Eosinophilic esophagitis should be considered in the differential diagnosis of any cases with refractory reflux who complain of chronic unexplained dysphagia, with history of recurrent food impaction, and atopy or abnormal endoscopic features.

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AIM:To determine the outcome of esophageal cancer patients referred for palliative care, in Gorgan and Gonbad gastrointestinal clinics, northeast of Iran. METHODS: This cross-sectional study was done on inoperable esophageal cancer cases referred to gastrointestinal clinics in Gorgan and Gonbad city (2005)(2006). Demographic data were collected during the procedure and cases were followed up every one month. Improvement proportion was calculated with 95% confidence interval, to determine the rate of improvement. Survival analysis and KaplanMeier methods were used to estimate the duration of palliative care effectiveness. RESULTS: We recruited 39 cases into the study. Squamous cell carcinoma was the most prevalent (92.3%). The middle third of the esophagus was involved predominantly (51.3%). Dilation was the most preferred method (89.7%) and stenting was done in 4 cases. Decreasing dysphagia score was not related to palliation method or pathology type of carcinoma. Age of the patients was significantly related to the improvement of dysphagia score. Mean survival time was 137.6 d and median was 103 d. CONCLUSION: Results of this study showed a low survival rate after palliative care in esophageal cancer cases despite dysphagia scores' improvement after dilating or stenting.

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Introduction: Northeastern Iran is known as a high risk area of upper gastrointestinal cancers. Recent reports have suggested a declining trend for these cancers as well as an increase in the incidence of other malignancies including breast cancer. Our present aim was to describe the epidemiological pattern of breast cancer in this region during 2004-2009. Methods: All new cancer cases from public and private diagnostic and therapeutic centers of Golestan province were registered. A structured questionnaire was prepared and used based on the oncology was considered for coding. Age standardized incidence rates (ASR) of breast cancer were calculated. Results: A total of 11,038 new cancer cases were registered during 2004-2009, of which, 1,101 (10%) were females with breast cancer. The median age of the breast cancer patients was 46 years. The ASR for breast cancer was 28 per 100,000 person-years. We found an unusual rapid increase in breast cancer rate at the age of 25 years. The value <0.01). Conclusion: Our study showed high rate of breast cancer in Golestan province of Iran. We found an unusual peak of breast cancer in young women. So, the age of starting screening programs may need to be studies are warranted to clarify the role of important determinants, especially regarding the ethnic disparity, on breast cancer in this region.

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Objective: In this large multi-institutional study, we aimed to analyze the prognostic power of computed tomography (CT)-based radiomics models in COVID-19 patients. Methods: CT images of 14,339 COVID-19 patients with overall survival outcome were collected from 19 medical centers. Whole lung segmentations were performed automatically using a previously validated deep learning-based model, and regions of interest were further evaluated and modified by a human observer. All images were resampled to an isotropic voxel size, intensities were discretized into 64-binning size, and 105 radiomics features, including shape, intensity, and texture features were extracted from the lung mask. Radiomics features were normalized using Z-score normalization. High-correlated features using Pearson (R2>0.99) were eliminated. We applied the Synthetic Minority Oversampling Technique (SMOT) algorithm in only the training set for different models to overcome unbalance classes. We used 4 feature selection algorithms, namely Analysis of Variance (ANOVA), Kruskal-Wallis (KW), Recursive Feature Elimination (RFE), and Relief. For the classification task, we used seven classifiers, including Logistic Regression (LR), Least Absolute Shrinkage and Selection Operator (LASSO), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost (AB), Naive Bayes (NB), and Multilayer Perceptron (MLP). The models were built and evaluated using training and testing sets, respectively. Specifically, we evaluated the models using 10 different splitting and cross-validation strategies, including different types of test datasets (e.g. non-harmonized vs. ComBat-harmonized datasets). The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were reported for models evaluation. Results: In the test dataset (4301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83(sd:0.01) (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + RF classifier. In RT-PCR-only positive test sets (3644), similar results were achieved, and there was no statistically significant difference. In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in highest performance of AUC, reaching 0.83 (sd:0.01) (CI95%: 0.81-0.85), with sensitivity and specificity of 0.77 and 0.74, respectively. At the same time, ComBat harmonization did not depict statistically significant improvement relevant to non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and LR classifier resulted in the highest performance of AUC (0.80 (sd:0.084)) with sensitivity and specificity of 0.77 (sd:0.11) and 0.76 (sd: 0.075), respectively. Conclusion: Lung CT radiomics features can be used towards robust prognostic modeling of COVID-19 in large heterogeneous datasets gathered from multiple centers. As such, CT radiomics-based model has significant potential for use in prospective clinical settings towards improved management of COVID-19 patients.