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    MMP-2 expression and correlation with pathology and MRI of glioma. Zhang Hui,Ma Yunxia,Wang Haibao,Xu Liyan,Yu Yongqiang Oncology letters The expression of matrix metalloproteinase-2 (MMP-2) in brain glioma and its correlation with patients' clinicopathological characteristics and magnetic resonance imaging (MRI) features were investigated. A total of 104 patients with brain glioma admitted and treated in the First Affiliated Hospital of Anhui Medical University from June 2010 to September 2014 were randomly enrolled. MRI examination was performed before operation. Immunohistochemistry (IHC) was used to detect the expression levels of MMP-2 in brain glioma tissues and paired normal brain tissues after operation and to analyze the associations of MMP-2 expression with the clinicopathological characteristics of brain glioma and survival time of patients. The relationship between MMP-2 expression and preoperative MRI features of glioma was analyzed. The positive rate of MMP-2 expression in brain glioma was 73.08% (76/104), while that in paired normal brain tissues was only 12.5% (13/104), obviously lower than that in brain glioma tissues (P<0.05). The MMP-2 expression in the body of glioma was not related to the patients' sex, age, tumor location and pathological type (P>0.05), but there was a significant correlation with the tumor diameter and pathological grade of the patients (P<0.05). Analysis by Cox model suggested that tumor diameter, pathological grade and MMP-2 were independent prognostic factors for glioma (P<0.05). The overall survival (OS) of patients in the positive MMP-2 expression group was 16.4 months, while the OS in the negative MMP-2 expression group was 20.16 months, and the difference between the two groups was statistically significant (P<0.05). The positive expression of MMP-2 in glioma was closely related to the uniformity of MRI signal for tumor, tumor diameter, severity of peritumoral edema, degree of enhancement and pathological grade of tumor (P<0.05). MMP-2 is highly expressed in brain glioma, and it is a negative factor for prognosis. Therefore, the MRI manifestations of glioma can reflect to some extent the intensity of MMP-2 expression. 10.3892/ol.2018.9806
    On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images. Sengupta Anirban,Agarwal Sumeet,Gupta Pradeep Kumar,Ahlawat Sunita,Patir Rana,Gupta Rakesh Kumar,Singh Anup European journal of radiology PURPOSE:High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and non-contrast enhancing tumor is difficult as both appear hyperintense in T-W/FLAIR images. Most studies involving differentiation between vasogenic edema and non-enhancing tumor consider radiologist-based tumor delineation as the ground truth. However, analysis by a radiologist can be subjective and there remain both inter- and intra-rater differences. The objective of the current study is to develop a methodology for differentiation between non-enhancing tumor and vasogenic edema in HGG patients based on T perfusion MRI parameters, using a ground truth which is independent of a radiologist's manual delineation of the tumor. MATERIAL AND METHODS:This study included 9 HGG patients with pre- and post-surgery MRI data and 9 metastasis patients with pre-surgery MRI data. MRI data included conventional T-W, T-W, and FLAIR images and DCE-MRI dynamic images. In this study, the authors hypothesize that surgeried non-enhancing FLAIR hyperintense tissue, which was obtained using pre- and post-surgery MRI images of glioma patients, should be largely comprised of non-enhancing tumor. Hence this could be used as an alternative ground truth for the non-enhancing tumor region. Histological examination of the resected tissue was done for validation. Vasogenic edema was obtained from the non-enhancing FLAIR hyperintense region of metastasis patients, as they have a clear boundary between enhancing tumor and edema. DCE-MRI data analysis was performed to obtain T perfusion MRI parameters. Support Vector Machine (SVM) classification was performed using T perfusion MRI parameters to differentiate between non-enhancing tumor and vasogenic edema. Receiver-operating-characteristic (ROC) analysis was done on the results of the SVM classifier. For improved classification accuracy, the SVM output was post-processed via neighborhood smoothing. RESULTS:Histology results showed that resected tissue consists largely of tumorous tissue with 7.21 ± 4.05% edema and a small amount of healthy tissue. SVM-based classification provided a misclassification error of 8.4% in differentiation between non-enhancing tumor and vasogenic edema, which was further reduced to 2.4% using neighborhood smoothing. CONCLUSION:The current study proposes a semiautomatic method for segmentation between non-enhancing tumor and vasogenic edema in HGG patients, based on an SVM classifier trained on an alternative ground truth to a radiologist's manual delineation of a tumor. The proposed methodology may prove to be a useful tool for pre- and post-operative evaluation of glioma patients. 10.1016/j.ejrad.2018.07.018