BACKGROUND: High-grade gliomas are among the most aggressive and deadly brain tumors, highlighting the critical need for improved prognostic markers and predictive models. Recent studies have identified the expression of IL7R as a significant risk fa...
Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited. That is mainly due to the fact that the features ...
O-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a crucial biomarker in glioblastoma (GBM) that influences response to temozolomide. Traditional detection methods, such as gene sequencing, are time-consuming and limited to postope...
BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focu...
Preoperative classification of brain tumors is critical to developing personalized treatment plans, however existing classification methods rely on manual intervention and often have problems with efficiency and accuracy, which may lead to misdiagnos...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 21, 2025
Multimodality is often necessary for improving object segmentation tasks, especially in the case of multilabel tasks, such as tumor segmentation, which is crucial for clinical diagnosis and treatment planning. However, a major challenge in utilizing ...
BACKGROUND: Accurate brain tumor segmentation from MRI images is critical in the medical industry, directly impacts the efficacy of diagnostic and treatment plans. Accurate segmentation of tumor region can be challenging, especially when noise and ab...
BACKGROUND: Identifying patients eligible for clinical trials through eligibility screening is time and resource-intensive. Natural Language Processing (NLP) models may enhance clinical trial screening by extracting data from Electronic Health Record...
PURPOSE: Dynamic glucose enhanced (DGE) MRI studies employ CEST or spin lock (CESL) to study glucose uptake. Currently, these methods are hampered by low effect size and sensitivity to motion. To overcome this, we propose to utilize exchange-based li...
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a ...
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