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Showing 2 results for Amouheidari
Dosimetric comparison of treatment of left breast cancer using two different dose schedules Dosimetric comparison of treatment of left breast cancer using two different dose schedules
Arezoo Kazemzadeh, Iraj Abedi, Alireza Amouheidari, Atefeh Shirvany,
Volume 78, Issue 9 (December 2020)
Abstract
Background: To date, different kinds of treatment methods have been proposed for radiotherapy of cancer patients. Choosing the kind of treatment method affects the quality of the patient's treatment. This study aims to investigate the effect of the number of radiation treatment sessions on the dose received by the patient and the distribution of tumor dose and dose received by organs at risk in breast cancer radiation therapy. These results help us to select the appropriate treatment schedules for the treatment of left breast patients.
Methods: This prospective cross-sectional study was performed on the treatment plans of 35 patients with left breast cancer who referred to Isfahan Milad Hospital between July 2019 and April 2020. They were candidates for left breast radiation therapy. Also, these patients had no history of surgery or chemotherapy, and no supraclavicular or axillary lymph nodes were involved. Patients were treated with a conventional fraction regimen (CF) or hypofractionated (HF) treatment schedule. Different dosimetry parameters for the target and organ at risks such as conformity index, homogeneity index and mean dose were obtained from the dose-volume histogram plot. Finally, the results of both plans were compared with each other.
Results: The data obtained from this study indicate a decrease in the average dose of all organs in the hypo fractionated regimens compared to conventional plans. The differences between two plans were statistically significant for tumor, lung, and skin (P=0.0). Moreover, the maximum dose for the skin was also reduced when hypofractionated regimens were used. However, the values of the homogeneity index and conformity index of tumor in the two methods did not show a significant difference (P were 0.99 and 0.86, respectively).
Conclusion: In general, the results of the current study indicate that the hypofractionated regimen leads to a reduction in dosimetric factors compared to conventional fraction plans. It seems that this method can be used as an alternative treatment plan for breast cancer radiation therapy due to the reduced duration of the treatment period.
 
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 (July 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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