Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition

Sabouri, Maziar; Hajianfar, Ghasem; Hosseini, Zahra; Amini, Mehdi; Mohebi, Mobin; Ghaedian, Tahereh; Madadi, Shabnam; Rastgou, F; Oveisi, Mehrdad; Rajabi, Ahmad Bitarafan; Shiri, Isaac; Zaidi, Habib

doi:10.1007/s10278-022-00705-9

Cited by 28 publications

(5 citation statements)

References 55 publications

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“…In this light, the term "radiomics" can be put forward as it fundamentally describes the analysis of medical images via computational data extraction and, in other words, transforming images into minable biomarkers [9][10][11]. Radiomics can help us prepare data hidden in images that cannot be seen with conventional image assessment methods with the naked eye in different diseases [12][13][14][15][16]. Whether in the diagnostic or the prognostic area, cancer research has always been of interest to radiomics researchers, and GBM is not an exception [17,18].…”

Section: Introductionmentioning

confidence: 99%

Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics

Hajianfar,

Haddadi Avval,

Hosseini

et al. 2023

Radiol med

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Purpose Glioblastoma Multiforme (GBM) represents the predominant aggressive primary tumor of the brain with short overall survival (OS) time. We aim to assess the potential of radiomic features in predicting the time-to-event OS of patients with GBM using machine learning (ML) algorithms. Materials and methods One hundred nineteen patients with GBM, who had T1-weighted contrast-enhanced and T2-FLAIR MRI sequences, along with clinical data and survival time, were enrolled. Image preprocessing methods included 64 bin discretization, Laplacian of Gaussian (LOG) filters with three Sigma values and eight variations of Wavelet Transform. Images were then segmented, followed by the extraction of 1212 radiomic features. Seven feature selection (FS) methods and six time-to-event ML algorithms were utilized. The combination of preprocessing, FS, and ML algorithms (12 × 7 × 6 = 504 models) was evaluated by multivariate analysis. Results Our multivariate analysis showed that the best prognostic FS/ML combinations are the Mutual Information (MI)/Cox Boost, MI/Generalized Linear Model Boosting (GLMB) and MI/Generalized Linear Model Network (GLMN), all of which were done via the LOG (Sigma = 1 mm) preprocessing method (C-index = 0.77). The LOG filter with Sigma = 1 mm preprocessing method, MI, GLMB and GLMN achieved significantly higher C-indices than other preprocessing, FS, and ML methods (all p values < 0.05, mean C-indices of 0.65, 0.70, and 0.64, respectively). Conclusion ML algorithms are capable of predicting the time-to-event OS of patients using MRI-based radiomic and clinical features. MRI-based radiomics analysis in combination with clinical variables might appear promising in assisting clinicians in the survival prediction of patients with GBM. Further research is needed to establish the applicability of radiomics in the management of GBM in the clinic.

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Section: Introductionmentioning

confidence: 99%

Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics

Hajianfar,

Haddadi Avval,

Hosseini

et al. 2023

Radiol med

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“…They reported that the variability of features over different imaging settings is feature-dependent and identified robust radiomics features for further studies. Sabouri et al 34 , 35 studied to identify left ventricle contractile patterns using conventional quantitative and radiomic features extracted from MPI-SPECT and machine learning algorithms. Their proposed model achieved promising results for detecting left ventricle contractile patterns, which can further be used for cardiac resynchronization therapy response prediction.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study

Amini,

Pursamimi,

Hajianfar

et al. 2023

Sci Rep

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This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-rest protocol MPI SPECT were enrolled in this study. The left ventricle myocardium, excluding the cardiac cavity, was manually delineated on rest and stress images to define a volume of interest. Added to clinical features (age, sex, family history, diabetes status, smoking, and ejection fraction), a total of 118 radiomics features, were extracted from rest and stress MPI SPECT images to establish different feature sets, including Rest-, Stress-, Delta-, and Combined-radiomics (all together) feature sets. The data were randomly divided into 80% and 20% subsets for training and testing, respectively. The performance of classifiers built from combinations of three feature selections, and nine machine learning algorithms was evaluated for two different diagnostic tasks, including 1) normal/abnormal (no CAD vs. CAD) classification, and 2) low-risk/high-risk CAD classification. Different metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), were reported for models’ evaluation. Overall, models built on the Stress feature set (compared to other feature sets), and models to diagnose the second task (compared to task 1 models) revealed better performance. The Stress-mRMR-KNN (feature set-feature selection-classifier) reached the highest performance for task 1 with AUC, ACC, SEN, and SPE equal to 0.61, 0.63, 0.64, and 0.6, respectively. The Stress-Boruta-GB model achieved the highest performance for task 2 with AUC, ACC, SEN, and SPE of 0.79, 0.76, 0.75, and 0.76, respectively. Diabetes status from the clinical feature family, and dependence count non-uniformity normalized, from the NGLDM family, which is representative of non-uniformity in the region of interest were the most frequently selected features from stress feature set for CAD risk classification. This study revealed promising results for CAD risk classification using machine learning models built on MPI SPECT radiomics. The proposed models are helpful to alleviate the labor-intensive MPI SPECT interpretation process regarding CAD status and can potentially expedite the diagnostic process.

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“…22,23 Additionally, there are few radiomics studies on SPECT/CT, most of which are based on myocardial or lung perfusion imaging, and studies on bone imaging are rare. [24][25][26][27] There was study showed that doctors still have ambiguous diagnoses regarding the results for 14.3% of patients after undergoing 99m Tc-MDP SPECT/CT examination. 28 Therefore, it is necessary to develop a machine learning approach with SPECT/CT to differentiate between bone metastases and benign bone lesions in patients with lung cancer, with the intention of assisting clinicians in making more accurate clinical decisions.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, most current studies on radiomics in bone metastasis of lung cancer only considers the diagnostic value of radiomics features or clinical features without investigating their added value 22,23 . Additionally, there are few radiomics studies on SPECT/CT, most of which are based on myocardial or lung perfusion imaging, and studies on bone imaging are rare 24–27 . There was study showed that doctors still have ambiguous diagnoses regarding the results for 14.3% of patients after undergoing 99m Tc‐MDP SPECT/CT examination 28 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients

Wang,

Chen,

Qiu

et al. 2023

Medical Physics

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BackgroundBone metastasis is a common event in lung cancer progression. Early diagnosis of lung malignant tumor with bone metastasis is crucial for selecting effective treatment strategies. However, 14.3% of patients are still difficult to diagnose after SPECT/CT examination.PurposeMachine learning analysis of [99mTc]‐methylene diphosphate (99mTc‐MDP) SPECT/CT scans to distinguish bone metastases from benign bone lesions in patients with lung cancer.MethodsOne hundred forty‐one patients (69 with bone metastases and 72 with benign bone lesions) were randomly assigned to the training group or testing group in a 7:3 ratio. Lesions were manually delineated using ITK‐SNAP, and 944 radiomics features were extracted from SPECT and CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomics features in the training set, and the single/bimodal radiomics models were established based on support vector machine (SVM). To further optimize the model, the best bimodal radiomics features were combined with clinical features to establish an integrated Radiomics‐clinical model. The diagnostic performance of models was evaluated using receiver operating characteristic (ROC) curve and confusion matrix, and performance differences between models were evaluated using the Delong test.ResultsThe optimal radiomics model comprised of structural modality (CT) and metabolic modality (SPECT), with an area under curve (AUC) of 0.919 and 0.907 for the training and testing set, respectively. The integrated model, which combined SPECT, CT, and two clinical features, exhibited satisfactory differentiation in the training and testing set, with AUC of 0.939 and 0.925, respectively.ConclusionsThe machine learning can effectively differentiate between bone metastases and benign bone lesions. The Radiomics‐clinical integrated model demonstrated the best performance.

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Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition

Cited by 28 publications

References 55 publications

Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics

Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics

Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study

Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients

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