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Radical resection and diagnostic methods in gliomas:  review article
Ahmadii R, Esmaeilzadeh M, Unterberg A,
Volume 67, Issue 4 (7-2009)
Abstract

Gliomas include a group of primary central nervous system (CNS) neoplasms with characteristics of neuroglial cells (eg, astrocytes, oligodendrocytes). The gliomas are classified commonly to WHO grade I-IV gliomas. The grading is based on the presence of nuclear atypia, vascular proliferation, mitoses, and necrosis. The malignant gliomas are progressive brain tumors that are divided into anaplastic gliomas and glioblastoma based upon their histopathologic features. Today, different modalities such as surgery, radiation therapy (in the form of external beam radiation or the stereotactic approach using radiosurgery) and chemotherapy have been used for the treatment of gliom's tomors but unfortunately the prognosis and survival rate is poor in most of patients. The survival depends on the tumor's type, size, location and the patient's age. We reviewed the prognostic factors, diagnostic modalities and surgical management of patients with gliomas.


Automatic segmentation of glioma tumors from BraTS 2018 challenge dataset using a 2D U-Net network
Zahra Papi , Iraj Abedi, Fatemeh Dalvand, Alireza Amouheidari,
Volume 80, Issue 4 (7-2022)
Abstract
Background: Glioma is the most common primary brain tumor, and early detection of tumors is important in the treatment planning for the patient. The precise segmentation of the tumor and intratumoral areas on the MRI by a radiologist is the first step in the diagnosis, which, in addition to the consuming time, can also receive different diagnoses from different physicians. The aim of this study was to provide an automated method for segmenting the tumor and intratumoral areas.
Methods: This is a fundamental-applied study that was conducted from May 2020 to September 2021 using multimodal MRI images of 285 patients with glioma tumors from the BraTS 2018 Database. This database was collected from 19 different MRI imaging centers, including multimodal MRI images of 210 HGG patients, and 75 LGG patients. In this study, a 2D U-Net architecture was designed with a patch-based method for training, which comprises an encoding path for feature extraction and a symmetrical decoding path. The training of this network was performed in three separate stages, using data from high-grade gliomas (HGG), and low-grade gliomas (LGG), and combining two groups of 210, 75, and 220 patients, respectively.
Results: The proposed model estimated the Dice Similarity Coefficient (DSC) results in HGG datasets 0.85, 0.85, 0.77, LGG datasets 0.80, 0.66, 0.51, and the combination of the two groups 0.88, 0.79, 0.77 for regions the whole tumor, tumor core, and enhancing region in the training dataset, respectively. The results related to Hussdorf Distance (HD) for HGG datasets were 8.24, 9.92, 4.43, LGG datasets 11.5, 11.31, 2.23, and the combination of the two groups 7.20, 8.82, 4.43 for regions the whole tumor, tumor core, and enhancing region in the training dataset, respectively.
Conclusion: Using the U-Net network can help physicians in the accurate segmentation of the tumor and its various areas, as well as increase the survival rate of these patients and improve their quality of life through accurate diagnosis and early treatment.

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