Dynamic Contrast Magnetic Resonance Imaging (DCE-MRI) and Diffusion Weighted MR Imaging (DWI) for Differentiation between Benign and Malignant Salivary Gland Tumors

Background: Salivary gland tumors form nearly 3% of head and neck tumors. Due to their large histological variety and vicinity to facial nerves, pre-operative diagnosis and differentiation of benign and malignant parotid tumors are a major challenge for radiologists. Objective: The majority of these tumors are benign; however, sometimes they tend to transform into a malignant form. Functional MRI techniques, namely dynamic contrast enhanced (DCE-) MRI and diffusion-weighted MRI (DWI) can indicate the characteristics of tumor tissue. Methods: DCE-MRI analysis is based on the parameters of time intensity curve (TIC) before and after contrast agent injection. This method has the potential to identify the angiogenesis of tumors. DWI analysis is performed according to diffusion of water molecules in a tissue for determination of the cellularity of tumors. Conclusion: According to the literature, these methods cannot be used individually to differentiate benign from malignant salivary gland tumors. An effective approach could be to combine the aforementioned methods to increase the accuracy of discrimination between different tumor types. The main objective of this study is to explore the application of DCE-MRI and DWI for assessment of salivary gland tumor types

Assessment of the prevalence of diabetic gastroparesis and validation of gastric emptying scintigraphy for diagnosis [Diyabetik Gastroparezi Prevalansı ve Tanısında Mide Boşalma Sintigrafisinin Geçerliliğinin Araştırılması]

Objective: Gastroparesis is defined as delayed gastric emptying and is a common medical condition in diabetic patients. Scintigraphy is commonly used as a standard diagnostic procedure for the quantitative assessment of gastroparesis. The aims of this study were to determine an optimum imaging time for the diagnosis of gastroparesis, to assess the prevalence of gastroparesis, to evaluate the correlation between endoscopy and scintigraphy findings as well as the correlation between gastric emptying with patient genders, blood glucose concentration, and functional dyspepsia. Methods: Gastric emptying was assessed in 50 diabetic patients with a mean age of 50.16 years. For evaluation of gastric emptying, a test meal containing 2 pieces of toast, 120 cc non-labeled water and fried egg labeled with 1 mCi of99mTc was given to each patient. The scintigraphy was performed immediately after ingestion and was repeated at 1, 1.5, 2 and 4 hours after ingestion. In some patients, an additional 90-minute dynamic scan was also acquired. Results: The prevalence of gastroparesis in this study population was determined as 64. Also, the results of this study revealed that a 4-hour scan after ingestion is more relevant than a 90-minute dynamic scan for the evaluation of delayed gastric emptying. There was no statistically significant difference between 1-hour and 2-hour scans, 1-hour and 90-minute scans, 2-hour and 90-minute scans, 2-hour and 4-hour scans. Likewise there was no significant correlation between blood glucose levels, gender and calculated values of gastric emptying time in all groups. Conclusion: According to our findings, it can be suggested that the prevalence of gastroparesis is higher than that mentioned in some previous studies. Also, this study indicates that a gastric emptying scintigraphy at 2 and 4 hours after meal ingestion might provide the anticipated clinical information in diabetic patients with dyspepsia without other evident reasons. © 2017 by Turkish Society of Nuclear Medicine

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

Objective: To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. Methods: Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients� history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. Results: For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95CI: 0.95�0.96), accuracy = 0.88 ± 0.046 (95 CI: 0.88�0.89), sensitivity = 0.88 ± 0.066 (95 CI = 0.87�0.9) and specificity = 0.89 ± 0.07 (95 CI = 0.87�0.9)). Conclusion: Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients. © 2021 The Author(s