Multimodal MRI has an important contribution to cancer research. This technique is completely non-invasive and non-ionizing, it provides major information for diagnosis, and answers the questions of therapists before, during, and after treatment. Hence, in this paper we review the interest of these MRI modalities and their impact on the diagnosis, during and after therapeutic care of brain tumors. The MRI modalities allow specifying the localization of the expanding pathological tumoral process, the differential diagnosis between brain tumors and confined lesions of different categories, the diagnosis of the tumoral type of the lesion, assessing the histological grade in cases of glial tumor, and its local extension as well as the therapeutic follow-up. The multimodal MRI approach has a major contribution to the advanced care of brain tumors.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
CDK5: Insights into its roles in diseases.
Mol Biol Rep
January 2025
Institute of Pathogenic Biology, Guilin Medical University, Guilin, 541199, China.
Cyclin-dependent kinase 5 (CDK5), a unique member of the CDK family, is a proline-directed serine/threonine protein kinase with critical roles in various physiological and pathological processes. Widely expressed in the central nervous system, CDK5 is strongly implicated in neurological diseases. Beyond its neurological roles, CDK5 is involved in metabolic disorders, psychiatric conditions, and tumor progression, contributing to processes such as proliferation, migration, immune evasion, genomic stability, and angiogenesis.
View Article and Find Full Text PDFCoati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network.
Electromagn Biol Med
January 2025
Department of Computer Applications, Kalasalingam Academy of Research and Education - Deemed to be University, Krishnankoil, India.
Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues.
View Article and Find Full Text PDFAberrant FGF signaling promotes granule neuron precursor expansion in SHH subgroup infantile medulloblastoma.
Elife
January 2025
Department of Neurology, Weill Institute for Neuroscience, University of California San Francisco, San Francisco, United States.
Mutations in Sonic Hedgehog (SHH) signaling pathway genes, for example, (SUFU), drive granule neuron precursors (GNP) to form medulloblastomas (MB). However, how different molecular lesions in the Shh pathway drive transformation is frequently unclear, and mutations in the cerebellum seem distinct. In this study, we show that fibroblast growth factor 5 (FGF5) signaling is integral for many infantile MB cases and that expression is uniquely upregulated in infantile MB tumors.
View Article and Find Full Text PDFMedulloblastoma Molecular Subgrouping and Outcomes Data of a Single Center From a Low- and Middle-Income Country.
Pediatr Blood Cancer
January 2025
The Hospital for Sick Children, University of Toronto, Toronto, Canada.
Introduction: Medulloblastoma (MB) is the most common malignant childhood brain tumor. Molecular subgrouping of MB has become a major determinant of management in high-income countries. Subgrouping is still very limited in low- and middle-income countries (LMICs), and its relevance to management with the incorporation of risk stratification (low risk, standard risk, high risk, and very high risk) has yet to be evaluated in this setting.
View Article and Find Full Text PDFUtilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases.
Front Neurol
January 2025
Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Objective: To develop a machine learning-based clinical and/or radiomics model for predicting the primary site of brain metastases using multiparametric magnetic resonance imaging (MRI).
Materials And Methods: A total of 202 patients (87 males, 115 females) with 439 brain metastases were retrospectively included, divided into training sets (brain metastases of lung cancer [BMLC] = 194, brain metastases of breast cancer [BMBC] = 108, brain metastases of gastrointestinal tumor [BMGiT] = 48) and test sets (BMLC = 50, BMBC = 27, BMGiT = 12). A total of 3,404 quantitative image features were obtained through semi-automatic segmentation from MRI images (T1WI, T2WI, FLAIR, and T1-CE).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!