نتایج جستجو برای: radiomic prediction mri
تعداد نتایج: 357714 فیلتر نتایج به سال:
In medical imaging for clinical diagnosis and biomedical research magnetic resonance imaging (MRI) is one of the prevailing techniques. A lot of subjects of MRI are annoyed by the loud noise that MRI generates. In the present study computer simulation of active control of the noise induced by MRI was performed. The sound generated by the measurement sequences for the brain was recorded with an ...
To assess pre-therapeutic MRI-based radiomic analysis to predict the pathological complete response neoadjuvant chemotherapy (NAC) in women with early triple negative breast cancer (TN). This monocentric retrospective study included 75 TN female patients MRI (T1-weighted, T2-weighted, diffusion-weighted and dynamic contrast enhancement images) performed before NAC. For each patient, tumor(s) pa...
PET-based radiomics have been used to noninvasively quantify the metabolic tumor phenotypes; however, little is known about the relationship between these phenotypes and underlying somatic mutations. This study assessed the association and predictive power of 18F-FDG PET-based radiomic features for somatic mutations in non-small cell lung cancer patients. Methods: Three hundred forty-eight non-...
Determining histological subtypes, such as invasive ductal and lobular carcinomas (IDCs ILCs) immunohistochemical markers, estrogen response (ER), progesterone (PR), the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis emerging a non-invasive substitute for biopsy to determine these signatures. We explore effectiveness of radiomics-based CNN (con...
We propose using multi-scale image textures to investigate links between neuroanatomical regions and clinical variables in MRI. Texture features are derived at multiple scales of resolution based on the Laplacian-of-Gaussian (LoG) filter. Three quantifier functions (Average, Standard Deviation and Entropy) are used to summarize texture statistics within standard, automatically segmented neuroan...
BACKGROUND AND PURPOSE Despite availability of advanced imaging, distinguishing radiation necrosis from recurrent brain tumors noninvasively is a big challenge in neuro-oncology. Our aim was to determine the feasibility of radiomic (computer-extracted texture) features in differentiating radiation necrosis from recurrent brain tumors on routine MR imaging (gadolinium T1WI, T2WI, FLAIR). MATER...
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