Radiomics and artificial intelligence: how medical physicists can help their translation into radiology, molecular imaging and radiation therapy routine clinical practice?

Bouchareb, Yassine; Khaniabadi, Pegah Moradi; Al-Dhuhli, Humoud; Al-Kindi, Faiza

doi:10.1016/s1120-1797(22)00087-4

Cited by 3 publications

(3 citation statements)

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“…Radiomics analysis has great potential for precision medicine as it uses data mining to create a correlation between clinical and biological findings [ 5 , 6 ]. Explicit radiomics features such as shape, statistics and texture are derived from medical images, resulting in accurate and non-invasive COVID-19 biomarkers that potentially influence clinical decision making.…”

Section: Introductionmentioning

confidence: 99%

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

Khaniabadi

Bouchareb²,

Al-Dhuhli³

et al. 2022

Computers in Biology and Medicine

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Section: Introductionmentioning

confidence: 99%

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

Khaniabadi

Bouchareb²,

Al-Dhuhli³

et al. 2022

Computers in Biology and Medicine

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“…Further reduction to 9 features was achieved using the algorithm-based feature. The 9 most pertinent features include: shape features (2), first-order features (1), and second-order features (6), respectively. Spearman correlation, narrowed down the number of features to 37 features by removing redundant features (see Figure 4) [24].…”

Section: Resultsmentioning

confidence: 99%

“…Radiomics analysis has great potential for precision medicine as it uses data mining to create a correlation between clinical and biological findings [5, 6]. Explicit radiomics features such as shape, statistics and texture are derived from medical images, resulting in accurate and non-invasive COVID-19 biomarkers that potentially influence clinical decision making [3, 7].…”

Section: Introductionmentioning

confidence: 99%

Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics

Khaniabadi

Bouchareb

Dhuhli

et al. 2022

Preprint

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Objective: We aimed to develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features. Methods: Three hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. For severity prediction, two radiologists scored involvement of lungs in COVID-19 and pneumonia scans based on percentage of involvement in all 5 lobes. Datasets were classified into mild (0–25%), moderate (26–50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation method. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. For both tasks, datasets were randomly divided into 90% training sets (70% and 30% for training and validation, respectively) and 10% test sets. Pearson correlation coefficient (PCC≥90%) was performed to exclude highly correlated features. Subsequently, different feature selection algorithms (Correlation attribute evaluation, Information gain attribute, Wrapper Subset selection algorithm, Relief method, and Correlation-based feature selection) were assessed. The most pertinent features were finally selected using voting method based on the evaluation of all algorithms. Several ML-based supervised algorithms were utilized, namely Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting. The synthetic minority oversampling technique (SMOTE) was used to balance the three classes in training sets. The optimal model was first selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation sets 20 times, followed by testing using the test set. To ensure the repeatability of the results, the entire process was repeated 50 times. Results: Nine pertinent features (2 shape, 1 first-order, and 6 second-order features) were obtained after feature selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC. Conclusion: The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients.

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Technological Advances in SPECT and SPECT/CT Imaging

Bouchareb,

AlSaadi,

Zabah

et al. 2024

Diagnostics

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Single photon emission tomography/computed tomography (SPECT/CT) is a mature imaging technology with a dynamic role in the diagnosis and monitoring of a wide array of diseases. This paper reviews the technological advances, clinical impact, and future directions of SPECT and SPECT/CT imaging. The focus of this review is on signal amplifier devices, detector materials, camera head and collimator designs, image reconstruction techniques, and quantitative methods. Bulky photomultiplier tubes (PMTs) are being replaced by position-sensitive PMTs (PSPMTs), avalanche photodiodes (APDs), and silicon PMs to achieve higher detection efficiency and improved energy resolution and spatial resolution. Most recently, new SPECT cameras have been designed for cardiac imaging. The new design involves using specialised collimators in conjunction with conventional sodium iodide detectors (NaI(Tl)) or an L-shaped camera head, which utilises semiconductor detector materials such as CdZnTe (CZT: cadmium–zinc–telluride). The clinical benefits of the new design include shorter scanning times, improved image quality, enhanced patient comfort, reduced claustrophobic effects, and decreased overall size, particularly in specialised clinical centres. These noticeable improvements are also attributed to the implementation of resolution-recovery iterative reconstructions. Immense efforts have been made to establish SPECT and SPECT/CT imaging as quantitative tools by incorporating camera-specific modelling. Moreover, this review includes clinical examples in oncology, neurology, cardiology, musculoskeletal, and infection, demonstrating the impact of these advancements on clinical practice in radiology and molecular imaging departments.

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Radiomics and artificial intelligence: how medical physicists can help their translation into radiology, molecular imaging and radiation therapy routine clinical practice?

Cited by 3 publications

References 0 publications

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics

Technological Advances in SPECT and SPECT/CT Imaging

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