Uncertainty Estimation in Classification of MGNT Using Radiogenomics for Glioblastoma Patients

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework for MGMT classification with uncertainty analysis utilizing imaging features extracted from multimodal magnetic resonance imaging (mMRI). The imaging features include conventional texture, volumetric, and sophisticated fractal, and multi-resolution fractal texture features. The proposed method is evaluated with publicly available BraTS-TCIA-GBM pre-operative scans and TCGA datasets with 114 patients. The experiment with 10-fold cross-validation suggests that the fractal and multi-resolution fractal texture features offer an improved prediction of MGMT status. The uncertainty analysis using an ensemble of Stochastic Gradient Langevin Boosting models along with multi-resolution fractal features offers an accuracy of 71.74% and area under the curve of 0.76. Finally, analysis shows that our proposed method with uncertainty analysis offers improved predictive performance when compared with different well-known methods in the literature

Obstacles to Using E-learning and Distance Learning in Jordanian Universities in the light of the Coronavirus Pandemic from the Students Point of View

This study aims to identify barriers to the use of e-learning and distance learning in Jordanian universities from the perspective of students in Jordan during the outbreak of the COVID-19 pandemic. The study used descriptive methodology to develop a questionnaire with 50 items, all of which were arranged in a behavioral order to match the structure of the interview items. Validity and reliability of the instrument were evaluated, and the instrument was applied to a sample of students (716), who were randomly selected. The results of the study showed that there are barriers to using e-learning and distance learning in Jordanian universities according to students opinions about the coronavirus pandemic. The results also show that there are significant differences in student responses regarding barriers to online learning. It also covered the barriers and challenges that prevent this change, such as poor internet connection and lack of interest, showing the need for new courses to improve students experience in online learning

Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically-driven quantitative biomarkers

Existing Quantitative Imaging Biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials

Molecular Characterization of Carbapenem-Resistant Acinetobacter baumannii Isolated from Intensive Care Unit Patients in Jordanian Hospitals

Acinetobacter baumannii is a common cause of healthcare-associated infections (HAI) worldwide, mostly occurring in intensive care units (ICUs). Extended-spectrum beta lactamases (ESBL)-positive A. baumannii strains have emerged as highly resistant to most currently used antimicrobial agents, including carbapenems. The most common mechanism for carbapenem resistance in this species is β-lactamase-mediated resistance. Carbapenem-hydrolyzing class D oxacillinases are widespread among multidrug-resistant (MDR) A. baumannii strains. The present study was conducted to determine the presence and distribution of blaOXA genes among multidrug-resistant A. baumannii isolated from ICU patients and genes encoding insertion sequence (IS-1) in these isolates. Additionally, the plasmid DNA profiles of these isolates were determined. A total of 120 clinical isolates of A. baumannii from various ICU clinical specimens of four main Jordanian hospitals were collected. Bacterial isolate identification was confirmed by biochemical testing and antibiotic sensitivity was then assessed. PCR amplification and automated sequencing were carried out to detect the presence of blaOXA-51, blaOXA-23, blaOXA-24, and blaOXA-58 genes, and ISAba1 insertion sequence. Out of the 120 A. baumannii isolates, 95% of the isolates were resistant to three or more classes of the antibiotics tested and were identified as MDR. The most frequent resistance of the isolates was against piperacillin (96.7%), cephalosporins (97.5%), and β-lactam/β-lactamase inhibitor combinations antibiotics (95.8%). There were 24 (20%) ESBL-producing isolates. A co-existence of blaOXA-51 gene and ISAba1 in all the 24 ESBL-producing isolates was determined. In addition, in the 24 ESBL-producing isolates, 21 (87.5%) carried blaOXA-51 and blaOXA-23 genes, 1 (4.2%) carried blaOXA-51 and blaOXA-24, but all were negative for the blaOXA-58 gene. Plasmid DNA profile A and profile B were the most common (29%) in ESBL-positive MDR A. baumannii isolates while plasmid DNA profile A was the most common in the ESBL-negative isolates. In conclusion, there was an increase in prevalence of MDR-A. baumannii in ICU wards in Jordanian hospitals, especially those having an ESBL phenotype. Thus, identification of ESBL genes is necessary for the surveillance of their transmission in hospitals