Book chapter
Prospective Prediction of Thin-Cap Fibroatheromas from Baseline Virtual Histology Intravascular Ultrasound Data
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015, pp.603-610
Lecture Notes in Computer Science, Springer International Publishing
11/20/2015
DOI: 10.1007/978-3-319-24571-3_72
Abstract
Thin-cap fibroatheroma (TCFA) is particularly prone to rupture, which may result in myocardial infarction and death. Virtual histology intravascular ultrasound (VH-IVUS) provides quantitative information about plaque composition and enables TCFA identification. However, prospective prediction of future development of TCFA has not been previously possible. The aim of our study was to determine whether subsequent development of TCFA can be predicted from baseline VH-IVUS data. Corresponding VH-IVUS images of baseline and follow-up examinations were identified by a highly automated approach to register IVUS pullback pairs from 24 patients (2,331 image pairs). Next, 20 location-specific VH-based and IVUS-based features including plaque phenotype and morphology, and 15 systemic patient-specific features were extracted and ranked using a support vector machine recursive feature elimination (SVM RFE) technique. SVM was applied to assess the prediction power of different feature sets, by adding the first n-ranked features to the classification procedure (leave-one-patient-out cross validation) iteratively until all features were considered. The experimental results showed that the prospective prediction of TCFA achieves a sensitivity of 72.6% and a specificity of 73.3%, when an optimal set of the five best selected features is used. The results indicate the feasibility of prospective prediction of TCFA formation based on baseline VH-IVUS data.
Details
- Title: Subtitle
- Prospective Prediction of Thin-Cap Fibroatheromas from Baseline Virtual Histology Intravascular Ultrasound Data
- Creators
- Ling Zhang - University of IowaAndreas Wahle - University of IowaZhi Chen - University of IowaJohn Lopez - Loyola University ChicagoTomas Kovarnik - Charles UniversityMilan Sonka - University of Iowa
- Resource Type
- Book chapter
- Publication Details
- Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015, pp.603-610
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-319-24571-3_72
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Publisher
- Springer International Publishing; Cham
- Language
- English
- Date published
- 11/20/2015
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; The Iowa Institute for Biomedical Imaging; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984186601302771
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