PURPOSE: To develop and validate interpretable machine learning models for differentiating glioblastoma (GB) from solitary brain metastasis (SBM) using radiomics features from contrast-enhanced T1-weighted MRI (CE-T1WI), and to compare the impact of ...
Glioblastoma is the most aggressive primary brain tumor, with poor prognosis following initial identification. Current diagnostic methods, including neuroimaging and molecular pathology, face several limitations in tumor delineation, differentiation ...
BACKGROUND: Glioblastoma (GBM) presents a significant clinical challenge due to its aggressive nature and extensive heterogeneity. Tumor purity, the proportion of malignant cells within a tumor, is an important covariate for understanding the disease...
OBJECTIVE: Interest has grown in combining radiology, pathology, genomic, and clinical data to improve the accuracy of diagnostic and prognostic predictions toward precision health. However, most existing works choose their datasets and modeling appr...
Glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) are common malignant brain tumors with similar radiological features, while the accurate and non-invasive dialgnosis is essential for selecting appropriate...
OBJECTIVE: Development of a deep learning model for accurate preoperative identification of glioblastoma and solitary brain metastases by combining multi-centre and multi-sequence magnetic resonance images and comparison of the performance of differe...
Accurate differentiation of pseudoprogression (PsP) from True Progression (TP) following radiotherapy (RT) in glioblastoma patients is crucial for optimal treatment planning. However, this task remains challenging due to the overlapping imaging chara...
Studies in health technology and informatics
May 15, 2025
Manual segmentation of histopathological images is both resource-intensive and prone to human error, particularly when dealing with challenging tumor types like Glioblastoma (GBM), an aggressive and highly heterogeneous brain tumor. The fuzzy borders...
BACKGROUND: Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.
Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and ...
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