RESUMEN
Glioblastoma, IDH-wild type, the most common malignant primary central nervous system tumor, represents a formidable challenge in clinical management due to its poor prognosis and limited therapeutic responses. With an evolving understanding of its underlying biology, there is an urgent need to identify prognostic molecular groups that can be subject to targeted therapy. This study established a cohort of 124 sequential glioblastomas from a tertiary hospital and aimed to find correlations between molecular features and survival outcomes. Comprehensive molecular characterization of the cohort revealed prevalent alterations as previously described, such as TERT promoter mutations and involvement of the PI3K-Akt-mTOR, CK4/6-CDKN2A/B-RB1, and p14ARF-MDM2-MDM4-p53 pathways. MGMT promoter methylation is a significant predictor of improved overall survival, aligned with previous data. Conversely, age showed a marginal association with higher mortality. Multivariate analysis to account for the effect of MGMT promoter methylation and age showed that, in contrast to other published series, this cohort demonstrated improved survival for tumors harboring PTEN mutations, and that there was no observed difference for most other molecular alterations, including EGFR amplification, RB1 loss, or the coexistence of EGFR amplification and deletion/exon skipping (EGFRvIII). Despite limitations in sample size, this study contributes data to the molecular landscape of glioblastomas, prompting further investigations to examine these findings more closely in larger cohorts.
Asunto(s)
Neoplasias Encefálicas , Glioblastoma , Isocitrato Deshidrogenasa , Humanos , Glioblastoma/genética , Glioblastoma/mortalidad , Glioblastoma/patología , Persona de Mediana Edad , Masculino , Femenino , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/mortalidad , Neoplasias Encefálicas/patología , Anciano , Adulto , Isocitrato Deshidrogenasa/genética , Mutación , Estudios de Cohortes , Pronóstico , Biomarcadores de Tumor/genética , Metilación de ADN/genética , Adulto Joven , Anciano de 80 o más Años , Regiones Promotoras Genéticas/genética , Análisis de SupervivenciaRESUMEN
BACKGROUND: Malignant Primary Brain Tumor (MPBT) and Metastatic Brain Tumor (MBT) are the most common types of brain tumors, which require different management approaches. Magnetic Resonance Imaging (MRI) is the most frequently used modality for assessing the presence of these tumors. The utilization of Deep Learning (DL) is expected to assist clinicians in classifying MPBT and MBT more effectively. OBJECTIVE: This study aims to examine the influence of MRI sequences on the classification performance of DL techniques for distinguishing between MPBT and MBT and analyze the results from a medical perspective. METHODS: Total 1,360 images performed from 4 different MRI sequences were collected and preprocessed. VGG19 and ResNet101 models were trained and evaluated using consistent parameters. The performance of the models was assessed using accuracy, sensitivity, and other precision metrics based on a confusion matrix analysis. RESULTS: The ResNet101 model achieves the highest accuracy of 83% for MPBT classification, correctly identifying 90 out of 102 images. The VGG19 model achieves an accuracy of 81% for MBT classification, accurately classifying 86 out of 102 images. T2 sequence shows the highest sensitivity for MPBT, while T1C and T1 sequences exhibit the highest sensitivity for MBT. CONCLUSIONS: DL models, particularly ResNet101 and VGG19, demonstrate promising performance in classifying MPBT and MBT based on MRI images. The choice of MRI sequence can impact the sensitivity of tumor detection. These findings contribute to the advancement of DL-based brain tumor classification and its potential in improving patient outcomes and healthcare efficiency.