AdaRes: A deep learning-based model for ultrasound image denoising: Results of image quality metrics, radiomics, artificial intelligence, and clinical studies.

PURPOSE: The quality of ultrasound images is degraded by speckle and Gaussian noises. This study aims to develop a deep-learning (DL)-based filter for ultrasound image denoising. METHODS: A novel DL-based filter using adaptive residual (AdaRes) learning was proposed. Five image quality metrics (IQMs) and 27 radiomics features were used to evaluate denoising results. The effect of our proposed filter, AdaRes, on four pre-trained convolutional neural network (CNN) classification models and three radiologists was assessed. RESULTS: AdaRes filter was tested on both natural and ultrasound image databases. IQMs results indicate that AdaRes could remove noises in three different noise levels with the highest performances. In addition, a radiomics study proved that AdaRes did not distort tissue textures and it could preserve most radiomics features. AdaRes could also improve the performance classification using CNNs in different settings. Finally, AdaRes also improved the mean overall performance (AUC) of three radiologists from 0.494 to 0.702 in the classification of benign and malignant lesions. CONCLUSIONS: AdaRes filtered out noises on ultrasound images more effectively and can be used as an auxiliary preprocessing step in computer-aided diagnosis systems. Radiologists may use it to remove unwanted noises and improve the ultrasound image quality before the interpretation.

Deep Radiomics Features of Median Nerves for Automated Diagnosis of Carpal Tunnel Syndrome With Ultrasound Images: A Multi-Center Study.

OBJECTIVES: Ultrasound is widely used in diagnosing carpal tunnel syndrome (CTS). However, the limitations of ultrasound in CTS detection are the lack of objective measures in the detection of nerve abnormality and the operator-dependent nature of ultrasound imaging. Therefore, in this study, we developed and proposed externally validated artificial intelligence (AI) models based on deep-radiomics features. METHODS: We have used 416 median nerves from 2 countries (Iran and Colombia) for the development (112 entrapped and 112 normal nerves from Iran) and validation (26 entrapped and 26 normal nerves from Iran, and 70 entrapped and 70 normal nerves from Columbia) of our models. Ultrasound images were fed to the SqueezNet architecture to extract deep-radiomics features. Then a ReliefF method was used to select the clinically significant features. The selected deep-radiomics features were fed to 9 common machine-learning algorithms to choose the best-performing classifier. The 2 best-performing AI models were then externally validated. RESULTS: Our developed model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.910 (88.46% sensitivity, 88.46% specificity) and 0.908 (84.62% sensitivity, 88.46% specificity) with support vector machine and stochastic gradient descent (SGD), respectively using the internal validation dataset. Furthermore, both models consistently performed well in the external validation dataset, and achieved an AUC of 0.890 (85.71% sensitivity, 82.86% specificity) and 0.890 (84.29% sensitivity and 82.86% specificity), with SVM and SGD models, respectively. CONCLUSION: Our proposed AI models fed with deep-radiomics features performed consistently with internal and external datasets. This justifies that our proposed system can be employed for clinical use in hospitals and polyclinics.

Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models.

OBJECTIVES: Multiparametric MRI (mp-MRI) has been significantly used for detection, localization and staging of Prostate cancer (PCa). However, all the assessment suffers from poor reproducibility among the readers. The aim of this study was to evaluate radiomics models to diagnose PCa using high-resolution T2-weighted (T2-W) and dynamic contrast-enhanced (DCE) MRI. MATERIALS AND METHODS: Thirty two patients who had high prostate specific antigen level were recruited. The prostate biopsies considered as the reference to differentiate between 66 benign and 36 malignant prostate lesions. 181 features were extracted from each modality. K-nearest neighbors, artificial neural network, decision tree, and linear discriminant analysis were used for machine-learning study. The leave-one-out cross-validation method was used to prevent overfitting and build robust models. RESULTS: Radiomics analysis showed that T2-W images were more effective in PCa detection compare to DCE images. Local binary pattern features and speeded up robust features had the highest ability for prediction in T2-W and DCE images, respectively. The classifier fusion using decision template method showed the highest performance with accuracy, specificity, and sensitivity of 100%. DISCUSSION: The findings of this framework provide researchers on PCa with a promising method for reliable detection of prostate lesions in MR images by fused model.

An open-access breast lesion ultrasound image database : Applicable in artificial intelligence studies.

Breast cancer is one of the largest single contributors to the burden of disease worldwide. Early detection of breast cancer has been shown to be associated with better overall clinical outcomes. Ultrasonography is a vital imaging modality in managing breast lesions. In addition, the development of computer-aided diagnosis (CAD) systems has further enhanced the importance of this imaging modality. Proper development of robust and reproducible CAD systems depends on the inclusion of different data from different populations and centers to considerate all variations in breast cancer pathology and minimize confounding factors. The current database contains ultrasound images and radiologist-defined masks of two sets of histologically proven benign and malignant lesions. Using this and similar pieces of data can aid in the development of robust CAD systems.

Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models.

OBJECTIVES: Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients with papillary thyroid cancer. METHODS: An in-house deep learning-based model called "ClymphNet" was developed and tested using two datasets containing ultrasound images of 195 malignant and 178 benign lymph nodes. An expert radiologist also viewed these ultrasound images and extracted qualitative imaging features used in routine clinical practice. These signs were used to train three different machine learning algorithms. Then the deep learning model was compared with the machine learning models on internal and external validation datasets containing 22 and 82 malignant and 20 and 76 benign lymph nodes, respectively. RESULTS: Among the three machine learning algorithms, the support vector machine model (SVM) outperformed the best, reaching a sensitivity of 91.35%, specificity of 88.54%, accuracy of 90.00%, and an area under the curve (AUC) of 0.925 in all cohorts. The ClymphNet performed better than the SVM protocol in internal and external validation, achieving a sensitivity of 93.27%, specificity of 92.71%, and an accuracy of 93.00%, and an AUC of 0.948 in all cohorts. CONCLUSION: A deep learning model trained with ultrasound images outperformed three conventional machine learning algorithms fed with qualitative imaging features interpreted by radiologists. Our study provides evidence regarding the utility of ClymphNet in the early and accurate differentiation of benign and malignant lymphadenopathy.

Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts.

PURPOSE: To develop and validate a machine learning (ML) model for the classification of breast lesions on ultrasound images. METHOD: In the present study, three separate data cohorts containing 1288 breast lesions from three countries (Malaysia, Iran, and Turkey) were utilized for MLmodel development and external validation. The model was trained on ultrasound images of 725 breast lesions, and validation was done separately on the remaining data. An expert radiologist and a radiology resident classified the lesions based on the BI-RADS lexicon. Thirteen morphometric features were selected from a contour of the lesion and underwent a three-step feature selection process. Five features were chosen to be fed into the model separately and combined with the imaging signs mentioned in the BI-RADS reference guide. A support vector classifier was trained and optimized. RESULTS: The diagnostic profile of the model with various input data was compared to the expert radiologist and radiology resident. The agreement of each approach with histopathologic specimens was also determined. Based on BI-RADS and morphometric features, the model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.885, which is higher than the expert radiologist and radiology resident performances with AUC of 0.814 and 0.632, respectively in all cohorts. DeLong's test also showed that the AUC of the ML protocol was significantly different from that of the expert radiologist (ΔAUCs = 0.071, 95%CI: (0.056, 0.086), P = 0.005). CONCLUSIONS: These results support the possible role of morphometric features in enhancing the already well-excepted classification schemes.

Tumor Microenvironment, Radiology, and Artificial Intelligence: Should We Consider Tumor Periphery?

OBJECTIVES: The tumor microenvironment (TME) consists of cellular and noncellular components which enable the tumor to interact with its surroundings and plays an important role in the tumor progression and how the immune system reacts to the malignancy. In the present study, we investigate the diagnostic potential of the TME in differentiating benign and malignant lesions using image quantification and machine learning. METHODS: A total of 229 breast lesions and 220 cervical lymph nodes were included in the study. A group of expert radiologists first performed medical imaging and segmented the lesions, after which a rectangular mask was drawn, encompassing all of the contouring. The mask was extended in each axis up to 50%, and 29 radiomics features were extracted from each mask. Radiomics features that showed a significant difference in each contour were used to develop a support vector machine (SVM) classifier for benign and malignant lesions in breast and lymph node images separately. RESULTS: Single radiomics features extracted from extended contours outperformed radiologists' contours in both breast and lymph node lesions. Furthermore, when fed into the SVM model, the extended models also outperformed the radiologist's contour, achieving an area under the receiver operating characteristic curve of 0.887 and 0.970 in differentiating breast and lymph node lesions, respectively. CONCLUSIONS: Our results provide convincing evidence regarding the importance of the tumor periphery and TME in medical imaging diagnosis. We propose that the immediate tumor periphery should be considered for differentiating benign and malignant lesions in image quantification studies.

Interpretation of radiomics features-A pictorial review.

Radiomics is a newcomer field that has opened new windows for precision medicine. It is related to extraction of a large number of quantitative features from medical images, which may be difficult to detect visually. Underlying tumor biology can change physical properties of tissues, which affect patterns of image pixels and radiomics features. The main advantage of radiomics is that it can characterize the whole tumor non-invasively, even after a single sampling from an image. Therefore, it can be linked to a "digital biopsy". Physicians need to know about radiomics features to determine how their values correlate with the appearance of lesions and diseases. Indeed, physicians need practical references to conceive of basics and concepts of each radiomics feature without knowing their sophisticated mathematical formulas. In this review, commonly used radiomics features are illustrated with practical examples to help physicians in their routine diagnostic procedures.

Nonlinear optical response of cancer cells following conventional and nano-technology based treatment strategies: Results of chemo-, thermo- and radiation therapies.

BACKGROUND: Although traditional treatments are able to increase cancer survival rate, undesirable impact on off-target tissues are considered a limitation of these approaches. Nanotechnology-based treatments have been proposed as a possible option to enhance targeting., Further,current methods for evaluating cellular damage, are time consuming, highly dependent on the operator skills, and expensive. The aim of this study was to evaluate the capability of nonlinear optical response of cells to determine cellular damages during conventional and nano-technology based treatments. METHODS: Three different cancer cell lines, CT26, KB, and MCF-7 were used in this study. The alginate hydrogel co-loaded with cisplatin and Au nanoparticle (ACA) nanocomplex and gold-coated iron oxide nanoparticle (Au@IONP) were considered for chemo- and chemo-photothermal therapies, and thermo-radiation therapy, respectively. The sign and value of nonlinear optical absorption coefficient and imaginary part of the third-order nonlinear susceptibility of cells were computed. MTT assay was utilized as a reference method. RESULTS: The value of nonlinear optical indices increased with increasing cellular damage and cell death. The linear regression analysis indicated high correlation between nonlinear optical indices and MTT results, in all treatments. CONCLUSION: The nonlinear optical indices are robust from confounding factors, namely treatment approach (traditional and nano-technology based), treatment modality (chemotherapy, thermotherapy, photothermal therapy, and radiation therapy), and cell types. Nonlinear optical properties of cells can be used as a rapid estimation method for cell damage, at the nanoscale level.

COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings.

OBJECTIVES: CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients. METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases. RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier. CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis. KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.

The impact of frame numbers on cardiac ECG-gated SPECT images with interpolated extra frames using echocardiography.

Background: Cardiac echocardiography and cardiac ECG-gated single-photon emission computed tomography (SPECT) are the most common modalities for left ventricle (LV) volumes and function assessment. The temporal resolution of SPECT images is limited and an ECG provides better temporal resolution. This study investigates the impact of frame numbers on images in terms of qualitative and quantitative assessments. Methods: In this study, 5 patients underwent echocardiography and cardiac ECG-gated SPECT imaging, and 5 standard views of the LV were recorded to determine LV walls boundaries and volumes. Also, 2 original images with 8 frames and 16 frames per cardiac cycle were recorded simultaneously in a single gantry orbit. Using the data extracted from the LV model, 8 extra new frames were created with interpolation between existing frames of the original 8-frame image. Three series of images (8 and 16 original and 16 interpolated) were reconstructed separately. LV volumes and ejection fraction (EF) were calculated using Quantitative Gated SPECT (QGS) software. Results: Compared to the original 8-frame gating, original 16-frame gated images resulted in larger end-diastole volume (EDV) (mean ± SD: 68.6 ± 27.11 mL vs 66.2±25.41 mL, p<0.001), smaller end-systole volume (ESV) (mean ± SD: 24.6±8.7 mL vs 26±7.3 mL, p<0.001), and higher EF (64% vs 60.2%, p<0.001). The results for the interpolated series were also different from the original images (closer to the original 16-frame series rather than 8-frame). Conclusion: Changing the frame number from 8 to 16 in cardiac ECG-gated SPECT images caused a significant change in LV volumes and EF. Frame interpolation with sophisticated algorithms can be used to improve the temporal resolution of SPECT images.

Clinical and radiological characteristics of pediatric patients with COVID-19: focus on imaging findings.

PURPOSE: CT imaging has been a detrimental tool in the diagnosis of COVID-19, but it has not been studied thoroughly in pediatric patients and its role in diagnosing COVID-19. METHODS: 27 pediatric patients with COVID-19 pneumonia were included. CT examination and molecular assay tests were performed from all participants. A standard checklist was utilized to extract information, and two radiologists separately reviewed the CT images. RESULTS: The mean age of patients was 4.7 ± 4.16 (mean ± SD) years. Seventeen patients were female, and ten were male. The most common imaging finding was ground-glass opacities followed by consolidations. Seven patients had a single area of involvement, five patients had multiple areas of involvement, and four patients had diffuse involvement. The sensitivity of CT imaging in diagnosing infections was 66.67%. Also, some uncommon imaging findings were seen, such as a tree-in-bud and lung collapse. CONCLUSION: CT imaging shows less involvement in pediatric compared to adult patients, due to pediatric patients having a milder form of the disease. CT imaging also has a lower sensitivity in detecting abnormal lungs compared to adult patients. The most common imaging findings are ground-glass opacities and consolidations, but other non-common imaging findings also exist.

CAD system based on B-mode and color Doppler sonographic features may predict if a thyroid nodule is hot or cold.

OBJECTIVES: The aim of this study was to evaluate if the analysis of sonographic parameters could predict if a thyroid nodule was hot or cold. METHODS: Overall, 102 thyroid nodules, including 51 hyperfunctioning (hot) and 51 hypofunctioning (cold) nodules, were evaluated in this study. Twelve sonographic features (i.e., seven B-mode and five Doppler features) were extracted for each nodule type. The isthmus thickness, nodule volume, echogenicity, margin, internal component, microcalcification, and halo sign features were obtained in the B-mode, while the vascularity pattern, resistive index (RI), peak systolic velocity, end diastolic velocity, and peak systolic/end diastolic velocity ratio (SDR) were determined, based on Doppler ultrasounds. All significant features were incorporated in the computer-aided diagnosis (CAD) system to classify hot and cold nodules. RESULTS: Among all sonographic features, only isthmus thickness, nodule volume, echogenicity, RI, and SDR were significantly different between hot and cold nodules. Based on these features in the training dataset, the CAD system could classify hot and cold nodules with an area under the curve (AUC) of 0.898. Also, in the test dataset, hot and cold nodules were classified with an AUC of 0.833. CONCLUSIONS: 2D sonographic features could differentiate hot and cold thyroid nodules. The CAD system showed a great potential to achieve it automatically. KEY POINTS: • Cold nodules represent higher volume (p = 0.005), isthmus thickness (p = 0.035), RI (p = 0.020), and SDR (p = 0.044) and appear hypoechogenic (p = 0.010) in US. • Nodule volume with an AUC of 0.685 and resistive index with an AUC of 0.628 showed the highest classification potential among all B-mode and Doppler features respectively. • The proposed CAD system could distinguish hot nodules from cold ones with an AUC of 0.833 (sensitivity 90.00%, specificity 70.00%, accuracy 80.00%, PPV 87.50%, and NPV 75.00%).

A Hybrid Multilayer Filtering Approach for Thyroid Nodule Segmentation on Ultrasound Images.

OBJECTIVES: Speckle noise is the main factor that degrades ultrasound image contrast and segmentation failure. Determining an effective filter can reduce speckle noise and improve segmentation performances. The aim of this study was to define a useful filter to improve the segmentation outcome. METHODS: Twelve filters, including median, hybrid median (Hmed), Fourier Butterworth, Fourier ideal, wavelet (Wlet), homomorphic Fourier Butterworth, homomorphic Fourier ideal, homomorphic wavelet (Hmp_Wlet), frost, anisotropic diffusion, probabilistic patch-based (PPB), and homogeneous area filters, were used to find the best filter(s) to prepare thyroid nodule segmentation. A receiver operating characteristic (ROC) analysis was used for filter evaluation in the nodule segmentation process. Accordingly, 10 morphologic parameters were measured from segmented regions to find the best parameters that predict the segmentation performance. RESULTS: The best segmentation performance was reached by using 4 hybrid filters that mainly contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters. The area under the ROC curve for these filters ranged from 0.900 to 0.943 in comparison with the original image, with an area under the curve of 0.685. From 10 morphologic parameters, the area, convex area, equivalent diameter, solidity, and extent can evaluate segmentation performance. CONCLUSIONS: Hybrid filters that contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters have a high potential to provide good conditions for thyroid nodule segmentation in ultrasound images. In addition to an ROC analysis, morphometry of a segmented region can be used to evaluate segmentation performances.

A Clinical Decision Support System Using Ultrasound Textures and Radiologic Features to Distinguish Metastasis From Tumor-Free Cervical Lymph Nodes in Patients With Papillary Thyroid Carcinoma.

OBJECTIVES: This study investigated the potential of a clinical decision support approach for the classification of metastatic and tumor-free cervical lymph nodes (LNs) in papillary thyroid carcinoma on the basis of radiologic and textural analysis through ultrasound (US) imaging. METHODS: In this research, 170 metastatic and 170 tumor-free LNs were examined by the proposed clinical decision support method. To discover the difference between the groups, US imaging was used for the extraction of radiologic and textural features. The radiologic features in the B-mode scans included the echogenicity, margin, shape, and presence of microcalcification. To extract the textural features, a wavelet transform was applied. A support vector machine classifier was used to classify the LNs. RESULTS: In the training set data, a combination of radiologic and textural features represented the best performance with sensitivity, specificity, accuracy, and area under the curve (AUC) values of 97.14%, 98.57%, 97.86%, and 0.994, respectively, whereas the classification based on radiologic and textural features alone yielded lower performance, with AUCs of 0.964 and 0.922. On testing the data set, the proposed model could classify the tumor-free and metastatic LNs with an AUC of 0.952, which corresponded to sensitivity, specificity, and accuracy of 93.33%, 96.66%, and 95.00%. CONCLUSIONS: The clinical decision support method based on textural and radiologic features has the potential to characterize LNs via 2-dimensional US. Therefore, it can be used as a supplementary technique in daily clinical practice to improve radiologists' understanding of conventional US imaging for characterizing LNs.

Diagnosis of human prostate carcinoma cancer stem cells enriched from DU145 cell lines changes with microscopic texture analysis in radiation and hyperthermia treatment using run-length matrix.

PURPOSE: Hyperthermia and radiation have the ability to induce structural and morphological changes on both macroscopic and microscopic level. Normal and damage cells have a different texture but may be perceived by human eye, as having the same texture. MATERIALS AND METHODS: To explore the potential of texture analysis based on run-length matrix, a total of 32 sphere images for each group and treatment regime were used in this study. Cells were subjected to the treatment with different doses of 6 MeV electron radiation (0 2, 4 and 6 Gy), hyperthermia (at 43° C in 0, 30, 60 and 90 min) and radiation + hyperthermia (at 43 °C in 30 min with 2, 4 and 6 Gy dose), respectively. Twenty run-length matrix (RLM) features were extracted as descriptors for each selected region of interest for texture analysis. Linear discriminant analysis was employed to transform raw data to lower-dimensional spaces and increase discriminative power. RESULTS: The features were classified by the first nearest neighbor classifier. RLM features represented the best performance with sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of 100% between 0 and 6 Gy radiation, 0 and 6 Gy radiation + hyperthermia, 0 and 90 min and 30 and 90 min hyperthermia groups. The area under receiver operating characteristic curve was 1 for these groups. CONCLUSION: RLM features have a high potential to characterize cell changes during different treatment regimes.

Assessment of Kidney Function After Allograft Transplantation by Texture Analysis.

INTRODUCTION: Ultrasonography is the preferable imaging technique for monitoring and assessing complications in kidney allograft transplants. Computer-aided diagnostic system based on texture analysis in ultrasonographic imaging is recommended to identify changes in kidney function after allograft transplantation. MATERIALS AND METHODS: A total of 61 biopsy-proven kidney allograft recipients (11 rejected and 50 unrejected) were assessed by a computer-aided diagnostic system. Up to 270 statistical texture features were extracted as descriptors for each region of interest in each recipient. Correlations of texture features with serum creatinine level and differences between rejected and unrejected allografts were analyzed. An area under the receiver operating characteristic curve was calculated for each significant texture feature. Linear discriminant analysis was employed to analyze significant features and increase discriminative power. Recipients were classified by the first nearest neighbor classifier. RESULTS: Fourteen texture features had a significant correlation with serum creatinine level and 16 were significantly different between the rejected and unrejected allografts, for which an area under the curve values were in the range of 0.575 for difference entropy S(4,0) to 0.676 for kurtosis. Using all 16 features, linear discriminant analysis indicated higher performance for classification of the two groups with an area under the curve of 0.975, which corresponded to a sensitivity of 90.9%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 98.0%. CONCLUSIONS: Texture analysis was a reliable method, with the potential for characterization, and can help physicians to diagnose kidney failure after transplantation on ultrasonographic imaging.

Radioprotective effects of selenium and vitamin-E against 6MV X-rays in human blood lymphocytes by micronucleus assay.

BACKGROUND: Critical macromolecules of cells such as DNA are in exposure to damage of free radicals that induced from the interaction of ionizing radiation with biological systems. Selenium and vitamin-E are natural compounds that have been shown to be a direct free radical scavenger. The aim of this study was to investigate the radioprotective effect of selenium and vitamin-E separately and synergistically against genotoxicity induced by 6MV x-rays irradiation in blood lymphocytes. METHODS: Fifteen volunteers were divided into three groups include A, B and C. These groups were given selenium (800IU), vitamin-E (100mg) and selenium (400IU) + vitamin-E (50mg), respectively. Peripheral blood samples were collected from each group before (0hr) and 1, 2 and 3hr after selenium and vitamin-E administration (separately and synergistically). Then the blood samples were irradiated to 200cGy of 6MV x-rays. After that lymphocyte samples were cultured with mitogenic stimulation to determine the chromosomal aberrations with micronucleus assay in cytokinesis-blocked binucleated cells. RESULTS: The lymphocytes in the blood samples collected at one hr after ingestion selenium and vitamin-E, exposed in vitro to x-rays exhibited a significant decrease in the incidence of micronuclei, compared with control group at 0hr. The maximum protection and decrease in frequency of micronuclei (50%) were observed at one hr after administration of selenium and vitamin-E synergistically. CONCLUSION: The data suggest that ingestion of selenium and vitamin-E as a radioprotector substance before exposures may reduce genetic damage caused by x-rays irradiation.

Application of Texture Analysis in Diagnosis of Multiple Sclerosis by Magnetic Resonance Imaging.

INTRODUCTION: Visual inspection by magnetic resonance (MR) images cannot detect microscopic tissue changes occurring in MS in normal appearing white matter (NAWM) and may be perceived by the human eye as having the same texture as normal white matter (NWM). The aim of the study was to evaluate computer aided diagnosis (CAD) system using texture analysis (TA) in MR images to improve accuracy in identification of subtle differences in brain tissue structure. MATERIAL & METHODS: The MR image database comprised 50 MS patients and 50 healthy subjects. Up to 270 statistical texture features extract as descriptors for each region of interest. The feature reduction methods used were the Fisher method, the lowest probability of classification error and average correlation coefficients (POE+ACC) method and the fusion Fisher plus the POE+ACC (FFPA) to select the best, most effective features to differentiate between MS lesions, NWM and NAWM. The features parameters were used for texture analysis with principle component analysis (PCA) and linear discriminant analysis (LDA). Then first nearest-neighbour (1-NN) classifier was used for features resulting from PCA and LDA. Receiver operating characteristic (ROC) curve analysis was used to examine the performance of TA methods. RESULTS: The highest performance for discrimination between MS lesions, NAWM and NWM was recorded for FFPA feature parameters using LDA; this method showed 100% sensitivity, specificity and accuracy and an area of Az=1 under the ROC curve. CONCLUSION: TA is a reliable method with the potential for effective use in MR imaging for the diagnosis and prediction of MS.

Application of texture analysis method for classification of benign and malignant thyroid nodules in ultrasound images.

BACKGROUND: The aim of this study was to evaluate computer aided diagnosis (CAD) system with texture analysis (TA) to improve radiologists' accuracy in identification of thyroid nodules as malignant or benign. METHODS: A total of 70 cases (26 benign and 44 malignant) were analyzed in this study. We extracted up to 270 statistical texture features as a descriptor for each selected region of interests (ROIs) in three normalization schemes (default, 3s and 1%-99%). Then features by the lowest probability of classification error and average correlation coefficients (POE+ACC), and Fisher coefficient (Fisher) eliminated to 10 best and most effective features. These features were analyzed under standard and nonstandard states. For TA of the thyroid nodules, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA) were applied. First Nearest-Neighbour (1-NN) classifier was performed for the features resulting from PCA and LDA. NDA features were classified by artificial neural network (A-NN). Receiver operating characteristic (ROC) curve analysis was used for examining the performance of TA methods. RESULTS: The best results were driven in 1-99% normalization with features extracted by POE+ACC algorithm and analyzed by NDA with the area under the ROC curve ( Az) of 0.9722 which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. CONCLUSION: Our results indicate that TA is a reliable method, can provide useful information help radiologist in detection and classification of benign and malignant thyroid nodules.