Introduction: Ionizing radiation in medical imaging is one of the dominant sources of exposure, and correct knowledge of radiation protection, affects staff safety behaviors during procedures. This study aimed to assess the radiation protection Knowledge, Attitude and Practice (KAP) amongst nuclear medicine centers' staff in Iran. Methods: To evaluate the level of radiation protection KAP, a validated questionnaire was distributed between 243 participants considering demographic characteristics in different geographical regions in Iran from 2014 to 2015. Results: There were statistically significant differences in the level of nuclear medicine staff KAP radiation protection with gender (p<0.05), practice age KAP level and radiation protection (p<0.05) among nuclear medicine staff with different working regions and healthcare market. There is no significant connection between educational age and KAP level of radiation protection of nuclear medicine department staff (p>0.05). Conclusion: Our findings have shown that radiation protection KAP level of nuclear medicine staff was inadequate in some regions. This might be due to the lack of continuous training and absence of adequate safety knowledge about ionizing radiation. It seems that awareness about radiation protection rules and regulations, along with continuous training and preparations has a direct effect on radiation practice leading to enhanced KAP of staff in nuclear medicine centers.

Background: Gastro-esophageal (GE) junction cancer is the fastest-growing tumor, particularly in the United States (US).Objective: This study aimed to compare dosimetric and radiobiological factors among field-in-field (FIF), three-field (3F), and four-field box (4FB) radiotherapy planning techniques for gastro-esophageal junction cancer. Material and Methods:In this experimental study, thirty patients with GE junction cancer were evaluated, and three planning techniques (field-in-field (FIF), three-field (3F), and four-field box (4FB)) were performed for each patient for a 6-MV photon beam. Dose distribution in the target volume, the monitor units (MUs) required, and the dose delivered to organs at risk (OARs) were compared for these techniques using the paired-sample t-test.Results: A significant difference was measured between the FIF and 3F techniques with respect to conformity index (CI), dose homogeneity index (HI), and tumor control probability (TCP) for the target organ, as well as the D mean for the heart, kidneys, and liver. For the spinal cord, the FIF technique showed a slight reduction in the maximum dose compared to the other two techniques. In addition, the V 20 Gy of the lungs and the normal tissue complication probability (NTCP) of all OARs were reduced with FIF method. Conclusion:The FIF technique showed better performance for treating patients with gastro-esophageal junction tumors, in terms of dose homogeneity in the target, conformity of the radiation field with the target volume, TCP, less dose to healthy organs, and fewer MU.

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Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19 as normal, mild, moderate, and severe based on the images of the lungs CT. Material and methods: In this study, two different deep transfer learning strategies were used. In the first procedure, features were extracted from fifteen pre-trained CNNs architectures and then fed into a support vector machine (SVM) classifier. In the second procedure, the pre-trained CNNs were fine-tuned using the chest CT images, and then features were extracted for the purpose of classification by the softmax layer. Finally, an ensemble method was developed based on majority voting of the deep learning outputs to increase the performance of the recognition on each of the two strategies. A dataset of CT scans was collected and then labeled as normal (314), mild (262), moderate (72), and severe (35) for COVID-19 by the consensus of two highly qualified radiologists. Results: The ensemble of five deep transfer learning outputs named EfficientNetB3, EfficientNetB4, InceptionV3, NasNetMobile, and ResNext50 in the second strategy has better results than the first strategy and also the individual deep transfer learning models in diagnosing the severity of COVID-19 with 85% accuracy. Conclusions: Our proposed study is well suited for quantifying lung involvement of COVID-19 and can help physicians to monitor the progression of the disease.

The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.

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Background: Pancreatic adenocarcinoma is a lethal condition with poor outcomes by various treatment modalities and an increasing incidence. Aim: The aim of this study is to evaluate the advantages of field-in-field (FIF) versus three-field and four-field radiation treatment planning techniques in three-dimensional treatment of patients with pancreatic cancer. Materials and Methods: The evaluations of these planning techniques were performed in terms of physical and biological criteria. Radiotherapy treatment data of 20 patients with pancreatic cancer were selected and evaluated for FIF, three-field, and four-field treatment techniques. The patients were treated by 6 MV photon beam of a medical linac, and these three treatment planning techniques were evaluated for all the 20 patients. The plans were compared based on dose distribution in the target volume, monitor unit (MU), and dose to organs at risk (OARs). Results: The results have shown that, with assuming the same prescribed dose to planned target volume, FIF plans have some advantages over three-field and four-field treatment plans, based on MU values, V20 Gy in the right lung, V20 Gy in the left lung, Dmean in the left kidney, Dmean in the liver, and Dmean in the spinal cord. Based on the obtained results, the use of FIF technique reduces MUs compared to the three-field and four-field techniques. Conclusion: Having a less MU for performing treatment reduces scattered radiation and therefore reduces the risk of secondary cancer in normal tissues. In addition, the use of FIF technique has advantage of less radiation dose to some OARs.