Arterial spin labeling MRI in patients undergoing carotid artery revascularization: a systematic review of the hemodynamic changes and clinical implications.

Objectives: Arterial spin labeling (ASL) MRI is a non-invasive imaging modality that measures cerebral blood flow (CBF) without the need for contrast agents or radiation, offering insights into hemodynamic changes. Carotid revascularization procedures, carotid endarterectomy and carotid artery stenting, aim to improve cerebral perfusion and reduce the risk of ischemic events. This study explores ASL's clinical potential in assessing CBF changes in carotid stenosis patients prior to revascularization procedures.

Integrating standard epilepsy protocol, ASL-perfusion, MP2RAGE/EDGE and the MELD-FCD classifier in the detection of subtle epileptogenic lesions: a 3 Tesla MRI pilot study.

Background: Malformations of cortical development (MCDs) in children with focal epilepsy pose significant diagnostic challenges, and a precise radiological diagnosis is crucial for surgical planning. New MRI sequences and the use of artificial intelligence (AI) algorithms are considered very promising in this regard, yet studies evaluating the relative contribution of each diagnostic technique are lacking.

Methods: The study was conducted using a dedicated "EPI-MCD MR protocol" with a 3 Tesla MRI scanner in patients with focal epilepsy and previously negative MRI.

Advances in magnetic resonance imaging for the assessment of paediatric focal epilepsy: a narrative review.

Background And Objective: Epilepsy affects approximately 50 million people worldwide, with 30-40% of patients not responding to medication, necessitating alternative therapies such as surgical intervention. However, the accurate localization of epileptogenic lesions, particularly in pediatric magnetic resonance imaging (MRI)-negative drug-resistant epilepsy, remains a challenge. This paper reviews advanced neuroimaging techniques aimed at improving the detection of such lesions to enhance surgical outcomes.

Segmental Distribution of Hepatocellular Carcinoma in Cirrhotic Livers.

Background: To evaluate the segmental distribution of hepatocellular carcinoma (HCC) according to Couinaud’s anatomical division in cirrhotic patients. Methods: Between 2020 and 2021, a total of 322 HCC nodules were diagnosed in 217 cirrhotic patients who underwent computed tomography (CT) or magnetic resonance imaging (MRI) for the evaluation of suspicious nodules (>1 cm) detected during ultrasound surveillance. For each patient, the segmental position of the HCC nodule was recorded according to Couinaud’s description.

Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging.

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice.

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management.

Tumor-Associated Macrophages and Inflammatory Microenvironment in Gastric Cancer: Novel Translational Implications.

Gastric cancer (GC) represents the fifth most frequently diagnosed cancer worldwide, with a poor prognosis in patients with advanced disease despite many improvements in systemic treatments in the last decade. In fact, GC has shown resistance to several treatment options, and thus, notable efforts have been focused on the research and identification of novel therapeutic targets in this setting. The tumor microenvironment (TME) has emerged as a potential therapeutic target in several malignancies including GC, due to its pivotal role in cancer progression and drug resistance.