بحوث سكوبس — مدرس مساعد. مرتضى عبدالامير محمد

This study aims to evaluate the predictive accuracy of textural parameters and current parameters of 18F-fluorodeoxyglucose and 68Ga-labeled prostate-specific antigen positron emission tomography (FDG and PSMA PET) images in prostate cancer (PCa) and compare the features retrieved from both scans. Based on symptoms, digital rectal examination (DRE), prostate-specific antigen (PSA) level in the blood, or histopathology from transrectal ultrasound-guided biopsy and 4Kscore Test, 120 patients have confirmed PCa. Sixty of them were scanned on a PET/CT machine using 18F-FDG, and the other 60 patients were scanned using 68Ga-PSMA of radiopharmacy. Each tumour was delineated using PET. Edge texture parameters were used to define each tumour, and 73 features in all were taken from eight distinct texture matrices and computed using the open-source program Chang-Gung Image Texture Analysis (CGITA). Using Spearman correlation, feature correlation with conventional quantitative metrics (Maximum Standardized Uptake Value (SUVmax), Total Lesion Glycolysis (TLG), Metabolic Tumor Volume (MTV)) was investigated, and it was found that the High-Intensity Low-Energy Radiation (HILRE) correlation was strong. PCa was best discriminated by HILRE (64-bin) in receiver operating characteristic curves. It is concluded that 68Ga-PSMA-based PET imaging is better than 18F-FDG-based PET and is strongly associated with PCa tumour allocation. According to extracted features, HILRE is the most significant measure and it is, thus, considered here an independent predictor of PCa prognosis. Although the study's findings are helpful, confirmation by further prospective research is required. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

AIM: This study assessed the myocardial infarction (MI) using a novel fusion approach (multi-flavored or tensor-based) of multi-parametric cardiac magnetic resonance imaging (CMRI) at four sequences; T1-weighted (T1W) in the axial plane, sense-balanced turbo field echo (sBTFE) in the axial plane, late gadolinium enhancement of heart short axis (LGE-SA) in the sagittal plane, and four-chamber views of LGE (LGE-4CH) in the axial plane. METHODS: After considering the inclusion and exclusion criteria, 115 patients (83 with MI diagnosis and 32 as healthy control patients), were included in the present study. Radiomic features were extracted from the whole left ventricular myocardium (LVM). Feature selection methods were Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-Square (Chi2), Analysis of Variance (Anova), Recursive Feature Elimination (RFE), and SelectPersentile. The classification methods were Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). Different metrics, including receiver operating characteristic curve (AUC), accuracy, F1-score, precision, sensitivity, and specificity were calculated for radiomic features extracted from CMR images using stratified five-fold cross-validation. RESULTS: For the MI detection, Lasso (as the feature selection) and RF/LR (as the classifiers) in sBTFE sequences had the best performance (AUC: 0.97). All features and classifiers of T1 + sBTFE sequences with the weighted method (as the fused image), had a good performance (AUC: 0.97). In addition, the results of the evaluated metrics, especially mean AUC and accuracy for all models, determined that the T1 + sBTFE-weighted fused method had strong predictive performance (AUC: 0.93 ± 0.05; accuracy: 0.93 ± 0.04), followed by T1 + sBTFE-PCA fused method (AUC: 0.85 ± 0.06; accuracy: 0.84 ± 0.06). CONCLUSION: Our selected CMRI sequences demonstrated that radiomics analysis enables to detection of MI accurately. Among the investigated sequences, the T1 + sBTFE-weighted fused method with the highest AUC and accuracy values was chosen as the best technique for MI detection. © 2024 – IOS Press. All rights reserved.