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.
Plaque progression and vulnerability are influenced by many risk factors. Our goal is to find simple methods to combine multiple risk factors for better plaque development predictions. A sample size of 374 intravascular ultrasound (IVUS) slices with matched follow-up was obtained from 9 patients (Mean age 59, 7 m) with informed consent obtained. 3D fluid-structure interaction models were constructed to obtain plaque stress/strain conditions. Four morphological and biomechanical factors (plaque burden (PB), cap thickness (CT), lipid percent (LP) and average plaque wall stress (PWS)) were chosen to predict plaque burden increase defined as PBI = (PB at follow-up) - (PB at baseline). For a given slice Si, the ground truth Y PBI is define as Y PBI (Si)=1 if PBI(Si)>0; Y PBI =0 if PBI(Si)≤0. For a single predictor W, a threshold value Wc was used to assign the binary prediction outcome: Y W (Si)=1 if W>Wc; Y W (Si)=0 if W≤Wc. Wc was chosen to get optimal agreement between Y W and Y PBI . To use multiple predictors (say, W1, W2, W3) to PBI, a new predictor Combo(W1,W2,W3) was created with its values defined as Combo(W1,W2,W3)=Y W1 +Y W2 +Y W3 , where Y W1 , Y W2 and Y W3 were evaluated the same way as before. Combo was then treated as a single predictor and a threshold value was determined to achieve best agreement with Y PBI . Table 1 summarizes the optimal thresholds and agreement rates for all 15 strategies. Agreement rate using PB alone was 57.5%. PWS was the best single predictor for PBI with agreement rate 62.6%. Combining CT and PWS achieved 66.5% agreement rate, 9% better over PB, which was also obtained by combining 4 risk factors. The method presented here could be used to combine predictors from different sources (stenosis, cap, lipid, inflammation, macrophage, hemorrhage, stress, strain, flow shear stress, FFR, smoking, diabetes, cholesterol, alcohol, hypertension, pro-rupture genes, etc.) to improve prediction accuracy and help decision-making in clinical practice.
Atherosclerotic plaque progression and rupture play an important role in cardiovascular disease development and the final drastic events such as heart attack and stroke. Medical imaging and image-based computational modeling methods advanced considerably in recent years to quantify plaque morphology and biomechanical conditions and gain a better understanding of plaque evolution and rupture process. This article first briefly reviewed clinical imaging techniques for coronary thin-cap fibroatheroma (TCFA) plaques used in image-based computational modeling. This was followed by a summary of different types of biomechanical models for coronary plaques. Plaque progression and vulnerability prediction studies based on image-based computational modeling were reviewed and compared. Much progress has been made and a reasonable high prediction accuracy has been achieved. However, there are still some inconsistencies in existing literature on the impact of biomechanical and morphological factors on future plaque behavior, and it is very difficult to perform direct comparison analysis as differences like image modality, biomechanical factors selection, predictive models, and progression/vulnerability measures exist among these studies. Encouraging data and model sharing across the research community would partially resolve these differences, and possibly lead to clearer assertive conclusions. In vivo image-based computational modeling could be used as a powerful tool for quantitative assessment of coronary plaque vulnerability for potential clinical applications.
Cardiovascular diseases are closely linked to atherosclerotic plaque development and rupture. Assessment of plaque vulnerability is of fundamental significance to cardiovascular research and disease diagnosis, prevention, tre... | Find, read and cite all the research you need on Tech Science Press
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