Radiographers play a pivotal role in healthcare but face significant occupational health challenges, including work-related musculoskeletal disorders (WRMSDs), burnout, fatigue, and sleep disturbances. These challenges, influenced by high workloads and inadequate ergonomics, compromise their well-being and job performance. This study aims to assess the prevalence of these health outcomes and evaluate the role of workplace factors in mitigating or exacerbating these issues. A systematic review and meta-analysis was conducted per PRISMA 2020 guidelines. Literature searches across PubMed, Scopus, Web of Science, and Embase included quantitative studies published from 2014 to 2024. Data on prevalence, outcomes, and measurement tools were extracted, and a random-effects model was used to estimate pooled prevalence. Risk of bias was assessed with the ROBINS-E tool. From 1080 records, 14 studies met inclusion criteria. WRMSDs were reported by 85 % (95 % CI: 74-95) of radiographers, with the neck (73 %) and lower back (67 %) being the most affected regions. Burnout prevalence included emotional exhaustion at 29.8 % (95 % CI: 19.8-39.8). Chronic fatigue and sleep disturbances were strongly linked to extended shifts and poor ergonomics. Protective factors included professional recognition and workplace ergonomic interventions. Radiographers are disproportionately affected by physical and psychological health risks. Addressing these through enhanced workplace ergonomics, stress management, and workload balancing is imperative to improving their quality of life and job performance. Implementing ergonomic training, fostering professional recognition, and reducing workloads are crucial for sustainable radiography practices. Policy reforms and workplace interventions can significantly improve radiographers' health, ensuring better job satisfaction and retention.

T2DM is associated with neurodegenerative changes that can be detected through advanced magnetic resonance imaging. Brain atrophy in T2DM is linked to cognitive decline, yet the extent and pattern of structural brain changes remain underexplored. This study aims to assess volumetric differences in brain structures between diabetic and non-diabetic individuals using voxel-based morphometry (VBM). A prospective observational study was conducted with 86 participants (43 T2DM case and 43 healthy controls) who underwent high-resolution 3D MRI scans using 3D SPGR and T1-weighted Fast Spin Echo sequences. Image preprocessing and volumetric analysis were performed using Statistical Parametric Mapping (SPM-12) and the Computational Anatomy Toolbox (CAT-12). Brain volumes were analyzed for 24 regions. Statistical analyses were conducted using independent t-tests and linear regression, with p < 0.05 considered significant. T2DM subjects exhibited significant gray matter (GM) volume reductions compared to controls, particularly in the hippocampus and middle frontal gyrus, as detected in both 3DSPGR and T1 FSE sequences. However, regional differences emerged between imaging modalities: while T1 FSE imaging revealed significant bilateral hippocampal atrophy, 3DSPGR data showed no such difference. Notably, both sequences demonstrated significantly increased volumes in the anterior and posterior temporal lobes in T2DM participants, suggesting possible region-specific hypertrophy. T2DM is associated with significant brain atrophy, particularly in brain regions associated with cognition. Based on these findings, MRI-based volumetric analysis has the potential to detect and monitor T2DM-related neurodegeneration early, emphasizing the need for routine neuroimaging in diabetic populations. Research on longitudinal assessments will be necessary in the future to gain a deeper understanding of the progression of brain atrophy in diabetics.

Hepatocellular carcinoma (HCC) is among the leading causes of cancer-related deaths worldwide. The clinical utility of artificial intelligence (AI) and radiomics models based on contrast-enhanced computed tomography (CECT) for HCC detection remains underexplored. This systematic review and meta-analysis evaluated the diagnostic performance of machine learning and radiomics-based models. A comprehensive literature search (2015-2025) across four major databases identified 29 eligible studies according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The risk of bias was assessed using the QUADAS-2 tool. Of these, 17 studies provided independent performance metrics and were included in the meta-analysis. Pooled estimates of sensitivity, specificity, accuracy, and the F1 score were calculated using a random effects model. Models evaluated on independent test datasets, the pooled sensitivity and specificity were 0.562 and 0.570, respectively. A subgroup of radiomics-specific models (n=3) demonstrated comparatively higher sensitivity (0.660) and specificity (0.670), with minimal heterogeneity in sensitivity (I² = 0%, τ² ≈ 0), suggesting strong internal consistency. However, due to the limited number of studies, these results should be interpreted with caution. Combined models showed lower sensitivity (0.368) and specificity (0.297), with high heterogeneity (I² = 98.3%), likely due to the absence of independent test sets, which may have led to limited generalisability. Radiomics-based models evaluated on independent datasets exhibit more consistent diagnostic accuracy for HCC detection using CECT. However, methodological variability underscores the need for standardised reporting, robust external validation, and incorporation of explainable AI techniques to enhance clinical adoption.

Purpose: Medical technology has gone a long way in diagnosis and characterization of breast tumors. Diffusion-weighted MR imaging is the state of the art for breast screening and diagnosing. The aim of this meta-analysis is to evaluate the diagnostic performances of diffusion-weighted MR imaging in characterization of breast lesions with different b value in 1.5 T MRI. Method: An extensive search on Scopus, Embase, and PubMed databases were performed on studies published between January 2000 and 2020. The systematic seek initially yielded 2467 studies, out of which 27 research were covered on this meta-evaluation. The included studies for meta-analysis utilized different b value and noted that the ADC value was highly influenced by the b value, for differential diagnosis of breast tumors. Results: The current meta-analysis has shown the ADC values was lower for malignant breast lesions as compared with benign lesions. The recommended mean threshold ADC was 1.25 ± 0.17 × 10^–3 mm²/s range from 0.93 to 1.60 × 10^–3 mm²/s for differential diagnosis of breast tumors. Sub-group analysis on the bases of b value showed statistically significant differences in the ADC value of benign and malignant breast tumors. Conclusion: In conclusion, we noted that b value has a significant effect in calculating the ADC value of the breast lesions as well as ADC threshold value but lacks standardization. The ADC value measurement has a potential for differentiation between benign and malignant breast lesions.

Degenerative spinal disease is a leading cause of lower back pain worldwide, impairing mobility and quality of life. Early degeneration is increasingly seen in younger adults due to sedentary lifestyles and occupational stress. Conventional MRI, particularly T2-weighted imaging, is limited in detecting early microstructural changes. Diffusion kurtosis imaging (DKI), by quantifying non-Gaussian water diffusion, offers enhanced sensitivity for identifying subtle alterations preceding overt degeneration. To evaluate the ability of quantitative DKI parameters to detect early intervertebral disc degeneration by correlating them with the Pfirrmann grading system in the thoracolumbar spine. This prospective study included 76 participants, 54 with degenerative spine disease and 22 healthy controls. MRI was performed on a 3 T scanner using sagittal T1-, T2-, and DKI sequences. Parameter maps included mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), fractional anisotropy (FA), and diffusivity indices (MD, AD, RD, KA). Regions of interest were placed in the nucleus pulposus and annulus fibrosus, with subregional analysis of the anterior and posterior annulus. Grade V discs were excluded, yielding (1,287) discs (Grades I-IV). Statistical analyses included group comparisons and Spearman's correlation. MK, AK, and RK were significantly higher in the degenerative group (p < 0.001), while MD, AD, and RD were substantially lower (p < 0.001). Correlations were strongest in the mid-to-lower lumbar levels, particularly within the nucleus pulposus and anterior annulus. DKI enables quantitative characterisation of disc microstructure and demonstrates diagnostic potential for differentiating degenerative from non-degenerative discs, supporting its role as an emerging imaging biomarker for early degeneration assessment.

Objectives: The main objective of the study was to determine the normal subcarinal angle (SCA) in an adult patient, to compare the relationship of SCA with patient gender and age, and to correlate the SCA with body mass index (BMI).Methods: A total of 193 subjects (109 males and 85 females) in the age group of 20–60 years scheduled for computed tomography thorax at the Department of Radiodiagnosis and Imaging, Kasturba Hospital, Manipal, were included in the study. Plain images were reconstructed into 1.4 mm reformatted image and processed into minimum intensity projection, and measurement was taken using angle tool. Mean and standard deviation was used to determine normal SCA, and independent t-test was used to compare the relationship of SCA with patient’s age and gender. “Pearson correlation” was used to correlate SCA with BMI.Results- The mean SCA of the adult patient was 69.75±3.38°. The mean SCA in female was 69.90±4.07° and in male was 69.63±2.75°. In the age group of 20–40 years, the mean SCA was 70.38±3.85°, and in the age group of 41–60 years, it was 69.25±2.88°.Conclusion: There was no relationship between SCA and patient’s gender; however, the mean SCA of the age group 20–40 was found greater than that of 41–60 which was statistically significant. BMI has no significant correlation with SCA.