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| http://dx.doi.org/10.1148/radiol.2017174027 | DOI Listing |
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Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning-Based Prediction Models to Distinguish Glioblastomas from Single Brain Metastases.
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: The accurate and early distinction of glioblastomas (GBMs) from single brain metastases (BMs) provides a window of opportunity for reframing treatment strategies enabling optimal and timely therapeutic interventions. We sought to leverage physiologically sensitive parameters derived from diffusion tensor imaging (DTI) and dynamic susceptibility contrast (DSC)-perfusion-weighted imaging (PWI) along with machine learning-based methods to distinguish GBMs from single BMs. : Patients with histopathology-confirmed GBMs ( = 62) and BMs ( = 26) and exhibiting contrast-enhancing regions (CERs) underwent 3T anatomical imaging, DTI and DSC-PWI prior to treatment.
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Biology Department, Faculty of Science, Islamic University of Madinah, Madinah, Saudi Arabia.
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View Article and Find Full Text PDFThe role of a vaccine booster for a fractional order model of the dynamic of COVID-19: a case study in Thailand.
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Department of Applied Basic Subjects, Thatphanom College, Nakhon Phanom University, Nakhon Phanom, 48000, Thailand.
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