Technical Briefs

In Vivo Serial MRI-Based Models and Statistical Methods to Quantify Sensitivity and Specificity of Mechanical Predictors for Carotid Plaque Rupture: Location and Beyond

[+] Author and Article Information
Zheyang Wu, Chun Yang, Dalin Tang

Mathematical Sciences Department, Worcester Polytechnic  Institute, Worcester, MA 01609Mathematical Sciences Department,  Worcester Polytechnic Institute, Worcester, MA 01609, School of Mathematical Sciences,  Beijing Normal University, Lab of Math and Complex Systems, Ministry of Education, Beijing, ChinaMathematical Sciences Department, Worcester Polytechnic  Institute, Worcester, MA 01609 e-mail: dtang@wpi.edu

J Biomech Eng 133(6), 064503 (Jun 14, 2011) (5 pages) doi:10.1115/1.4004189 History: Received December 25, 2010; Revised May 06, 2011; Posted May 09, 2011; Published June 14, 2011; Online June 14, 2011

It has been hypothesized that mechanical risk factors may be used to predict future atherosclerotic plaque rupture. Truly predictive methods for plaque rupture and methods to identify the best predictor(s) from all the candidates are lacking in the literature. A novel combination of computational and statistical models based on serial magnetic resonance imaging (MRI) was introduced to quantify sensitivity and specificity of mechanical predictors to identify the best candidate for plaque rupture site prediction. Serial in vivo MRI data of carotid plaque from one patient was acquired with follow-up scan showing ulceration. 3D computational fluid-structure interaction (FSI) models using both baseline and follow-up data were constructed and plaque wall stress (PWS) and strain (PWSn) and flow maximum shear stress (FSS) were extracted from all 600 matched nodal points (100 points per matched slice, baseline matching follow-up) on the lumen surface for analysis. Each of the 600 points was marked “ulcer” or “nonulcer” using follow-up scan. Predictive statistical models for each of the seven combinations of PWS, PWSn, and FSS were trained using the follow-up data and applied to the baseline data to assess their sensitivity and specificity using the 600 data points for ulcer predictions. Sensitivity of prediction is defined as the proportion of the true positive outcomes that are predicted to be positive. Specificity of prediction is defined as the proportion of the true negative outcomes that are correctly predicted to be negative. Using probability 0.3 as a threshold to infer ulcer occurrence at the prediction stage, the combination of PWS and PWSn provided the best predictive accuracy with (sensitivity, specificity) = (0.97, 0.958). Sensitivity and specificity given by PWS, PWSn, and FSS individually were (0.788, 0.968), (0.515, 0.968), and (0.758, 0.928), respectively. The proposed computational-statistical process provides a novel method and a framework to assess the sensitivity and specificity of various risk indicators and offers the potential to identify the optimized predictor for plaque rupture using serial MRI with follow-up scan showing ulceration as the gold standard for method validation. While serial MRI data with actual rupture are hard to acquire, this single-case study suggests that combination of multiple predictors may provide potential improvement to existing plaque assessment schemes. With large-scale patient studies, this predictive modeling process may provide more solid ground for rupture predictor selection strategies and methods for image-based plaque vulnerability assessment.

Copyright © 2011 by American Society of Mechanical Engineers
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Grahic Jump Location
Figure 1

In vivo 3D MR images (T1 weighting) of a human carotid plaque at baseline (Time 1) and follow-up (Time 2) with ulceration observed at Time 2

Grahic Jump Location
Figure 2

3D plaque wall stress and flow shear stress plots obtained from 3D FSI models

Grahic Jump Location
Figure 3

Data point assignments and their correspondence between Time 1 and Time 2. The lines connecting lumen points to out-boundary points were drawn using a 4-segment even-spacing method previously published [1] to determine node types and wall thickness. This segment even-spacing method was an improvement over the simple shortest distance method.



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