Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study

Mohebi, Mobin; Amini, Mehdi; Alemzadeh‐Ansari, Mohammad Javad; Alizadehasl, Azin; Rajabi, Ahmad Bitarafan; Shiri, Isaac; Zaidi, Habib; Orooji, Mahdi

doi:10.1007/s10278-023-00820-1

Cited by 8 publications

(1 citation statement)

References 41 publications

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“…Machine learning techniques, such as XGBoost and neural networks, have become increasingly popular in recent years due to their ability to handle complex data and identify patterns that may not be apparent with traditional statistical methods [ 20 ]. Machine learning has been used to develop predictive models for various diseases, such as diabetes, heart disease, and cancer, as well as to identify subgroups of patients who may benefit from targeted treatments [ 21 – 25 ]. With the rise of machine learning techniques in healthcare research, it is crucial to examine how these models compare to traditional statistical approaches in terms of variable selection and ranking.…”

Section: Introductionmentioning

confidence: 99%

Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia

Huang,

Huang

2024

PLoS ONE

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Importance Sleep is critical to a person’s physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates. Objective The study aimed to compare how different model metrics rank the importance of various covariates. Design, setting, and participants A cross-sectional cohort study was conducted retrospectively using the National Health and Nutrition Examination Survey (NHANES), which is publicly available. Methods This study employed univariate logistic models to filter out strong, independent covariates associated with sleep disorder outcome, which were then used in machine-learning models, of which, the most optimal was chosen. The machine-learning model was used to rank model covariates based on gain, cover, and frequency to identify risk factors for sleep disorder and feature importance was evaluated using both univariable and multivariable t-statistics. A correlation matrix was created to determine the similarity of the importance of variables ranked by different model metrics. Results The XGBoost model had the highest mean AUROC of 0.865 (SD = 0.010) with Accuracy of 0.762 (SD = 0.019), F1 of 0.875 (SD = 0.766), Sensitivity of 0.768 (SD = 0.023), Specificity of 0.782 (SD = 0.025), Positive Predictive Value of 0.806 (SD = 0.025), and Negative Predictive Value of 0.737 (SD = 0.034). The model metrics from the machine learning of gain and cover were strongly positively correlated with one another (r > 0.70). Model metrics from the multivariable model and univariable model were weakly negatively correlated with machine learning model metrics (R between -0.3 and 0). Conclusion The ranking of important variables associated with sleep disorder in this cohort from the machine learning models were not related to those from regression models.

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Section: Introductionmentioning

confidence: 99%

Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia

Huang,

Huang

2024

PLoS ONE

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Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization

Gharibi,

Hajianfar,

Sabouri

et al. 2024

Medical Physics

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BackgroundCoronary artery disease (CAD) has one of the highest mortality rates in humans worldwide. Single‐photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) provides clinicians with myocardial metabolic information non‐invasively. However, there are some limitations to interpreting SPECT images performed by physicians or automatic quantitative approaches. Radiomics analyzes images objectively by extracting quantitative features and can potentially reveal biological characteristics that the human eye cannot detect. However, the reproducibility and repeatability of some radiomic features can be highly susceptible to segmentation and imaging conditions.PurposeWe aimed to assess the reproducibility of radiomic features extracted from uncorrected MPI‐SPECT images reconstructed with 15 different settings before and after ComBat harmonization, along with evaluating the effectiveness of ComBat in realigning feature distributions.Materials and methodsA total of 200 patients (50% normal and 50% abnormal) including rest and stress (without attenuation and scatter corrections) MPI‐SPECT images were included. Images were reconstructed using 15 combinations of filter cut‐off frequencies, filter orders, filter types, reconstruction algorithms, number of iterations and subsets resulting in 6000 images. Image segmentation was performed on the left ventricle in the first reconstruction for each patient and applied to 14 others. A total of 93 radiomic features were extracted from the segmented area, and ComBat was used to harmonize them. The intraclass correlation coefficient (ICC) and overall concordance correlation coefficient (OCCC) tests were performed before and after ComBat to examine the impact of each parameter on feature robustness and to assess harmonization efficiency. The ANOVA and the Kruskal–Wallis tests were performed to evaluate the effectiveness of ComBat in correcting feature distributions. In addition, the Student's t‐test, Wilcoxon rank‐sum, and signed‐rank tests were implemented to assess the significance level of the impacts made by each parameter of different batches and patient groups (normal vs. abnormal) on radiomic features.ResultsBefore applying ComBat, the majority of features (ICC: 82, OCCC: 61) achieved high reproducibility (ICC/OCCC ≥ 0.900) under every batch except Reconstruction. The largest and smallest number of poor features (ICC/OCCC < 0.500) were obtained by IterationSubset and Order batches, respectively. The most reliable features were from the first‐order (FO) and gray‐level co‐occurrence matrix (GLCM) families. Following harmonization, the minimum number of robust features increased (ICC: 84, OCCC: 78). Applying ComBat showed that Order and Reconstruction were the least and the most responsive batches, respectively. The most robust families, in a descending order, were found to be FO, neighborhood gray‐tone difference matrix (NGTDM), GLCM, gray‐level run length matrix (GLRLM), gray‐level size zone matrix (GLSZM), and gray‐level dependence matrix (GLDM) under Cut‐off, Filter, and Order batches. The Wilcoxon rank‐sum test showed that the number of robust features significantly differed under most batches in the Normal and Abnormal groups.ConclusionThe majority of radiomic features show high levels of robustness across different OSEM reconstruction parameters in uncorrected MPI‐SPECT. ComBat is effective in realigning feature distributions and enhancing radiomic features reproducibility.

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Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms

Taleie,

Hajianfar,

Sabouri

et al. 2023

J Digit Imaging

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Heart failure caused by iron deposits in the myocardium is the primary cause of mortality in beta-thalassemia major patients. Cardiac magnetic resonance imaging (CMRI) T2* is the primary screening technique used to detect myocardial iron overload, but inherently bears some limitations. In this study, we aimed to differentiate beta-thalassemia major patients with myocardial iron overload from those without myocardial iron overload (detected by T2*CMRI) based on radiomic features extracted from echocardiography images and machine learning (ML) in patients with normal left ventricular ejection fraction (LVEF > 55%) in echocardiography. Out of 91 cases, 44 patients with thalassemia major with normal LVEF (> 55%) and T2* ≤ 20 ms and 47 people with LVEF > 55% and T2* > 20 ms as the control group were included in the study. Radiomic features were extracted for each end-systolic (ES) and end-diastolic (ED) image. Then, three feature selection (FS) methods and six different classifiers were used. The models were evaluated using various metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Maximum relevance-minimum redundancy-eXtreme gradient boosting (MRMR-XGB) (AUC = 0.73, ACC = 0.73, SPE = 0.73, SEN = 0.73), ANOVA-MLP (AUC = 0.69, ACC = 0.69, SPE = 0.56, SEN = 0.83), and recursive feature elimination-K-nearest neighbors (RFE-KNN) (AUC = 0.65, ACC = 0.65, SPE = 0.64, SEN = 0.65) were the best models in ED, ES, and ED&ES datasets. Using radiomic features extracted from echocardiographic images and ML, it is feasible to predict cardiac problems caused by iron overload.

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Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study

Cited by 8 publications

References 41 publications

Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia

Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia

Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization

Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms

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