ABSTRACT
Atherosclerosis is a significant health concern with a growing incidence worldwide. It is directly linked to an increased cardiovascular risk and to major adverse cardiovascular events, such as acute coronary syndromes. In this review, we try to assess the potential diagnostic role of biomarkers in the early identification of patients susceptible to the development of atherosclerosis and other adverse cardiovascular events. We have collected publications concerning already established parameters, such as low-density lipoprotein cholesterol (LDL-C), as well as newer markers, e.g., apolipoprotein B (apoB) and the ratio between apoB and apoA. Additionally, given the inflammatory nature of the development of atherosclerosis, high-sensitivity c-reactive protein (hs-CRP) or interleukin-6 (IL-6) are also discussed. Additionally, newer publications on other emerging components linked to atherosclerosis were considered in the context of patient evaluation. Apart from the already in-use markers (e.g., LDL-C), emerging research highlights the potential of newer molecules in optimizing the diagnosis of atherosclerotic disease in earlier stages. After further studies, they might be fully implemented in the screening protocols.
ABSTRACT
Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma (GB) remains the most common primary malignant brain tumor, with a median survival of 15 months. This paper presents literature data on GB imaging and the contribution of AI to the characterization and tracking of GB, as well as recurrence. Furthermore, from an imaging point of view, the differential diagnosis of these tumors can be problematic. How can an AI algorithm help with differential diagnosis? The integration of clinical, radiomics and molecular markers via AI holds great potential as a tool for enhancing patient outcomes by distinguishing brain tumors from mimicking lesions, classifying and grading tumors, and evaluating them before and after treatment. Additionally, AI can aid in differentiating between tumor recurrence and post-treatment alterations, which can be challenging with conventional imaging methods. Overall, the integration of AI into GB imaging has the potential to significantly improve patient outcomes by enabling more accurate diagnosis, precise treatment planning and better monitoring of treatment response.
