AIMC Topic: Brain Neoplasms

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Multi-modal MRI synthesis with conditional latent diffusion models for data augmentation in tumor segmentation.

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 ...
Humans Algorithms Magnetic Resonance Imaging Multimodal Imaging Image Interpretation, Computer-Assisted Brain Neoplasms Deep Learning Imaging, Three-Dimensional
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Brain tumor segmentation with deep learning: Current approaches and future perspectives.

Journal of neuroscience methods Mar 21, 2025
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...
Magnetic Resonance Imaging Humans Image Interpretation, Computer-Assisted Brain Neoplasms Neuroimaging Deep Learning
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Utilising Natural Language Processing to Identify Brain Tumor Patients for Clinical Trials: Development and Initial Evaluation.

World neurosurgery Mar 18, 2025
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...
Retrospective Studies Electronic Health Records Brain Neoplasms Humans Algorithms Clinical Trials as Topic Cohort Studies Natural Language Processing Patient Selection
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Dynamic glucose enhanced imaging using direct water saturation.

Magnetic resonance in medicine Mar 17, 2025
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...
Magnetic Resonance Imaging Glucose Humans Gray Matter Algorithms Male Brain Water Image Processing, Computer-Assisted White Matter Female Adult Brain Neoplasms
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A Learnable Prior Improves Inverse Tumor Growth Modeling.

IEEE transactions on medical imaging Mar 17, 2025
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 ...
Models, Biological Algorithms Brain Brain Neoplasms Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans
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Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients.

BMC cancer Mar 12, 2025
BACKGROUND: Early and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases is critical for guiding targeted therapy. This study aimed to develop a deep...
Deep Learning Humans Magnetic Resonance Imaging Adenocarcinoma of Lung Mutation Aged ROC Curve Brain Neoplasms Retrospective Studies ErbB Receptors Male Female Lung Neoplasms Adult Middle Aged Radiomics
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A multi-stage fusion deep learning framework merging local patterns with attention-driven contextual dependencies for cancer detection.

Computers in biology and medicine Mar 6, 2025
Cancer is a severe threat to public health. Early diagnosis of disease is critical, but the lack of experts in this field, the personal assessment process, the clinical workload, and the high level of similarity in disease classes make it difficult. ...
Deep Learning Lung Neoplasms Skin Neoplasms Brain Neoplasms Neoplasms Humans Neural Networks, Computer
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Efficient Brain Tumor Detection and Segmentation Using DN-MRCNN With Enhanced Imaging Technique.

Microscopy research and technique Mar 4, 2025
This article proposes a method called DenseNet 121-Mask R-CNN (DN-MRCNN) for the detection and segmentation of brain tumors. The main objective is to reduce the execution time and accurately locate and segment the tumor, including its subareas. The i...
Algorithms Humans Brain Neural Networks, Computer Brain Neoplasms Image Processing, Computer-Assisted
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MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.

Scientific reports Mar 3, 2025
We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene...
Machine Learning Prognosis Female Aged Adult Radiomics Middle Aged Humans ROC Curve Support Vector Machine Kaplan-Meier Estimate Glioma Magnetic Resonance Imaging Biomarkers, Tumor Hyaluronan Receptors Neoplasm Grading Brain Neoplasms Male
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Advances of artificial intelligence in clinical application and scientific research of neuro-oncology: Current knowledge and future perspectives.

Critical reviews in oncology/hematology Mar 1, 2025
Brain tumors refer to the abnormal growths that occur within the brain's tissue, comprising both primary neoplasms and metastatic lesions. Timely detection, precise staging, suitable treatment, and standardized management are of significant clinical ...
Brain Neoplasms Medical Oncology Artificial Intelligence Precision Medicine Tumor Microenvironment Humans
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