Publications by authors named "Elnaz Tabibian"

This study aimed to develop a diagnostic algorithm for preoperative differentiating uterine sarcoma from leiomyoma through a supervised machine-learning method using multi-parametric MRI. A total of 65 participants with 105 myometrial tumors were included: 84 benign and 21 malignant lesions (belonged to 51 and 14 patients, respectively; based on their postoperative tissue diagnosis). Multi-parametric MRI including T1-, T2-, and diffusion-weighted (DW) sequences with ADC-map, contrast-enhanced images, as well as MR spectroscopy (MRS), was performed for each lesion. Thirteen singular MRI features were extracted from the mentioned sequences. Various combination sets of selective features were fed into a machine classifier (coarse decision-tree) to predict malignant or benign tumors. The accuracy metrics of either singular or combinational models were assessed. Eventually, two diagnostic algorithms, a simple decision-tree and a complex one were proposed using the most accurate models. Our final simple decision-tree obtained accuracy = 96.2%, sensitivity = 100% and specificity = 95%; while the complex tree yielded accuracy, sensitivity and specificity of 100%. To summarise, the complex diagnostic algorithm, compared to the simple one, can differentiate tumors with equal sensitivity, but a higher specificity and accuracy. However, it needs some further time-consuming modalities and difficult imaging calculations. Trading-off costs and benefits in appropriate situations must be determinative.

Objective: The present study aimed to compare the qualitative (time intensity curve analysis), the semi-quantitative and the quantitative multiphase 3T dynamic contrast-enhanced (DCE) MRI parameters as predictors of malignancy in adnexal masses. Materials and Methods: In this prospective study, women with an adnexal mass who were scheduled for surgical resection or were followed for more than one year period to confirm the benignity of their lesions, underwent multiphase 3T DCE-MRI. The qualitative (time intensity curve), semi-quantitative (SImax, SIrel, WIR) and quantitative (Ktrans, Kep, Vb) analyses were performed on DCE-MRI sequences and their predictive values were compared. Results: A total of 17 benign and 14 malignant lesions were included. According to the qualitative analysis, none of the lesions with Type I time intensity curves (TIC) were malignant and none of the masses with Type III TICs were benign. The accuracy of the quantitative parameters in detection of malignancy was found to be higher than that of semi-quantitative variables, particularly when calculated for a small ROI within the high signal area of the mass (sROI) rather than the largest ROI including the whole mass (lROI), and when inter-MRI variations were omitted using ratios. The Kep(tumor)/Kep(myometrium) ratio measured from sROI was the best parameter for differentiating a malignant lesion with a sensitivity of 100% and a specificity of 92.3%. Conclusion: We concluded that a Type I TIC confirms a benign lesion, and a type III TIC confirms the malignancy and further evaluation is not recommended for these lesions. So complementary quantitative analysis is only recommended for adnexal masses with type II TICs.

Background: Post-hysterectomy histopathological examination is currently the main diagnostic tool for differentiating uterine sarcomas from leiomyomas. This study aimed to investigate the diagnostic accuracy of preoperative quantitative metrics based on T2-weighted sequences and contrast-enhanced MRI (CE-MRI) for distinguishing uterine sarcomas from leiomyomas.

Materials And Methods: The institutional review board approved the study. Sixty-five women confirmed to have a total of 105 lesions participated. Routine pelvic MRI sequences, T2 map and CE-MRI images were performed preoperatively using a 3 T MR scanner. Six quantitative metrics-T2 mapping parameter, T2 scaled ratio, tumor myometrium contrast ratio on T2, tumor psoas contrast ratio on T2, tumor myometrium contrast-enhanced ratio, and tumor psoas contrast-enhanced ratio-were extracted from the acquired image sets. Chi-square test was used to compare the percentage of malignant lesions with the central necrosis to the corresponding percentage for the benign masses. Using the area under receiver operating characteristic (AUC) curve, the performance of different metrics for distinguishing uterine sarcomas from leiomyomas was measured. Moreover, for each metric, we extracted the optimal cut-off value. The values of sensitivity, specificity, negative predictive value, and positive predictive value were calculted for the classifiers based on different metrics.

Results: The average age, average lesion size, and proportion of premenopausal women in benign and malignant groups were comparable in our dataset. The signal intensity of uterine sarcomas at T2-weighted sequences was significantly higher than that of leiomyomas (p < 0.001), while intensity at T1-weighted sequences exhibited no significant difference between the two masses (p = 0.201). Our data also suggested that a central necrosis was ten times more common among malignant lesions compared to benign ones (p < 0.001). Among different metrics, T2 mapping parameter achieved the highest AUC value and accuracy in differentiating two groups. Three measures-T2 scaled ratio, tumor myometrium contrast ratio on T2, and tumor myometrium contrast-enhanced ratio-achieved a sensitivity of 100%, therefore none of the malignant lesions would have been missed if these metrics had been adopted in patient management.

Conclusions: The findings suggested that the evaluated metrics could be useful in the preoperative assessment of myometrial masses to differentiate uterine sarcomas from leiomyomas. The proposed framework has major implications for improving current practice in the management of myometrial masses.

Purpose: To propose a computer-assisted method for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted magnetic resonance imaging (PWI).

Materials And Methods: Forty-two women confirmed to have a total of 60 masses (10 uterine sarcomas and 50 benign leiomyomas) were included. The reference diagnosis was based on postoperative histopathological examination. All women underwent the standard MRI protocol with 3-Tesla MR imager (Magnetom Trio, Siemens, Erlangen, Germany) for assessment of myometrial masses, followed by PWI. For each mass, two regions of interest (ROI) were outlined manually by an experienced radiologist; one (ROI) represented the entire tumor while the other (ROIs) was placed on the area of the lesion with the most marked contrast enhancement. Two additional ROI with diameters similar to ROI (3.0 to 3.1 mm) were placed on psoas muscle (ROI) and myometrium (ROI) in order to provide baselines for comparisons. The obtained ROIs of PWI images were then analyzed using the DCE Tool plug-in (version 2.0SP1) within ClearCanvas (Toronto, Ontario, Canada) framework. The DCE Tool provides seven parameters (K, k, V, IAUC, initial slope, peak, the mean squared error) for modelling contrast uptake within an ROI using the modified Tofts model. Parameters extracted from the ROIs were fed into a decision tree ensemble, which classified the corresponding lesions either as malignant or benign. The leave-one-out cross validation (LOOCV) was utilized to evaluate the performance of the classifier.

Results: None of the parameters extracted from ROI or ROI differed significantly between uterine sarcoma and benign leiomyomas (all p > 0.05). The overall accuracy of 66.7% was obtained by feeding seven parameters extracted from ROI to the classifier. When 21 features extracted from ROI, ROI, and ROI were fed into the classifier an accuracy of 91.7%, sensitivity of 100%, and specificity of 90% were achieved in the optimal operating point of classifier.

Conclusion: Although none of the PWI parameters differed significantly between benign and malignant lesions, when the information provided by the extracted features was aggregated using a machine learning method, a promising discriminative power was obtained. This suggests that the proposed model for combining the PWI parameters is potentially useful for differentiating uterine sarcoma from leiomyomas.

Purpose: This study aimed to review and pool the current literature on intra-articular ozone injection in knee osteoarthritis (OA) patients.

Methods: A systematic review of three big databases was performed to identify all English-language randomized clinical trials (RCTs) that evaluated the efficacy of intra-articular ozone injection vs a control injection for knee OA sufferers, using the following two measuring tools: pain VAS and Western Ontario and McMaster Universities Arthritis Index (WOMAC).

Results: A total of 428 patients in five RCTs were included, from which 53% (n=225) were in the ozone group and 47% in the control (hyaluronic acid [HA], dextrose, and air injection) group (n=203). The mean age of the patients in both groups was 64 years. Females were the majority. All studies had at least 2 months of follow-up (F/U). Mean difference (MD) between the groups for VAS in the first month was -0.23 with a -value of 0.71 (negative value was in favor of ozone), whereas this difference in the third and sixth months reached 1.04 and 1.31, respectively, favoring the control group. These data demonstrated that control injection had a more prolonged pain relief period. A similar trend was seen regarding WOMAC scores; pooled results showed that ozone was slightly better than the control injections during the first month (MD =-7.84 [=0.15]), but it declined to MD=2.55 and 8.23 at 2- to 3- and 4- to 6-month F/U, respectively, again in favor of control injections. Also, adverse events occurred homogeneously in both ozone (6/150 cases, 4%) and control groups (7/129 cases, 5.4%; -value=0.31).

Conclusion: Based on the current meta-analysis, intra-articular ozone injection efficacy was significantly superior to placebo and slightly lower to other control injections with non-significant difference. Therefore, ozone could be recommended as an efficient non-surgical treatment, durable for at least 3-6 months, in mild or moderate knee OA management.

BACKGROUND There are few reports of breast cancer cases with uterine metastases; among them, myometrium is more frequently involved than endometrium. The majority of breast cancer metastases to endometrium are lobular type, and there have been only 5 reported cases of ductal type since 1984. Here, we describe a new case of invasive ductal carcinoma with metastases to endometrium and isolated presentation of abnormal uterine bleeding, in addition to reviewing the existing literature on other similar cases. CASE REPORT The patient was a 51-year-old Persian woman with no remarkable past medical or family history of cancer, who presented with a 6-month complaint of menorrhagia to our gynecology clinic. Diagnostic studies including trans-vaginal ultrasonography, pathological examination of endometrial curettage specimen, immunohistochemistry findings, and X-plane and magnetic resonance mammography, and breast core-needle biopsy revealed invasive ductal breast carcinoma as the origin of the endometrial metastasis. CONCLUSIONS Abnormal uterine bleeding in a premenopausal patient should alert clinicians to the possibility of secondary as well as primary neoplasms. It is necessary to differentiate a metastatic tumor from a primary one, since the treatment and prognosis are completely different.

Pneumonia is considered as the main cause of abdominal pain in children whereas it presents mostly by respiratory symptoms in adults. Here we present a 71-year-old Iranian female who complained specifically of abdominal pain on admission to our emergency department. We had found nothing as an etiology in our first evaluations. After several hours chest pain was added to the symptoms and the following chest, X-Ray showed a consolidation in the right lung base. She was treated by pneumonia antibiotic regimen and discharged after seven days in a good clinical condition. In conclusion, pneumonia should be considered as a differential diagnosis of abdominal pain in adults as well as in children.

Background & Objectives: The role of human leukocyte antigen (HLA) in clinical response to immunotherapy is not completely known. In this study we evaluated the relationship between HLA-DRB1 genotype, which has been proved to be more common in Iranian MS patients, and clinical response to interferon-beta (IFNβ), which is the most common immunotherapy for relapsing-remitting MS.

Design And Setting: In this study 68 Iranian patients with confirmed diagnosis of RRMS who had been referred to and admitted in Neurology Department of Amiralam and Khatam Hospitals in Tehran were selected. Patients were followed prospectively for 2 years since initiation of therapy and clinical data, including EDSS scores were recorded every 3 months. MRI was performed at the time of diagnosis and each year.

Methods: HLA-DRB1 typing was performed by polymerase chain reaction (PCR) for all patients and data was analyzed by STATA 12th edition.

Results: There were 47 (69.1%) responders and 21 (30.9%) non-responders. These two groups were demographically and clinically comparable. Fisher's exact test did not show any difference between HLA-DRB1 allele frequencies in responders and non-responders.

Conclusions: Our findings confirmed the lack of association between HLA-DRB1 and clinical response to IFNβ among MS patients as previous studies had done.