Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Clinical/methodological problem: The identification of clinically significant prostate carcinomas while avoiding overdiagnosis of low-malignant tumors is a challenge in routine clinical practice. Standard radiologic procedures: Multiparametric magnetic resonance imaging (MRI) of the prostate acquired and interpreted according to PI-RADS (Prostate Imaging Reporting and Data System Guidelines) is accepted as a clinical standard among urologists and radiologists. Methodological innovations: The PI-RADS guidelines have been newly updated to version 2.1 and, in addition to more precise technical requirements, include individual changes in lesion assessment. Performance: The PI-RADS guidelines have become crucial in the standardization of multiparametric MRI of the prostate and provide templates for structured reporting, facilitating communication with the referring physician. Evaluation: The guidelines, now updated to version 2.1, represent a refinement of the widely used version 2.0. Many aspects of reporting have been clarified, but some previously known limitations remain and require further improvement of the guidelines in future versions.

The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.

Amyloidosis is a systemic disorder produced by the deposition of insoluble protein fibrils that fold and deposit in the myocardium. Patients with amyloidosis and cardiac involvement have higher mortality than patients without cardiac involvement. The two most prevalent forms of amyloidosis associated with cardiac involvement are AL amyloidosis, due to the deposition of immunoglobulin light chains, and ATTR amyloidosis, due to the deposition of the transthyretin (TTR) protein in mutated or senile form. This article aims to review the different cardiac imaging modalities (echocardiography, cardiac magnetic resonance imaging, nuclear medicine and tomography) that allow to determine the severity of cardiac involvement in patients with amyloidosis, the type of amyloidosis and its prognosis. Finally, we suggest a diagnostic algorithm to determine cardiac involvement in amyloidosis adapted to locally available diagnostic tools, with a practical and clinical approach.

The integration of medical images is the process of registering and fusing them to obtain a greater amount of diagnostic information. In this work an analysis is performed for the integration of images obtained through computed axial tomography and magnetic resonance imaging, for which a tool was developed in the Matlab program, where the registration is implemented through equivalent features; in addition, the pairs of images are compared by several fusion rules, with a view to identify the best algorithm in which the resulting fused image contains the most information from the original representations.

Background: Multiple sclerosis is often a longitudinal disease continuum with an initial relapsing-remitting phase (RRMS) and later secondary progression (SPMS). Most currently approved therapies are not sufficiently effective in SPMS. Early detection of SPMS conversion is therefore critical for therapy selection. Important decision-making tools may include testing of partial cognitive performance and magnetic resonance imaging (MRI). Aim of the work: To demonstrate the importance of cognitive testing and MRI for the prediction and detection of SPMS conversion. Elaboration of strategies for follow-up and therapy management in practice, especially in outpatient care. Material and methods: Review based on an unsystematic literature search. Results: Standardized cognitive testing can be helpful for early SPMS diagnosis and facilitate progression assessment. Annual use of sensitive screening tests such as Symbol Digit Modalities Test (SDMT) and Brief Visual Memory Test-Revised (BVMT-R) or the Brief International Cognitive Assessment for MS (BICAMS) test battery is recommended. Persistent inflammatory activity on MRI in the first three years of disease and the presence of cortical lesions are predictive of SPMS conversion. Standardized MRI monitoring for features of progressive MS can support clinically and neurocognitively based suspicion of SPMS. Discussion: Interdisciplinary care of MS patients by clinically skilled neurologists, supported by neuropsychological testing and MRI, has a high value for SPMS prediction and diagnosis. The latter allows early conversion to appropriate therapies, as SPMS requires different interventions than RRMS. After drug switching, clinical, neuropsychological, and imaging vigilance allows stringent monitoring for neuroinflammatory and degenerative activity as well as treatment complications.