Background/Objectives: Alzheimer’s disease is the leading cause of dementia, marked by progressive cognitive decline and a severe socioeconomic burden. Early and accurate diagnosis is crucial to enhancing patient outcomes, yet traditional clinical and imaging assessments are often limited in sensitivity, particularly at early stages. This study presents a dual-modal framework that integrates symptom-based clinical data with magnetic resonance imaging (MRI) using machine learning (ML) and deep learning (DL) models, enhanced by explainable AI (XAI). Methods: Four ML classifiers—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)—were trained on demographic and clinical features. For stage-wise classification, five DL models—CNN, EfficientNetB3, DenseNet-121, ResNet-50, and MobileNetV2—were applied to MRI scans. Interpretability was incorporated through SHAP and Grad-CAM visualizations. Results: Random Forest achieves the highest accuracy of 97% on clinical data, while CNN achieves the best overall performance of 94% in MRI-based staging. SHAP and Grad-CAM were used to find clinically relevant characteristics and brain areas, including hippocampal atrophy and ventricular enlargement. Conclusions: Integrating clinical and imaging data and interpretable AI improves the accuracy and reliability of AD staging. The proposed model offers a valid and clear diagnostic route, which can assist clinicians in making timely diagnoses and adjusting individual treatment.

Background/Objective: This cross-sectional study assessed the knowledge, attitudes, and practices (KAP) of radiology professionals in Saudi Arabia regarding environmental sustainability. Methods: An online survey with 298 participants revealed moderate knowledge and positive attitudes towards sustainability. Results: However, a significant gap between awareness and practice was observed. The most common sustainable practice was digital documentation (53.4%), while energy saving and water saving measures were rare, with more than half rarely or never practicing energy saving. The primary barrier to implementation was a lack of awareness and training (50%). The sustainability association was significantly higher among older, female, and hospital-based professionals. Conclusions: The findings underscore the urgent need for improved educational initiatives, strong institutional support, and clear policy frameworks to effectively integrate sustainable practices into radiology departments in Saudi Arabia.

Background: Repeated brain computed tomography (CT) scans in children may result in substantial cumulative radiation exposure, particularly in young children, who are more sensitive to ionizing radiation. The purpose of the study was to assess the dose levels of radiation in patients who receive repeated brain CT during childhood and adherence rates to pediatric imaging protocols. Methods: A retrospective cross-sectional study was conducted among 177 patients aged ≤5 years who underwent two or more brain CT examinations with a total of 514 CT examinations. The information was gathered through the hospital Picture Archiving and Communication System (PACS), which included patient demographics, scan parameters, and scanner-reported dose indicators such as volume-averaged computed tomography dose index (CTDIvol) and dose-length product (DLP). The effective dose (ED) was calculated and compared with estimated doses based on a nominal pediatric CT protocol. Results: The findings indicated a great variation in scan parameters, with CTDIvol values of 8.9 to 51.7 mGy and DLP values of 177 to 1310 mGy.cm. The number of repeated scans showed a great increase in the cumulative ED (p < 0.001). The median doses in patients below the age of one year were greater than those in older children. There was also a closer relation of scanner-reported doses to adult protocols, which suggests a lack of an optimized pediatric setting. Conclusions: Children under 5 who undergo repeated brain CT scans may face excessive radiation exposure. The matter is aggravated by the fact that scans are performed repeatedly without optimization of the dose, which leads to significant cumulative ED.

Background/Objectives: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, representing nearly 85% of cases worldwide. Predicting patient response before treatment, however, remains a major clinical challenge. Radiomics enables non-invasive extraction of quantitative imaging data that reflects tumor characteristics and heterogeneity. In this study, we developed and externally validated a CT-derived radiomic signature for predicting outcomes in advanced NSCLC patients treated with first-line platinum-based chemotherapy. Methods: NSCLC across three tertiary care centers were included. Tumor lesions from baseline contrast-enhanced CT scans were semi-automatically outlined using 3D Slicer software (version 5.12.2), followed by the extraction of 851 quantitative imaging biomarkers through PyRadiomics version 3.0. The extracted parameters comprised histogram-based features, morphological measurements, texture-related variables, and wavelet-derived attributes. Reliability of feature extraction was evaluated using intraclass correlation coefficient analysis, whereas LASSO regression together with stability selection was applied to identify the most relevant predictors. Predictive capability was assessed across five machine learning techniques, including logistic regression, random forest, support vector machine, gradient boosting, and multilayer perceptron models. The resulting radiomics-based composite score was then tested in an independent external validation cohort. Response to treatment was determined according to RECIST 1.1 guidelines. Results: After feature reproducibility assessment and stability screening, 421 radiomic parameters were retained for further analysis and 14 feasible parameters were selected by LASSO. There was good predictive power of the integrated model that included both radiomic and clinical features, with AUC values of 0.876, 0.849, and 0.831 in the training set, internal validation set and external validation set, respectively. The radiomic signature successfully differentiated patients according to their probability of therapeutic response. Furthermore, by using decision-curve analysis, the combined radiomics nomogram was more clinically useful than models based on clinical characteristics within clinically relevant decision-threshold ranges with greater net benefit. Conclusions: This pre-treatment CT-based radiomics signature demonstrates potential as a decision-support tool for chemotherapy-response prediction in NSCLC. However, prospective and multi-ethnic validation is required before clinical application. Based on this data, larger multi-ethnic cohorts should be considered for prospective validation.