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This work is supported by grant PDC2023-145812-I00 (Project SAMPL2D), which is funded by MI- CIU/AEI/10.13039/501100011033 and by "Next Generation EU /PRTR".
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Casanova-Carvajal, OscarAutor o CoautorA Review of Artificial Intelligence-Based Systems for Non-Invasive Glioblastoma Diagnosis
Publicado en:Life. 15 (4): 643- - 2025-04-14 15(4), DOI: 10.3390/life15040643
Autores: Contreras, Kebin; Velez-Varela, Patricia E; Casanova-Carvajal, Oscar; Alvarez, Angel Luis; Urbano-Bojorge, Ana Lorena
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Resumen
Background: Glioblastoma multiforme (GBM) is an aggressive brain tumor with a poor prognosis. Traditional diagnosis relies on invasive biopsies, which pose surgical risks. Advances in artificial intelligence (AI) and machine learning (ML) have improved non-invasive GBM diagnosis using magnetic resonance imaging (MRI), offering potential advantages in accuracy and efficiency. Objective: This review aims to identify the methodologies and technologies employed in AI-based GBM diagnostics. It further evaluates the performance of AI models using standard metrics, highlighting both their strengths and limitations. Methodology: In accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, a systematic review was conducted across major academic databases. A total of 104 articles were identified in the initial search, and 15 studies were selected for final analysis after applying inclusion and exclusion criteria. Outcomes: The included studies indicated that the signal T1-weighted imaging (T1WI) is the most frequently used MRI modality in AI-based GBM diagnostics. Multimodal approaches integrating T1WI with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) have demonstrated improved classification performance. Additionally, AI models have shown potential in surpassing conventional diagnostic methods, enabling automated tumor classification and enhancing prognostic predictions.
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Impacto bibliométrico. Análisis de la aportación y canal de difusión
El trabajo ha sido publicado en la revista Life debido a la progresión y el buen impacto que ha alcanzado en los últimos años, según la agencia WoS (JCR), se ha convertido en una referencia en su campo. En el año de publicación del trabajo, 2025, se encontraba en la posición 26/109, consiguiendo con ello situarse como revista Q1 (Primer Cuartil), en la categoría Biology.
2025-07-08:
- Scopus: 1
Impacto y visibilidad social
Análisis de liderazgo de los autores institucionales
Este trabajo se ha realizado con colaboración internacional, concretamente con investigadores de: Colombia.