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The implementation and feasibility of Radiomics in an Aortic Stenosis patient cohort

Siegersma, Klaske (2018) The implementation and feasibility of Radiomics in an Aortic Stenosis patient cohort.

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Abstract:Introduction Aortic Stenosis (AS) is a very common and deadly valvular disease, predominantly present in the elderly. Outflow obstruction of AS leads to hypertrophy of cardiac muscle cells, eventually leading to fibrosis. This non-ischemic scarring is visualized with (LGE)-images of cardiac magnetic resonance (CMR). Fibrosis can induce heart failure and sudden cardiac death. Since AS has relatively high mortality, timely and accurate risk-stratification of the patients that benefit from early valve surgery is important. Radiomics is a novel method for extraction of quantitative features from medical images, relating image features to phenotyping, diagnosis and treatment through predictive modelling. This study implements radiomics on an AS patient cohort for predicting risk of surgery. A second study utilizes radiomics for computer-aided diagnosis of myocardial fibrosis. Methods Dataset-1 included 146 AS-patients for predictive modelling of aortic valve replacement (AVR). This cohort and additional controls were used for identification of fibrosis on CMR. A segmentation of the myocardium was performed for extraction of radiomic features. Cylindrical reconstruction of myocardium aided in extraction of case-specific features and texture feature analysis. Univariate analysis was performed on individual features. Multivariate analysis with temporal validation included a generalized linear model (GLM), random forest (RF) and support vector machine (SVM), with minimum redundancy, maximum relevance (mRMR) feature selection. A second feature set, comprised of clinical features, was used to determine the performance of these features in prediction of AVR and computer-aided diagnosis of LGE. Performance measures were concordance index (CI), respectively Area Under the Curve (AUC). A second dataset was implemented for external validation. Results 5639 features were extracted from LGE-CMR images. Univariate analysis for AVR revealed 49 prognostic features (FDR q-value<0.05, CI>0.6). Multivariate clinical GLM, including peak aortic jet velocity, high-sensitivity troponin-I and electrocardiographic strain pattern showed higher CI (0.86) than models built with radiomic features (average CI: 0.55). Classification of fibrosis showed opposite performance; average AUC of models with radiomic features was 0.92, clinical modelling showed 0.78. External validation showed similar performance to temporal validation for prediction of AVR (average CI: 0.60), but lower for classification of LGE (AUC: 0.70). Discussion This study was the first study to implement external validation for predictive modelling and computer-aided diagnosis in CMR. Although training data and external validation cohort were significantly different in patient characteristics, promising results were shown for classification of LGE. A larger dataset can aid in further analysis to determine the optimal timing of AVR and clinical pathway for patients with AS.
Item Type:Essay (Master)
Clients:
UMC Utrecht, Utrecht, Netherlands
Faculty:TNW: Science and Technology
Subject:44 medicine
Programme:Technical Medicine MSc (60033)
Link to this item:http://purl.utwente.nl/essays/74511
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