首页|Researchers at University of Utah Report New Data on Support Vector Machines (A Non-contrast Multi-parametric Mri Biomarker for Assessment of Mr-guided Focused Ultrasound Thermal Therapies)
Researchers at University of Utah Report New Data on Support Vector Machines (A Non-contrast Multi-parametric Mri Biomarker for Assessment of Mr-guided Focused Ultrasound Thermal Therapies)
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NETL
NSTL
IEEE
Fresh data on Support Vector Machines are presented in a new report. According to news reporting originating from Salt Lake City, Utah, by NewsRx correspondents, research stated, “<bold>Objective:</bold>We present the development of a non-contrast multi-parametric magnetic resonance (MPMR) imaging biomarker to assess treatment outcomes for magnetic resonance-guided focused ultrasound (MRgFUS) ablations of localized tumors. Images obtained immediately following MRgFUS ablation were inputs for voxel-wise supervised learning classifiers, trained using registered histology as a label for thermal necrosis. <bold >Methods: </bold >VX2 tumors in New Zealand white rabbits quadriceps were thermally ablated using an MRgFUS system under 3 T MRI guidance.” Financial support for this research came from Huntsman Cancer Foundation. Our news editors obtained a quote from the research from the University of Utah, “Animals were reimaged three days post-ablation and euthanized. Histological necrosis labels were created by 3D registration between MR images and digitized H&E segmentations of thermal necrosis to enable voxel-wise classification of necrosis. Supervised MPMR classifier inputs included maximum temperature rise, cumulative thermal dose (CTD), post-FUS differences in T2-weighted images, and apparent diffusion coefficient, or ADC, maps. A logistic regression, support vector machine, and random forest classifier were trained in red a leave-one-out strategy in test data from four subjects. <bold >Results: </bold >In the validation dataset, the MPMR classifiers achieved higher recall and Dice than a clinically adopted 240 cumulative equivalent minutes at 43 degrees C (CEM (43)) threshold (0.43) in all subjects.”
Salt Lake CityUtahUnited StatesNorth and Central AmericaEmerging TechnologiesMachine LearningSupervised LearningSupport Vector MachinesVector MachinesUniversity of Utah
NETL
NSTL
IEEE
