Reperfusion Injury to Skeletal Muscle on Bone Scan

We report the bone scan appearance of reperfusion injury involving the muscle groups of the lower extremities in a patient after undergoing thromboembolectomy of the bilaterally femoral artery. The patient was referred to our department with a history of colon adenocarcinoma and widespread bone pain. Bone scan demonstrated extraosseous accumulation of technetium-99m methylene diphosphonate in the muscle groups of the lower extremities, predominantly in the right extremity. Pathologic examination of the gastrocnemius muscle was suggestive of reperfusion injury and atrophic changes. There was no finding of rhabdomyolysis

Detection of breast cancer via deep convolution neural networks using MRI images

Ikizceli, Turkan/0000-0002-5683-0391; Erbay, Hasan/0000-0002-7555-541X; YURTTAKAL, Ahmet Hasim/0000-0001-5170-6466WOS:000538675900065Breast cancer is the type of cancer that develops from cells in the breast tissue. It is the leading cancer in women. Early detection of the breast cancer tumor is crucial in the treatment process. Mammography is a valuable tool for identifying breast cancer in the early phase before physical symptoms develop. To reduce false-negative diagnosis in mammography, a biopsy is recommended for lesions with greater than a 2% chance of having suspected malignant tumors and, among them, less than 30 percent are found to have malignancy. To decrease unnecessary biopsies, recently, Magnetic Resonance Imaging (MRI) has also been used to diagnose breast cancer. MRI is the highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. However, it requires an experienced radiologist and time-consuming process. On the other hand, convolutional neural networks (CNNs) have demonstrated better performance in image classification compared to feature-based methods and show promising performance in medical imaging. Herein, CNN was employed to characterize lesions as malignant or benign tumors using MRI images. Using only pixel information, a multi-layer CNN architecture with online data augmentation was designed. Later, the CNN architecture was trained and tested. The accuracy of the network is 98.33% and the error rate 0.0167. The sensitivity of the network is 1.0 whereas specificity is 0.9688. The precision is 0.9655

Metabolic Imaging Based Sub-Classification of Lung Cancer

Lung cancer is one of the deadliest cancer types whose 84% is non-small cell lung cancer (NSCLC). In this study, deep learning-based classification methods were investigated comprehensively to differentiate two subtypes of NSCLC, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). The study used 1457 F-18-FDG PET images/slices with tumor from 94 patients (88 men), 38 of which were ADC and the rest were SqCC. Three experiments were carried out to examine the contribution of peritumoral areas in PET images on subtype classification of tumors. We assessed multilayer perceptron (MLP) and three convolutional neural network (CNN) models such as SqueezeNet, VGG16 and VGG19 using three kinds of images in these experiments: 1) Whole slices without cropping or segmentation, 2) cropped image portions (square subimages) that include the tumor and 3) segmented image portions corresponding to tumors using random walk method. Several optimizers and regularization methods were used to optimize each model for the diagnostic classification. The classification models were trained and evaluated by performing stratified 10-fold cross validation, and F-score and area-under-curve (AUC) metrics were used to quantify the performance. According to our results, it is possible to say that inclusion of peritumoral regions/tissues both contributes to the success of models and makes segmentation effort unnecessary. To the best of our knowledge, deep learning-based models have not been applied to the subtype classification of NSCLC in PET imaging, therefore, this study is a significant cornerstone providing thorough comparisons and evaluations of several deep learning models on metabolic imaging for lung cancer. Even simpler deep learning models are found promising in this domain, indicating that any improvement in deep learning models in machine learning community can be reflected well in this domain as well

A COMPARATIVE STUDY ON SEGMENTATION AND CLASSIFICATION IN BREAST MRI IMAGING

Cinarer, Gokalp/0000-0003-0818-6746; Erbay, Hasan/0000-0002-7555-541XWOS: 000455271800005Background: Breast cancer is the type of cancer that develops from cells in the breast tissue. The breast cancer is leading cancer in women. One in every eight to nine women has breast cancer at some point during their lifetime. Computer-Aided Diagnosis (CAD) Technology is getting more important to assist radiologists not only to detect breast cancer tumor but also to interpret lesioned regions. The CAD, as a second reader in the clinic, improves the classification of malignant and benign lesions. On the other hand, Magnetic Resonance Imaging (MRI) is a highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. In MRI image analysis, the segmentation images are important objective because accurate measurement of the delineation of the regions of interest (ROI) is critical for the breast cancer diagnosis and treatment. Herein, by using MRI scans, we propose a semi-automated CAD system prototype to assist radiologists in detecting breast cancer tumors and interpreting lesioned regions. The prototype, first, pre-processes the raw selected suspicious region to reduce the noises and to reveal the structure. Later, using Expectation Maximization (EM), the prototype segments the pre-processed region. After that, we use the Discrete Wavelet Transform (DWT) for providing efficient multi-resolution sub and decomposition of signals. Then Random Forest Algorithm is used for feature selection. Finally, Naive Bayes, Linear Discriminant Analysis and C4.5 Decision Tree Algorithms are used to classify the features of the ROI in the diagnosis analysis. We tested the prototype CAD on 105 patients, among them, 53 are benign and 52 malign. 80% of the images are allocated for training and 20% of images reserved for testing. The CAD classified 20 patients correctly in case of 5 fold cross-validation. Only one patient is misclassified. The computer-aided diagnosis system with the C4.5 has accuracy 95.24%. Furthermore, C4.5 classifies the breast cancer tumors better than Naive Bayes and Linear Discriminant Analysis. We tested the prototype CAD on 105 patients, among them, 53 are benign and 52 malign. The computer-aided diagnosis system with the C4.5 has accuracy 95.24%. Furthermore, C4.5 classifies the breast cancer tumors better than Naive Bayes and Linear Discriminant Analysis

Hepatobiliary function assessed by Tc-99m-mebrofenin cholescintigraphy in the evaluation of fibrosis in chronic hepatitis: histopathological correlation

Purpose Although liver biopsy remains the gold standard in the staging of liver fibrosis in chronic hepatitis C virus (HCV) infection, several noninvasive methods are under evaluation for clinical use. The aim of this study was to evaluate the utility of hepatobiliary function through technetium-99m-N-(-3-bromo-2,4,6-trimethylacetanilide) iminodiacetic acid (Tc-99m-mebrofenin) scintigraphy in evaluating liver fibrosis in patients with chronic HCV infection

Hepatobiliary function assessed by Tc-99m-mebrofenin cholescintigraphy in the evaluation of fibrosis in chronic hepatitis: histopathological correlation

Purpose Although liver biopsy remains the gold standard in the staging of liver fibrosis in chronic hepatitis C virus (HCV) infection, several noninvasive methods are under evaluation for clinical use. The aim of this study was to evaluate the utility of hepatobiliary function through technetium-99m-N-(-3-bromo-2,4,6-trimethylacetanilide) iminodiacetic acid (Tc-99m-mebrofenin) scintigraphy in evaluating liver fibrosis in patients with chronic HCV infection

Evaluation of the diagnostic and prognostic use of gated myocardial perfusion single-photon emission computed tomography in patients with acute chest pain: comparison with the SYNTAX score

ObjectiveThe aim of this study was to evaluate the diagnostic and prognostic efficacy of gated single-photon emission computed tomography (GSPECT) in patients with acute chest pain and to compare quantitative GSPECT parameters and the coronary angiographic SYNTAX score.Materials and methodsA total of 168 patients who presented with clinical symptoms of acute chest pain were enrolled in the study. Study participants were divided into two groups according to the risk of acute coronary syndrome (ACS): low-intermediate and high risk. All participants underwent rest or stress-GSPECT (R/S-GSPECT). Coronary angiography was performed in all high-risk patients and the SYNTAX score was determined. All patients were followed for 243 months and monitored for the occurrence of major adverse coronary events (MACE).ResultsAmong patients with low-intermediate ACS risk, R-GSPECT and S-GSPECT were associated with 100 and 86% sensitivity, 99 and 98% specificity, 100 and 98% negative predictive value, 80 and 86% positive predictive value, and 98 and 97% accuracy, respectively. At follow-up, MACE occurred in 16 patients. Among high-risk patients, GSPECT quantitative parameters were the most significant predictors of MACE in Cox regression analysis.ConclusionR/S-GSPECT, a noninvasive diagnostic method, is associated with an excellent safety profile and exceptional diagnostic and prognostic accuracy in cases of ACS. Copyright (C) 2015 Wolters Kluwer Health, Inc. All rights reserved

Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

We investigated the association between the textural features obtained from F-18-FDG images, metabolic parameters (SUVmax(,) SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage

Hepatic vein and portal vein Doppler ultrasound of maternal liver in normal pregnancy

Objective: The purpose of our study was to evaluate the changes occurring in maximum flow velocity and waveform patterns of the portal and hepatic veins during pregnancy in second and third trimesters

Short-Term Left Ventricular Remodeling After Revascularization in Subacute Total and Subtotal Occlusion With the Infarct-Related Left Anterior Descending Artery.

BACKGROUND: Large randomized studies revealed that percutaneous coronary intervention has no clinical benefit in patients with total occlusion. The purpose of this study is to evaluate left ventricular remodelling after PCI for total and subtotal infarct-related left anterior desending artery in stable patients who have not received trombolytic theraphy. METHODS: Sixty stable patients with subacute anterior myocardial infarction who have total or subtotal occlusion in the infarct-related left anterior descending artery were enrolled the study (20 patient in the total-medical group, 20 patient in the total-PCI group and 20 patient in the subtotal-PCI group). All patients’ left ventricular diameters, volumes and ejection fractions measured at admission and after a month. RESULTS: The necrotic segment number in scintigraphy were similar in three groups. In the total-PCI group, there were significant increases in left ventricular diastolic diameter, left ventricular end-diastolic volume and left ventricular end-systolic volume at first month. A borderline significant increase was observed in LVEDV in the total-medical group at first month. No significant difference was seen in all echocardiographic parameters in the subtotal-PCI group at a month after discharge. The percentage of increase in LVEDV was significantly higher and the percentage of increase in LVESV was borderline significantly higher in the total-PCI group than the other groups. CONCLUSIONS: In stable patients, PCI for total occlusion in the subacute phase of anterior MI causes an increase in LV remodeling. Nevertheless PCI for subtotal occlusion in the subacute phase of anterior MI may prevent LV remodeling