Academic Journal
Data-driven models for the prediction of coronary atherosclerotic plaque progression/regression.
| العنوان: | Data-driven models for the prediction of coronary atherosclerotic plaque progression/regression. |
|---|---|
| المؤلفون: | Bulant, Carlos A, Boroni, Gustavo A, Bass, Ronald, Räber, Lorenz, Lemos, Pedro A, García-García, Héctor M, Blanco, Pablo J |
| المصدر: | Bulant, Carlos A; Boroni, Gustavo A; Bass, Ronald; Räber, Lorenz; Lemos, Pedro A; García-García, Héctor M; Blanco, Pablo J (2024). Data-driven models for the prediction of coronary atherosclerotic plaque progression/regression. Scientific Reports, 14(1493) Nature Publishing Group 10.1038/s41598-024-51508-7 |
| بيانات النشر: | Nature Publishing Group |
| سنة النشر: | 2024 |
| المجموعة: | BORIS (Bern Open Repository and Information System, University of Bern) |
| مصطلحات موضوعية: | 610 Medicine & health |
| الوصف: | Coronary artery disease is defined by the existence of atherosclerotic plaque on the arterial wall, which can cause blood flow impairment, or plaque rupture, and ultimately lead to myocardial ischemia. Intravascular ultrasound (IVUS) imaging can provide a detailed characterization of lumen and vessel features, and so plaque burden, in coronary vessels. Prediction of the regions in a vascular segment where plaque burden can either increase (progression) or decrease (regression) following a certain therapy, has remained an elusive major milestone in cardiology. Studies like IBIS-4 showed an association between plaque burden regression and high-intensity rosuvastatin therapy over 13 months. Nevertheless, it has not been possible to predict if a patient would respond in a favorable/adverse fashion to such a treatment. This work aims to (i) Develop a framework that processes lumen and vessel cross-sectional contours and extracts geometric descriptors from baseline and follow-up IVUS pullbacks; and to (ii) Develop, train, and validate a machine learning model based on baseline/follow-up IVUS datasets that predicts future percent of atheroma volume changes in coronary vascular segments using only baseline information, i.e. geometric features and clinical data. This is a post hoc analysis, revisiting the IBIS-4 study. We employed 140 arteries, from 81 patients, for which expert delineation of lumen and vessel contours were available at baseline and 13-month follow-up. Contour data from baseline and follow-up pullbacks were co-registered and then processed to extract several frame-wise features, e.g. areas, plaque burden, eccentricity, etc. Each pullback was divided into regions of interest (ROIs), following different criteria. Frame-wise features were condensed into region-wise markers using tools from statistics, signal processing, and information theory. Finally, a stratified 5-fold cross-validation strategy (20 repetitions) was used to train/validate an XGBoost regression models. A feature selection method before the ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | https://boris.unibe.ch/191759/ |
| الاتاحة: | https://boris.unibe.ch/191759/1/s41598-024-51508-7.pdf https://boris.unibe.ch/191759/ |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.599ECDCC |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |