A Scoping Review on Cognition in Myelodysplastic Syndromes: Advances and Challenges.

Background/objectives: Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders characterized by ineffective hematopoiesis and a risk of progression to acute myeloid leukemia (AML). Cognitive impairments, including deficits in memory, attention, and executive function, are frequently reported in MDS patients. These impairments are linked to systemic inflammation, neurotoxic treatment effects, and the psychological burden of chronic disease.

Exploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis.

Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH).

Magnetic Resonance Imaging Techniques for Post-Treatment Evaluation After External Beam Radiation Therapy of Prostate Cancer: Narrative Review.

: This study aimed to investigate the prognostic value of advanced techniques of magnetic resonance imaging (MRI) biochemical recurrence (BCR) after radiotherapy in patients with prostate cancer (PCa). : A comprehensive literature review was conducted to evaluate the role of MRI in detecting BCR of PCa patients after external beam radiation therapy. : National guidelines do not recommend imaging techniques in clinical follow-up PCa.

Evaluating Brain Tumor Detection with Deep Learning Convolutional Neural Networks Across Multiple MRI Modalities.

Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of anatomical structures. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown potential in augmenting MRI-based diagnostic accuracy for brain tumor detection.

Predictive Markers of Post-Stroke Cognitive Recovery and Depression in Ischemic Stroke Patients: A 6-Month Longitudinal Study.

The growing number of stroke survivors face physical, cognitive, and psychosocial impairments, making stroke a significant contributor to global disability. Various factors have been identified as key predictors of post-stroke outcomes. The aim of this study was to develop a standardized predictive model that integrates various demographic and clinical factors to better predict post-stroke cognitive recovery and depression in patients with ischemic stroke (IS).