Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection

Avard, Elham; Shiri, Isaac; Hajianfar, Ghasem; Abdollahi, Hamid; Kalantari, Kiara Rezaei; Houshmand, Golnaz; Kasani, Kianosh; Bitarafan-Rajabi, Ahmad; Deevband, Mohammad Reza; Oveisi, Mehrdad; Zaidi, Habib

doi:10.1016/j.compbiomed.2021.105145

Cited by 45 publications

(32 citation statements)

References 44 publications

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“…We included 14,339 patients along with their CT images, segmented the lungs, and extracted distinct radiomics features. As there is no “one fits all” machine learning approaches for radiomic studies, given that their performance is task-dependent and there is large variability across models [ [49] , [50] , [51] , [52] , [53] , [54] , [55] ], we tested the cross-combination of four feature selectors and seven classifiers, which resulted in twenty-eight different combinations of algorithms to find the best performing model. Since the dataset was gathered from different centers, we applied the ComBat Harmonization algorithm that has been successfully applied in radiomics studies over the extracted features [ 25 , 55 , 56 ].…”

Section: Discussionmentioning

confidence: 99%

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Shiri

Salimi

Pakbin

et al. 2022

Computers in Biology and Medicine

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Section: Discussionmentioning

confidence: 99%

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Shiri

Salimi

Pakbin

et al. 2022

Computers in Biology and Medicine

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“…to administer contrast to patients) using ML. This subject has recently gained the interest of many researchers (13)(14)(15)(16)(17)(18)(19)(20)(21) but despite these interests, many open problems and challenges on this subject still exist. The accurate prediction of contrast information without contrast administration with ML methods is very challenging for many reasons.…”

Section: Introduction Background and Objectivesmentioning

confidence: 99%

Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods

Abdulkareem

Kenawy

Rauseo

et al. 2022

Front. Cardiovasc. Med.

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ObjectivesCurrently, administering contrast agents is necessary for accurately visualizing and quantifying presence, location, and extent of myocardial infarction (MI) with cardiac magnetic resonance (CMR). In this study, our objective is to investigate and analyze pre- and post-contrast CMR images with the goal of predicting post-contrast information using pre-contrast information only. We propose methods and identify challenges.MethodsThe study population consists of 272 retrospectively selected CMR studies with diagnoses of MI (n = 108) and healthy controls (n = 164). We describe a pipeline for pre-processing this dataset for analysis. After data feature engineering, 722 cine short-axis (SAX) images and segmentation mask pairs were used for experimentation. This constitutes 506, 108, and 108 pairs for the training, validation, and testing sets, respectively. We use deep learning (DL) segmentation (UNet) and classification (ResNet50) models to discover the extent and location of the scar and classify between the ischemic cases and healthy cases (i.e., cases with no regional myocardial scar) from the pre-contrast cine SAX image frames, respectively. We then capture complex data patterns that represent subtle signal and functional changes in the cine SAX images due to MI using optical flow, rate of change of myocardial area, and radiomics data. We apply this dataset to explore two supervised learning methods, namely, the support vector machines (SVM) and the decision tree (DT) methods, to develop predictive models for classifying pre-contrast cine SAX images as being a case of MI or healthy.ResultsOverall, for the UNet segmentation model, the performance based on the mean Dice score for the test set (n = 108) is 0.75 (±0.20) for the endocardium, 0.51 (±0.21) for the epicardium and 0.20 (±0.17) for the scar. For the classification task, the accuracy, F1 and precision scores of 0.68, 0.69, and 0.64, respectively, were achieved with the SVM model, and of 0.62, 0.63, and 0.72, respectively, with the DT model.ConclusionWe have presented some promising approaches involving DL, SVM, and DT methods in an attempt to accurately predict contrast information from non-contrast images. While our initial results are modest for this challenging task, this area of research still poses several open problems.

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“…The field of radiomics opens pathways for the study of normal tissues, cancer, and many other diseases, including potentially the newly emerging COVID-19 disease 6,7,29,36-40 . Specifically, Xie et al 41 evaluated the potential of a radiomics framework to diagnose COVID-19 from CT images.…”

Section: Introductionmentioning

confidence: 99%

“…The field of radiomics opens pathways for the study of normal tissues, cancer, and many other diseases, including potentially the newly emerging COVID-19 disease 6,7,29,[36][37][38][39][40] .…”

Section: Introductionmentioning

confidence: 99%

High-Dimensional Multinomial Multiclass Severity Scoring of COVID-19 Pneumonia Using CT Radiomics Features and Machine Learning Algorithms

Shiri

Mostafaei

Avval

et al. 2022

Preprint

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We aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). CT scans were preprocessed with bin discretization and resized, followed by segmentation of the entire lung and extraction of radiomics features. We utilized two feature selection algorithms, namely Bagging Random Forest (BRF) and Multivariate Adaptive Regression Splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. Subsequently, 10-fold cross-validation with bootstrapping (n=1000) was performed to validate the classification results. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4 by 4 confusion matrices. In addition, the areas under the receiver operating characteristic (ROC) curve (AUCs) for multi-class classifications were calculated and compared for both models using multiROC and pROC R packages. Using BRF, 19 radiomics features were selected, 9 from first-order, 6 from GLCM, 1 from GLDM, 1 from shape, 1 from NGTDM, and 1 from GLSZM radiomics features. Ten features were selected using the MARS algorithm, namely 2 from first-order, 1 from GLDM, 2 from GLRLM, 2 from GLSZM, and 3 from GLCM features. The Mean Absolute Deviation and Median from first-order, Small Area Emphasis from GLSZM, and Correlation from GLCM features were selected by both BRF and MARS algorithms. Except for the Inverse Variance feature from GLCM, all selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p-values<0.05). BRF+MLR and MARS+MLR resulted in pseudo-R2 prediction performances of 0.295 and 0.256, respectively. Meanwhile, there were no significant differences between the feature selection models when using a likelihood ratio test (p-value =0.319). Based on confusion matrices for BRF+MLR and MARS+MLR algorithms, the precision was 0.861 and 0.825, the recall was 0.844 and 0.793, whereas the accuracy was 0.933 and 0.922, respectively. AUCs (95% CI)) for multi-class classification were 0.823 (0.795-0.852) and 0.816 (0.788-0.844) for BRF+MLR and MARS+MLR algorithms, respectively. Our models based on the utilization of radiomics features, coupled with machine learning, were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients.

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Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection

Cited by 45 publications

References 44 publications

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods

High-Dimensional Multinomial Multiclass Severity Scoring of COVID-19 Pneumonia Using CT Radiomics Features and Machine Learning Algorithms

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