Predicting Locations of High-Risk Plaques in Coronary Arteries in Patients Receiving Statin Therapy
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F18%3A10376310" target="_blank" >RIV/00216208:11110/18:10376310 - isvavai.cz</a>
Alternative codes found
RIV/00064165:_____/18:10376310
Result on the web
<a href="https://doi.org/10.1109/TMI.2017.2725443" target="_blank" >https://doi.org/10.1109/TMI.2017.2725443</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TMI.2017.2725443" target="_blank" >10.1109/TMI.2017.2725443</a>
Alternative languages
Result language
angličtina
Original language name
Predicting Locations of High-Risk Plaques in Coronary Arteries in Patients Receiving Statin Therapy
Original language description
Features of high-risk coronary artery plaques prone to major adverse cardiac events (MACE) were identified by intravascular ultrasound (IVUS) virtual histology (VH). These plaque features are: thin-cap fibroatheroma (TCFA), plaque burden PB >= 70%, or minimal luminal area MLA <= 4 mm(2). Identification of arterial locations likely to later develop such high-risk plaques may help prevent MACE. We report a machine learning method for prediction of future high-risk coronary plaque locations and types in patients under statin therapy. Sixty-one patients with stable angina on statin therapy underwent baseline and one-year follow-up VH-IVUS non-culprit vessel examinations followed by quantitative image analysis. For each segmented and registered VH-IVUS frame pair (n = 6341), location-specific (approximate to 0.5 mm) vascular features and demographic information at baseline were identified. Seven independent support vector machine classifiers with seven different feature subsets were trained to predict high-risk plaque types one year later. A leave-one-patient-out cross-validation was used to evaluate the prediction power of different feature subsets. The experimental results showed that our machine learning method predicted future TCFA with correctness of 85.9%, 81.7%, and 77.0% (G-mean) for baseline plaque phenotypes of TCFA, thick-cap fibroatheroma, and non-fibroatheroma, respectively. For predicting PB >= 70%, correctness was 80.8% for baseline PB >= 70% and 85.6% for 50% <= PB< 70%. Accuracy of predicted MLA <= 4 mm(2) was 81.6% for baseline MLA <= 4 mm(2) and 80.2% for 4 mm(2) < MLA <= 6 mm(2). Location-specific prediction of future high-risk coronary artery plaques is feasible through machine learning using focal vascular features and demographic variables. Our approach outperforms previously reported results and shows the importance of local factors on high-risk coronary artery plaque development.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
30201 - Cardiac and Cardiovascular systems
Result continuities
Project
<a href="/en/project/NT13224" target="_blank" >NT13224: The prediction of extension and risk profile of coronary atherosclerosis and their changes during lipid-lowering therapy based on non-invasive techniques.</a><br>
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Medical Imaging
ISSN
0278-0062
e-ISSN
—
Volume of the periodical
37
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
151-161
UT code for WoS article
000419346900014
EID of the result in the Scopus database
2-s2.0-85023609906