Background: Diagnosis of appendicitis in children is clinically challenging. Computed tomography (CT) is the gold standard for diagnosis; however, radiation exposure early in life is a concern with this technique. Therefore, in this study, we aimed to evaluate the diagnostic reliability of low-dose CT, pediatric appendicitis score (PAS), and abdominal ultrasound (US) in children with acute appendicitis, to reach a safe diagnosis. Patients and methods: This retrospective study was conducted on 140 children who were admitted with clinically suspected acute appendicitis (45 with positive appendicitis and 95 children with negative appendicitis). Low-dose CT was performed, and PAS was retrospectively calculated for all subjects. US was initially performed for 38 subjects. All results were compared with the final diagnosis reached by an operative, histopathological analysis and follow-up. Results: Low-dose CT showed a sensitivity, specificity, and accuracy of 97.8%, 100%, and 99.3%, respectively. At a cutoff value ≥5, PAS showed a sensitivity, specificity, and accuracy of 95%, 84%, and 89%, respectively. Abdominal US examination showed sensitivity, specificity, and accuracy of 55.6%, 85%, and 71%, respectively. Implementing Poortman’s model resulted in higher accuracy (92%) of US. There was a significant difference in accuracy between a low-dose CT and PAS on one side and between Poortman’s model and US examination on the other side. A diagnostic scheme was suggested using PAS as the excluding tool (PAS ≤2 send home and ≥7 send directly to operation) followed by US examination and reserving low-dose CT for inconclusive cases. This scheme would eliminate the use of CT for at least 33.7% and in 7 cases who had initial US examination. Conclusion: Although CT remains the most accurate and less operator-dependent diagnostic tool for pediatric appendicitis, the radiation hazards could however be minimized using PAS as an excluding tool and US as the primary imaging modality followed by low-dose CT for inconclusive cases only.

Objective: The objective of this study is to evaluate the role of diffusion-weighted imaging (DWI) in assessment of the activity of Crohn disease (CD) and to explore differences between DWI in 3 T and 1.5 T. Methods: Postcontrast magnetic resonance enterography with DWI of 72 patients with pathological proof of CD was retrospectively evaluated for restricted diffusion qualitatively and quantitavely in 3 T (n = 40) and 1.5 T (n = 32). Magnetic resonance activity score of 7 or higher was used as reference of activity. Results: Fifty-five patients had active lesions. Diffusion-weighted imaging hyperintensity showed sensitivity (100%, 100%) and specificity (88.89%, 100%) in 1.5/3 T for activity assessment. Mean ± SD apparent diffusion coefficient for active lesions was 1.21 ± 0.42 and 1.28 ± 0.59 × 10⁻³ mm²/s in 1.5 and 3 T, respectively. The proposed cutoff values of 1.35 and 1.38 × 10⁻³ mm²/s in 1.5 and 3 T, respectively, had sensitivity (80%, 93%), specificity (100%, 90%), accuracy (88%, 93%), and no significant difference in accuracy between 1.5/3 T (P = 0.48). Conclusions: Diffusion-weighted imaging hypersensitivity and apparent diffusion coefficient values accurately assessed the activity of CD. No significant statistical difference in diagnostic accuracy was detected between 1.5 and 3 T.

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.

Pulmonary nodules are the precursors of bronchogenic carcinoma, its early detection facilitates early treatment which save a lot of lives. Unfortunately, pulmonary nodule detection and classification are liable to subjective variations with high rate of missing small cancerous lesions which opens the way for implementation of artificial intelligence (AI) and computer aided diagnosis (CAD) systems. The field of deep learning and neural networks is expanding every day with new models designed to overcome diagnostic problems and provide more applicable and simply used models. We aim in this review to briefly discuss the current applications of AI in lung segmentation, pulmonary nodule detection and classification.

To describe MRI and DWI spectrum of brain and optic pathway changes in cases who survived acute methanol poisoning and explore whether there is correlation between imaging features and long-term visual and neurological sequelae. We retrospectively reviewed the conventional MRI and DWI of 58 consecutive patients with methanol poisoning. All patients were examined in the chronic phase. Optic nerve enhancement and atrophy were detected in 33 cases (56.9%). Degree of optic nerve atrophy correlated well with cupping and time lag since initial exposure to methanol. Bilateral putamen necrosis was present in 45 cases (77.6%), 19 showed asymmetrical involvement, and caudate was involved in 6 cases. Asymmetrical necrosis and caudate involvement were correlated with higher grade of neurological deficit. Twenty-one cases (36.2%) showed combination of bilateral putamen necrosis and optic nerve enhancement. Subcortical white matter high SI was detected in 25 patients (43.1%). DWI clearly depicted putamen necrosis with non-restricted pattern. Spectrum of residual MRI Findings in patients who survived methanol poisoning included bilateral optic nerve atrophy and enhancement, bilateral putamen and caudate necrosis as well as subcortical white matter high SI at T2WI. Diffusion WI did not have additional value in chronic stage.

Purpose: To evaluate the ability of the Intravoxel Incoherent Motion (IVIM) and monoexponentially ADC in renal allograft function in the early and late phases of transplantation, and to predict their effectiveness in discrimination of the graft pathology. Methods: This is a prospective study included participants scanned with quantitative diffusion and perfusion sequences on a 3-T MR scanner (Philips, Ingenia); the ADC and IVIM parameters; were calculated. Correlations and regression analysis with the eGFR, transplantation periods, and pathology were assessed. Results: This study included 105 renal allograft recipients (85 males, and 20 females with mean age = 32.4 ± 11.9 years and age range = 22–61 years). There was a significant positive correlation between the whole parameters of the ADC and IVIM with eGFR however, the cortical parameters showed higher significant correlation coefficients (p < 0.001). Regression analysis revealed the most significant model can predict eGFR groups included cortical pseudo diffusion (D*) and cortical ADC (p < 0.001). In graft dysfunction eGFR was 61.5 ml/min and normal graft was 64 ml/min. This model demonstrates a high performance of an AUC 96% [0.93–0.97]. In the late transplantation, there is a higher correlation with D* compared to ADC, p-values = 0.001. Conclusion: IVIM and ADC Values are significant biomarkers for renal allograft function assessment, cortical ADC, and D* had the highest performance even in situations with mild impairment that is not affect the eGFR yet as cases of proteinuria with normal eGFR. Furthermore, D* is superior to ADC in the late assessment of the renal transplant. Graphical