Mri Prognostic Features
Mri Prognostic Features - Related Videos
05:42
Visual Search for Features and Conjunctions
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University How do people find objects in cluttered visual scenes? Think, for example, of looking for keys on a messy desk, finding the ripest-looking fruit at the grocery store, locating your car when you can’t quite remember where you parked it, or finding an old friend at an airport exit gate. Clearly, an understanding of visual perception is going to play a role in any answers, and more specifically, an understanding of visual attention...
06:06
Optogenetic Functional MRI
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Cited by 15 •
2016
This protocol describes the steps and data analysis required to successfully perform optogenetic functional magnetic resonance imaging (ofMRI). ofMRI is a novel technique that combines high-field fMRI readout with optogenetic stimulation, allowing for cell type-specific mapping of functional neural circuits and their dynamics across the whole living brain.
07:21
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
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Cited by 186 •
2011
An accurate and practical method to measure parameters like strain in myocardial tissue is of great clinical value, since it has been shown, that strain is a more sensitive and earlier marker for contractile dysfunction than the frequently used parameter EF.
08:20
MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder
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Cited by 31 •
2015
Here we outline the procedure for MRI-guided repetitive transcranial magnetic stimulation to the dorsomedial prefrontal cortex as an experimental treatment for major depressive disorder.
06:58
Synthesis of an In vivo MRI-detectable Apoptosis Probe
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Cited by 3 •
2012
Early detection of apoptosis may identify at-risk cell populations in a variety of diseases. Here we demonstrate a method to link an early apoptosis-detection protein (Annexin V) to a MRI-detectable iron oxide nanoparticle (SPIO). This method may be extended to other proteins of interest to generate MRI-detectable molecular imaging probes.
FAQs
Their prognostic value comes from linking visible imaging patterns with underlying tissue behavior. Lesion size and shape may describe extent, while signal intensity, contrast enhancement, diffusion, and perfusion can provide clues about cellularity, vascularity, necrosis, or infiltration. Interpreting these characteristics together is therefore more informative than treating any single MRI observation as a complete prediction.
Clinical and laboratory information adds context that imaging alone may not capture. Radiologists and researchers can combine MRI findings with these data to stratify patients more meaningfully, rather than assigning risk from lesion appearance in isolation. This integrated approach supports prognostic assessment that is connected to the broader medical picture and can inform treatment-related decisions.
Quantitative MRI and radiomics extend visual assessment by converting imaging patterns into measurable biomarkers. Instead of relying only on descriptive impressions, investigators can analyze numerical representations of features and incorporate them into prognostic models. This can support more personalized risk estimates, although the usefulness of the resulting biomarkers depends on consistent imaging practices and appropriate validation.
A basic workflow starts with MRI acquisition, followed by assessment of lesion size, shape, signal intensity, enhancement, diffusion, and perfusion. The findings are then considered with clinical and laboratory data, and may be represented quantitatively for model development. Standardized acquisition is important because differences in how images are obtained can affect feature measurements and reduce comparability.
In treatment planning, these features can help stratify risk and characterize disease patterns relevant to clinical decisions. During care, comparing imaging findings over time can support monitoring of therapeutic response. The value is greatest when MRI-derived information is interpreted alongside other patient data, so imaging contributes to, rather than replaces, the broader assessment.
Validation is essential before a quantitative feature or radiomic pattern is used in a prognostic model. The model should rely on measurements obtained with standardized acquisition and demonstrate that its imaging-based predictions are reproducible and clinically meaningful. This requirement limits overinterpretation of attractive image patterns and helps assess whether a potential biomarker can support reliable medical risk stratification.