Showing 5 results for Gity
Gity M, Motamedy M, , ,
Volume 60, Issue 4 (15 2002)
Abstract
Background: Sport medicine is a relatively new scientific branch in Iran. In order to evaluate sport injuries in Iranian skiers we examined and followed all ski players who was injured while skiing in Shemshak slope during a skiing season (January to April 2000).
Materials and Methods: During a period of 3 months, a total of 32050 persons skied in Shemshak slope and 76 case of injuries were identified the injury rate was calculated as 2.3/1000 skiers. Among the injured organs knee (32%) and head and neck region (20%) were respectively the most common sites of injury. Sprain of the medial collateral ligament was the most frequent knee injury (28% of the cases). 26.7% of the injured cases were amateurs and 21% of them used hired ski instruments.
Results: In this study such factors as lack of exercise before skiing, fatigue and time of skiing (beginning or end of the season) were not found to be related to the injury rate. However, head and neck injuries in contrast to knee injuries were most frequent in the end of the season (P<0.01).
Conclusion: This study confirms the necessity of greater care of knee joints during skiing and probable need of wearing helmet for head protection in the end of skiing season. More studies are necessary to clarify other details regarding sport injuries in skiers.
Karolin Abashzadeh , Fereydoun Siassi , Mostafa Qorbani , Fariba Koohdani , Negin Farasati , Gity Sotoudeh ,
Volume 74, Issue 12 (March 2017)
Abstract
Background: Nurses are prone to continuous stress due to their job situation that lead to many physical and psychological disorders. this job stress also affects their personal life and career. The aim of this study was to evaluate the association between major dietary patterns and anthropometry in nurses.
Methods: We conducted a cross-sectional study from February to October 2014. In this cross-sectional study, 320 female nurses were selected randomly from eight hospitals affiliated to Tehran University of Medical Sciences in 2014. This research project carried out with the code 24371 Tehran University of Medical Sciences in Research Ethics Committee approved. Anthropometry and blood pressure measurement was done. Data on physical activity were obtained using the short version of international physical activity questionnaire.
|
Results: Three dietary patterns were identified using factor analysis and labeled: healthy, unhealthy and traditional. The healthy dietary pattern score was significantly related to weight and body mass index (BMI) of participants after adjusting for confounders (P=0.05, P=0.01, respectively). There was not significant association between the unhealthy dietary pattern and anthropometry measures. The unhealthy dietary pattern score was inversely related to systolic and diastolic blood pressure after adjusting for confounders (P=0.001, P=0.03, respectively). There was not any significant association between the traditional dietary pattern and anthropometry and blood pressure measures (P>0.05).
Conclusion: According to the result of this study, three dietary patterns including, healthy, unhealthy and traditional were identified in nurses. The healthy dietary pattern was associated with weight and BMI and the unhealthy dietary pattern was inversely associated with blood pressure. The traditional dietary pattern had no effect on anthropometry and blood pressure measures.
|
Masumeh Gity , Ali Borhani , Mehrdad Mokri , Majid Shakiba , Morteza Atri , Nasim Batavani ,
Volume 76, Issue 8 (November 2018)
Abstract
Background: Estrogen-negative breast cancers have different clinical course, prognostic features and treatment response in comparison to estrogen receptor-positive (ER-positive) breast cancers. Human epidermal growth factor receptor 2 (HER2) oncoprotein has found to have a pivotal role in natural cell growth and cell division and is suggested to be directly related to tumor invasiveness in breast cancer patients. The purpose of this study was to retrospectively assess the mammography, ultrasound, and magnetic resonance imaging (MRI) features of estrogen negative breast cancers with and without overexpression of HER2/neu receptor.
Methods: In this cross-sectional retrospective study, mammographic, ultrasound and MRI features as well as HER2 status were assessed in patients with ER-negative breast cancer that were referred to Cancer Institute of Imam Khomeini Hospital Complex in Tehran from October 2015 to October 2017. Clinicopathologic data and mammography, ultrasound, and MRI features were reviewed and were correlated with HER2 status of estrogen-negative tumors.
Results: Of the 172 patients with ER-negative breast cancer, 101 patients were positive for HER2/neu receptor (58.8%). There was a significant correlation between HER2-positivity and tumor type (P=0.004). Among estrogen negative breast cancers, significant association were found between HER2 and tumor histologic grade (P=0.024) and TNM stage (P=0.021). HER2-positive tumors were more likely to present with microcalcification (P=0.007) and have irregular shapes (P=0.034) in mammography than HER2-negative tumors. No association was found between HER-2 status and tumor size, shape, margin, posterior feature, halo or orientation of the tumor in ultrasound. We also found no correlation between HER2 status and MRI features including mass shape or margin, internal enhancement pattern or curve type among estrogen-negative breast cancers.
Conclusion: Findings of this study showed that among estrogen-negative breast cancers, HER2/neu positive tumors are more likely to be diagnosed at higher stage and have higher histologic grade at the time of diagnosis. Tumor mass shape and microcalcification in mammography are found to be associated with HER2 status among patients with estrogen-negative breast cancer.
Masoumeh Gity , Behnaz Moradi, Rasool Arami , Ali Arabkheradmand, Mohamad Ali Kazemi,
Volume 77, Issue 1 (April 2019)
Abstract
Background: Diffusion-weighted imaging (DWI) is one of methods in evaluation of breast lesions. We aimed to investigate the apparent diffusion coefficient (ADC) values in breast tumors and their accuracy in differentiating benign versus malignant lesions.
Methods: In this cross-sectional study, 72 patients with 88 breast lesions were investigated by 1.5-T breast MRI from 2015 to 2017 in Athari Imaging Center in Tehran, Iran. Nearly all patients has undergone histopathology evaluation. One small region of interest (ROI) were placed on the most restricted region inside the solid part on the ADC map. Care was taken to avoid cystic or necrotic, fatty regions and hematoma inside the mass. A large round ROIs were placed in healthy fibroglandular tissue of contralateral breast ADC values were measured and compared in normal breast tissue and in most restricted parts of breast lesions (mass and non-mass). After determining cut-off for differentiation of benign and malignant lesions, sensitivity, specificity, accuracy, positive predictive value and negative predictive value were calculated.
Results: Mean age of patients was 43.3 years. The average tumor size of benign and malignant lesions were calculated 26.0 mm, 35.3 mm respectively and 23 mm and 46 mm in mass and non-mass respectively. Invasive ductal carcinoma include the majority of pathology result (in 37.5% of the patients). Our results revealed that the measured ADC values in normal breast tissue were higher than breast lesions (P≤0.01). Mean ADC value in benign lesions was 1.40×10-3 mm²/s and for malignant lesion was 1.08×10-3 mm²/s. ADC value in the normal breast tissue was 1.79×10-3 mm2/s and was significantly higher than ADC value of breast lesions (benign and malignant). Cut-off value in non-mass was not valid, but in mass was 1.19×10-3 mm²/s with sensitivity, specificity, positive predictive value, negative predictive and accuracy of 89.7%, 83.8%, 87.5%, 86.6%, and 87.1% respectively.
Conclusion: In DWI imaging, ADC value can differentiate benign and malignant masses with high sensitivity and specificity but not helpful in non-mass lesions.
Ali Ameri, Mahmoud Shiri, Masoumeh Gity , Mohammad Ali Akhaee,
Volume 79, Issue 5 (August 2021)
Abstract
Breast cancer is one of the most common types of cancer in women. Screening mammography is a low‑dose X‑ray examination of breasts, which is conducted to detect breast cancer at early stages when the cancerous tumor is too small to be felt as a lump. Screening mammography is conducted for women with no symptoms of breast cancer, for early detection of cancer when the cancer is most treatable and consequently greatly reduce the death rate from the breast cancer. Screening mammography should be performed every year for women age 45-54, and every two years for women age 55 and older who are in good health. A mammogram is read by a radiologist to diagnose cancer.
To assist radiologists in reading mammograms, computer-aided detection (CAD) systems have been developed which can identify suspicious lesions on mammograms. CADs can improve the accuracy and confidence level of radiologists in decision making and have been approved by FDA for clinical use. Traditional CAD systems work based on conventional machine learning (ML) and image processing algorithms. With recent advances in software and hardware resources, a great breakthrough in deep learning (DL) algorithms was followed, which revolutionized various engineering areas including medical technologies. Recently, DL models have been applied in CAD systems in mammograms and achieved outstanding performance. In contrast to conventional ML, DL algorithms eliminate the need for the tedious task of human-designed feature engineering, as they are capable of learning useful features automatically from the raw data (mammogram). One of the most common DL frameworks is the convolutional neural network (CNN). To localize lesions in a mammogram, a CNN should be applied in region‑based algorithms such as R‑CNN, Fast R‑CNN, Faster R‑CNN, and YOLO.
| Proper training of a DL‑based CAD requires a large amount of annotated mammogram data, where cancerous lesions have been marked by an experienced radiologist. This highlights the importance of establishing a large, annotated mammogram dataset for the development of a reliable CAD system. This article provides a brief review of the state‑of‑the‑art techniques for DL‑based CAD in mammography. |
