Predicting Locations of High-Risk Plaques in Coronary Arteries in Patients Receiving Statin Therapy

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00064165:_____/18:10376310

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting Locations of High-Risk Plaques in Coronary Arteries in Patients Receiving Statin Therapy

Popis výsledku v původním jazyce

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 &gt;= 70%, or minimal luminal area MLA &lt;= 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 &gt;= 70%, correctness was 80.8% for baseline PB &gt;= 70% and 85.6% for 50% &lt;= PB&lt; 70%. Accuracy of predicted MLA &lt;= 4 mm(2) was 81.6% for baseline MLA &lt;= 4 mm(2) and 80.2% for 4 mm(2) &lt; MLA &lt;= 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.

Název v anglickém jazyce

Predicting Locations of High-Risk Plaques in Coronary Arteries in Patients Receiving Statin Therapy

Popis výsledku anglicky

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 &gt;= 70%, or minimal luminal area MLA &lt;= 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 &gt;= 70%, correctness was 80.8% for baseline PB &gt;= 70% and 85.6% for 50% &lt;= PB&lt; 70%. Accuracy of predicted MLA &lt;= 4 mm(2) was 81.6% for baseline MLA &lt;= 4 mm(2) and 80.2% for 4 mm(2) &lt; MLA &lt;= 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30201 - Cardiac and Cardiovascular systems

Projekt

<a href="/cs/project/NT13224" target="_blank" >NT13224: Predikce rozsahu a rizikovosti koronárního postižení a jejich změn při hypolipidemické terapii na základě neinvazivních vyšetření.</a><br>

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Medical Imaging

ISSN

0278-0062

e-ISSN

—

Svazek periodika

37

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

151-161

Kód UT WoS článku

000419346900014

EID výsledku v databázi Scopus

2-s2.0-85023609906