Evaluation of Two Post-Processing Analysis Methods of Proton Magnetic Resonance Spectroscopy in Glioma Tumors.

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. 1H-MRS is a reliable and non-invasive tool used to study glioma. However, the metabolite spectra obtained by 1H-MRS requires a specific quantification procedure for post-processing. According to our knowledge, no comparisons have yet been made between spectrum analysis software for quantification of gliomas metabolites. Objective: Current study aims to evaluate the difference between this two common software in quantifying cerebral metabolites. Material and Methods: In this analytical study, we evaluate two post-processing software packages, java-based graphical for MR user interface packages (jMRUI) and totally automatic robust quantitation in NMR (TARQUIN) software. 1H-MRS spectrum from the brain of patients with gliomas tumors was collected for post-processing. AMARES algorithms were conducted to metabolite qualification on jMRUI software, and TARQUIN software were implemented with automated quantification algorithms. The study included a total of 30 subjects. For quantification, subjects were divided into a normal group (n=15) and group of gliomas (n=15). Results: When calculated by TARQUIN, the mean metabolites ratio was typically lower than by jMRUI. While, the mean ratio of metabolites varied when quantified by jMRUI vs. TARQUIN, both methods apparent clinical associations. Conclusion: TARQUIN and jMRUI are feasible choices for the post-processing of cerebral MRS data obtained from glioma tumors.

Monitoring of the choline/lipid ratio by 1H-MRS can be helpful for prediction and early detection of tumor response to nano-photo-thermal therapy.

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. We considered various treatment groups including gold nanoparticles (AuNPs), laser, and the combination of AuNPs and laser (NPTT group). Therapeutic effects on CT26 colon tumor-bearing BALB/c mice were studied by looking at alterations that happened in 1H-MRS signals and tumor size after conducting treatment procedures. In MRS studies, the alterations of choline and lipid concentrations and their ratio were investigated. Having normalized the metabolite peak to water peak, we found a significant decrease in choline concentration post-NPTT (from (1.25 ± 0.05) × 10-3 to (0.43 ± 0.04) × 10-3), while the level of lipid concentration in the tumor was slightly increased (from (2.91 ± 0.23) × 10-3 to (3.52 ± 0.31) × 10-3). As a result, the choline/lipid ratio was significantly decreased post-NPTT (from 0.41 ± 0.11 to 0.11 ± 0.02). Such alterations appeared just 1 day after NPTT. Tumor shrinkage in all groups was studied and significant changes were significantly detectable on day 7 post-NPTT procedure. In conclusion, the study of choline/lipid ratio using 1H-MRS may help us estimate what happens in a tumor treated by the NPTT method. Such an in vivo assessment is interestingly feasible as soon as just 1 day post-NPTT. This would undoubtedly help the oncologists make a more precise decision about treatment planning strategies. Monitoring of the choline/lipid ratio by 1H-MRS can be helpful for prediction and early detection of response to nano-photo-thermal therapy.

1H-MRS application in the evaluation of response to photo-thermal therapy using iron oxide-gold core-shell nanoparticles, an in vivo study.

BACKGROUND: 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. METHODS: In the present project, we conducted an in vivo study to assess the efficacy of 1H-MRS as a noninvasive technique to evaluate the response to treatment in the early hours following PTT. The BALB/c mice subcutaneously bearing tumor cells (CT26 cell line) were scanned by 1H-MRS before and after PTT. Iron oxide-gold core-shell (Fe3O4@Au) as PTT agent was injected into intra-peritoneal at first and then irradiated by NIR laser. Single-voxel Point RESolved Spectroscopy (PRESS) sequence (TE = 144) was used, and metabolites alternations were evaluated by the non-parametric Wilcoxon test. Besides, Nanoparticle (NP) relaxometry was conducted for negative contrast agents' potentials. RESULTS: MRS choline (Cho) peak dramatically reduced 24 h post-PTT (p = 0.01) and lipid peak as a marker for necrosis of tumor elevated (p = 0.01) just in group 3 (NPs injection + laser irradiation) 24 h after the procedure. CONCLUSION: 1H-MRS showed its potential as a method in detecting the changes in metabolites and revealing the outcome accurately. Response to photo-thermal therapy evaluation was achievable only one day after PTT and proved by a 10-day follow-up of the tumor size. Iron oxide-gold core-shell can also be used as a negative contrast agent in MRI images during therapy.

Subspace Clustering of Physiological Data From Acute Traumatic Brain Injury Patients: Retrospective Analysis Based on the PROTECT III Trial.

BACKGROUND: 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. Specifically, we present a case study to demonstrate the feasibility and applicability of subspace clustering techniques for extracting patterns from data collected from TBI patients. METHODS: Data for this study were obtained from the Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment-Phase III (PROTECT III) trial, which included a cohort of 882 TBI patients. We applied subspace-clustering methods (density-based, cell-based, and clustering-oriented methods) to this data set and compared the performance of the different clustering methods. RESULTS: The analyses showed the following three clusters of laboratory physiological data: (1) international normalized ratio (INR), (2) INR, chloride, and creatinine, and (3) hemoglobin and hematocrit. While all subclustering algorithms had a reasonable accuracy in classifying patients by mortality status, the density-based algorithm had a higher F1 score and coverage. CONCLUSIONS: Clustering approaches serve as an important step for phenotype definition and validation in clinical domains such as TBI, where patient and injury heterogeneity are among the major reasons for failure of clinical trials. The results from this study provide a foundation to develop scalable clustering algorithms for further research and validation.