.  Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm , 2017, Journal of Medical Systems.

[32]  Robert M. Haralick,et al.  Greedy Algorithm for Error Correction in Automatically Produced Boundaries from Low Contrast Ventriculograms , 2000, Pattern Analysis & Applications.

[33]  Richard D. White,et al.  Coronary imaging: angiography shows the stenosis, but IVUS, CT, and MRI show the plaque. , 2003, Cleveland Clinic journal of medicine.

[34]  Filippo Molinari,et al.  Carotid IMT Variability (IMTV) and Its Validation in Symptomatic versus Asymptomatic Italian Population: Can This Be a Useful Index for Studying Symptomaticity? , 2012, Echocardiography.

[35]  Spyretta Golemati,et al.  Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks. , 2007, Ultrasound in medicine & biology.

[36]  Constantinos S. Pattichis,et al.  Texture-based classification of atherosclerotic carotid plaques , 2003, IEEE Transactions on Medical Imaging.

[37]  J. Suri,et al.  Volumetric Analysis of Carotid Plaque Components and Cerebral Microbleeds: A Correlative Study. , 2017, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.

[38]  Nilanjan Dey,et al.  A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound , 2015, Comput. Methods Programs Biomed..

[39]  U Rajendra Acharya,et al.  Automated carotid IMT measurement and its validation in low contrast ultrasound database of 885 patient Indian population epidemiological study: results of AtheroEdge™ Software. , 2012, International angiology : a journal of the International Union of Angiology.

[40]  P. Libby,et al.  Progress and challenges in translating the biology of atherosclerosis , 2011, Nature.

[41]  Jasjit S. Suri,et al.  A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens , 2016, Comput. Methods Programs Biomed..

[42]  J. Suri,et al.  Atherosclerotic risk stratification strategy for carotid arteries using texture-based features. , 2012, Ultrasound in medicine & biology.

[43]  F. Lizzi Ultrasound Imaging , 1991, Proceedings Technology Requirements for Biomedical Imaging.

[44]  Nassir Navab,et al.  A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images , 2012, IEEE Transactions on Information Technology in Biomedicine.

[45]  R O Bonow,et al.  Contrast magnetic resonance imaging in the assessment of myocardial viability in patients with stable coronary artery disease and left ventricular dysfunction. , 1998, Circulation.

[46]  Filippo Molinari,et al.  Shape‐Based Approach for Coronary Calcium Lesion Volume Measurement on Intravascular Ultrasound Imaging and Its Association With Carotid Intima‐Media Thickness , 2015, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[47]  J. Borén,et al.  Ira Tabas , Kevin Jon Williams and Jan Borén and Therapeutic Implications Subendothelial Lipoprotein Retention as the Initiating Process in Atherosclerosis : Update , 2007 .

[48]  Tadashi Araki,et al.  PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology , 2016, Comput. Methods Programs Biomed..

[49]  U. Rajendra Acharya,et al.  Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization , 2013, Comput. Methods Programs Biomed..

[50]  M. Naghavi,et al.  Vulnerable Atherosclerotic Plaque: A Multifocal Disease , 2003, Circulation.