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Showing 3 results for Magnetic Resonance Imaging.
Prevalence of empty sella syndrome
Mohadese Zademir, Narjes Sargolzaie, Amirhossein Nourolah ,
Volume 78, Issue 4 (7-2020)
Abstract
Background: The empty sella syndrome (ESS) is a neurological or pathologic finding in which sella turcica is devoid of pituitary tissue and the subarachnoid space extends into sella turcica, which is either primary or secondary as well as partial and complete. The widespread use of CT scans and MRIs today has made the ESS a common finding in imaging. The aim of this study was to evaluate the prevalence of the empty sella syndrome.
Methods: This is a retrospective descriptive-analytic study in which all patients referred to Imam Ali Hospital (Zahedan) for electromagnetic brain imaging (n=1856) were recruited by cross-sectional sampling during the first 6 months from 21 March 2018 to 23 September 2018. Inclusion criteria included the absence of another known problem in the central nervous system and the absence of concurrent underlying disease. The data gathering tool was a questionnaire consisting of demographic and related variable to empty sella disorder.
Results: The results of this study showed that the prevalence of empty sella was 8.2% with a mean age of 37.02±12.51 years. 66.4% of the patients were female. The prevalence of primary empty sella was 78.9% with a mean age of 34.51±11.26 years. 71.7% of the patients had partial empty sella. There was a significant difference between the mean age and sex of patients with empty sella and non-empty sella subjects (P=0.008) and (P<0.0001). There was a statistically significant difference between the mean age of affected patients with type of empty sella (P<0.0001). There was no statistically significant difference between mean age of patients with empty sella and severity of empty sella (P=0.056). There was no significant difference between the frequency of empty sella type and the severity with gender (P=0.224) and (P=0.091).
Conclusion: The findings of this study indicated that the overall prevalence of empty sella in the referring patients was relatively low. Most of them were females with primary type and minor severity.

Evaluation of the accuracy of dynamic contrast enhanced MRI in the diagnosis of invasive prostate neoplasm using pathological findings
Negar Abdi, Iraj Abedi, Mozafar Naserpour , Masoud Rabbani,
Volume 79, Issue 6 (9-2021)
Abstract
Background: Prostate cancer is the most common malignancy in men and the second leading cause of death in all countries of the world. The exact mechanism of prostate cancer is not known. On the other hand, early detection of prostate cancer can lead to a complete cure. Several clinical experiments including Digital Rectum Examination (DRE), biochemistry such as Prostate Specific Antigen (PSA), and pathology such as Trans Rectal Ultra Sonography (TRUS) are used to assess the size and spread of prostate cancer. In this study, the relationship between mean serum PSA and Gleason score as a standard method in patients with prostate cancer was compared using the parameters extracted from DCE MRI.
Methods: This applied-fundamental study was performed on 90 patients with prostate cancer, according to McDonald's criteria who were referred to Shafa Imaging Center in Isfahan, from March 2020 to October 2020. Quantitative analysis is based on modeling the change of concentration of the contrast agent using pharmacokinetic modeling techniques. The pathologist then determined the Gleason score using anatomical landmarks (such as prostate urethra) in the same areas suspected of being cancerous. Existing commercial software captures DCE-MRI data and creates parametric maps such as Ktrans and Kep maps that can be used for diagnostic purposes.
Results: Kep and Ktrans maps showed a significant difference between healthy and cancerous tissue. Kep and Ktrans in prostate cancer were significantly higher than in healthy tissue (P<0.05). Pearson correlation coefficient was used to investigate the relationship between DCE-MRI parameters and histopathological findings. No significant relationship was observed between Gleason score and DCE MRI parameters.
Conclusion: DCE MRI parameters significantly improve the accurate diagnosis of prostate cancer and are useful and effective for diagnosis, management, and evaluation of men with prostate cancer, but should not be considered as a substitute for tissue biopsy.
 

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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