Background: Magnetic resonance spectroscopy (MRS) is a non-invasive diagnostic and the neuroimaging method of choice for the noninvasive monitoring of brain metabolism in patients with glioma tumors. H-MRS is a reliable and non-invasive tool used to study glioma. However, the metabolite spectra obtained by H-MRS requires a specific quantification procedure for post-processing.
View Article and Find Full Text PDFBackground: Photo-thermal therapy (PTT) has been at the center of attention as a new method for cancer treatment in recent years. It is important to predict the response to treatment in the PTT procedure. Using magnetic resonance spectroscopy (MRS) can be considered a novel technique in evaluating changes in metabolites resulted from PTT.
View Article and Find Full Text PDFBackground: With advances in digital health technologies and proliferation of biomedical data in recent years, applications of machine learning in health care and medicine have gained considerable attention. While inpatient settings are equipped to generate rich clinical data from patients, there is a dearth of actionable information that can be used for pursuing secondary research for specific clinical conditions.
Objective: This study focused on applying unsupervised machine learning techniques for traumatic brain injury (TBI), which is the leading cause of death and disability among children and adults aged less than 44 years.
Nanotechnology-based photothermal therapy (NPTT) is a new emerging modality of cancer therapy. To have the right prediction and early detection of response to NPTT, it is necessary to get rapid feedback from a tumor treated by NPTT procedure and stay informed of what happens in the tumor site. We performed this study to find if proton magnetic resonance spectroscopy (1H-MRS) can be well responsive to such an imperative requirement.
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