Applying chemical shift images (in-phase/opposed phased) for differentiating low-grade from high-grade glioma and comparison with magnetic resonance spectroscopy.

BACKGROUND: Grading gliomas is essential for treatment decisions and patient prognosis. In this study we evaluated the in-phase and out-of-phase sequences for distinguishing high-grade (HGG) from low-grade glioma (LGG) and the correlation with magnetic resonance spectroscopy (MRS) results. METHODS: This observational study comprised patients with brain tumors referred to our center for brain MRS. The gold standard for diagnosis was based on the World Health Organization (WHO) glioma classification. A standard tumor protocol was accomplished using a 1.5­T MRS scanner. Before contrast medium administration, extra in- and out-phase sequences were acquired. Three 20-30-mm2 oval regions of interest (ROIs) were placed in the solid component and the signal loss ratio (SLR) was calculated with the following formula: SLR tumor = (SI In phase - SI Opposed phase) / SI In phase Correlations and comparisons between groups were made using the Pearson, chi-square and, independent samples t tests. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performance. Statistical significance was set at p < 0.05. RESULTS: In total, 20 patients were included in the LGG and 13 were included in the HGG group. The mean SLR in the HGG and LGG groups was 3.66 ± 2.12 and 1.63 ± 1.86, respectively (p = 0.01). There was a statistically significant correlation between lipid lactate (0.48, p = 0.004) and free lipid (0.44, p = 0.009) concentrations on MRS with SLR. CONCLUSIONS: The SLR is a simple, rapid, and noninvasive marker for differentiating between LGG and HGG. There is a significant correlation with both the concentration and presence of free lipid and lipid-lactate peaks in MRS.

Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images.

INTRODUCTION: The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatment planning and initial evaluation of its effectiveness in GBM. This study aimed to design an intelligent system to classify glioblastoma patients based on hypoxia levels obtained from magnetic resonance images with the help of an artificial neural network (ANN). MATERIAL AND METHOD: MR images and PET measurements were available for this study. MR images were downloaded from the Cancer Imaging Archive (TCIA) database to classify glioblastoma patients based on hypoxia. The images in this database were prepared from 27 patients with glioblastoma on T1W + Gd, T2W-FLAIR, and T2W. Our designed algorithm includes various parts of pre-processing, tumor segmentation, feature extraction from images, and matching these features with quantitative parameters related to hypoxia in PET images. The system's performance is evaluated by categorizing glioblastoma patients based on hypoxia. RESULTS: The results of classification with the artificial neural network (ANN) algorithm were as follows: the highest sensitivity, specificity, and accuracy were obtained at 86.71, 85.99 and 83.17%, respectively. The best specificity was related to the T2W-EDEMA image with the tumor to blood ratio (TBR) as a hypoxia parameter. T1W-NECROSIS image with the TBR parameter also showed the highest sensitivity and accuracy. CONCLUSION: The results of the present study can be used in clinical procedures before treating glioblastoma patients. Among these treatment approaches, we can mention the radiotherapy treatment design and the prescription of effective drugs for the treatment of hypoxic tumors.