Mai S. Mabrouk

Automatic Detection of Melanoma Skin Cancer using Texture Analysis

Melanoma is considered the most dangerous type of skin cancer. Early and accurate diagnosis depends mainly on important issues, accuracy of feature extracted and efficiency of classifier method. This paper presents an automated method for melanoma diagnosis applied on a set of dermoscopy images. Features extracted are based on gray level Co-occurrence matrix (GLCM) and Using Multilayer perceptron classifier (MLP) to classify between Melanocytic Nevi and Malignant melanoma. MLP classifier was proposed with two different techniques in training and testing process: Automatic MLP and Traditional MLP. Results indicated that texture analysis is a useful method for discrimination of melanocytic skin tumors with high accuracy. The first technique, Automatic iteration counter is faster but the second one, Default iteration counter gives a better accuracy, which is 100 % for the training set and 92 % for the test set.

Identification of rheumatoid arthritis biomarkers based on single nucleotide polymorphisms and haplotype blocks: A systematic review and meta-analysis

Graphical abstract

TMT-HCC

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. New insights into the pathogenesis of this lethal disease are urgently needed. Chromosomal copy number alterations (CNAs) can lead to activation of oncogenes and inactivation of tumor suppressors in human cancers. Thus, identification of cancer-specific CNAs will not only provide new insight into understanding the molecular basis of tumor genesis but also facilitate the identification of HCC biomarkers using CNA. This paper presents the TMT-HCC system; it is a tool for text mining the biomedical literature for hepatocellular carcinoma (HCC) biomarkers identification. TMT-HCC provides researchers with a powerful way to identify and discern molecular biomarkers of HCC to inform diagnosis, prognosis, and treatment driver genes with causal roles in carcinogenesis is to detect genomic regions that under frequent alterations in cancers (CNAs). TMT-HCC also extracts protein-protein interactions from the full text of the scientific papers. The results provided that the integration of genomic and transcriptional data offers powerful potential for identifying novel cancer genes in HCC pathogenesis.

Genetic Case-Control Study for Eight Polymorphisms Associated with Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disease which has a significant socio-economic impact. The aim of the current study was to investigate eight candidate RA susceptibility loci to identify the associated variants in Egyptian population. Eight single nucleotide polymorphisms (SNPs) (MTHFR—C677T and A1298C, TGFβ1 T869C, TNFB A252G, and VDR—ApaI, BsmI, FokI, and TaqI) were tested by genotyping patients with RA (n = 105) and unrelated controls (n = 80). Associations were tested using multiplicative, dominant, recessive, and co-dominant models. Also, the linkage disequilibrium (LD) between the VDR SNPs was measured to detect any indirect association. By comparing RA patients with controls (TNFB, BsmI, and TaqI), SNPs were associated with RA using all models. MTHFR C677T was associated with RA using all models except the recessive model. TGFβ1 and MTHFR A1298C were associated with RA using the dominant and the co-dominant models. The recessive model represented the association for ApaI variant. There were no significant differences for FokI and the presence of RA disease by the used models examination. For LD results, There was a high D′ value between BsmI and FokI (D′ = 0.91), but the r2 value between them was poor. All the studied SNPs may contribute to the susceptibility of RA disease in Egyptian population except for FokI SNP.

K6. Circular binary segmentation modeling of array CGH data on hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a malignant tumor derived from hepatocytes that belong to primary malignant epithelial tumors of the liver. The outcome of HCC patients still remains dismal due to the difficulty in detecting the disease at its early stage. We propose a new approach aiming to identify new biomarkers for early diagnosis of HCC. Genomic DNA copy number alterations (CNAs) are associated with complex diseases like HCC. Array-based Comparative Genomic Hybridization (a-CGH) is a technique used to identify copy number changes in genomic DNA. We use a statistical model based on a circular binary segmentation (CBS) algorithm. Our approach makes use of a median absolute deviation model to separate outliers from their surrounding segments. We tested 35 samples of HCC patients on specific chromosome regions, then applied CBS algorithm to detect genomic DNA alternations in copy number. Our results show that a gain of 1q was detected in 63% and a gain of 20q was detected in 26% of HCC cases. Also, a loss of 4q was detected in 3%, a loss of 13q was detected in 29%, loss in 16q was detected in 9%, and loss of 17q was detected in 3% of HCC cases.

A New Method to Grid Noisy cDNA Microarray Images Utilizing Denoising Techniques

DNA Microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. It is capable of testing and extracting the expression of large number of genes in parallel. The gene expression process is divided into three basic steps: gridding, segmentation, and quantification. Automatic gridding; which is to assign coordinates to every element of the spot array, is considered the most challenging phase of microarrays image processing. For processing of microarray images, a new, automatic, fast and accurate approach is proposed for gridding noisy cDNA microarray images. In the real world, microarray image doesn’t reflect measures of the fluorescence intensities for the dye of interest only, as different kinds of noise and artifacts can be observed. In this paper, a novel gridding method based on projection is developed accompanied by a pre-processing, post-processing, and refinement steps for noisy microarray images. Results revealed that the proposed method is used with high accuracy and minimal processing time and can be applied to various types of noisy microarray images.

Pigmented skin lesion diagnosis using geometric and chromatic features

Skin cancer appears to be one of the most dangerous types among others by the presence of malignant melanoma as one of pigmented skin lesion forms. Automated system for the purpose of pigmented skin lesion diagnosis mentioned through that paper is recommended as a non-invasive diagnosis tool. To obviate the problem of qualitative interpretation, two different image sets are used to examine the proposed system, a set of images acquired by standard camera (clinical images) and another set of dermoscopic images captured from the magnified dermoscope. Images are enhanced and segmented to separate the lesion from the background. Different geometric and chromatic features are extracted from the region of interest resulting from segmentation process. Then, the most prominent features that can cause an effect are selected by different selection methods; which are the Fisher score ranking and the t-test method. Most prominent features were introduced to two different classifiers; artificial neural network and Support vector machine for the discrimination of the two groups of lesions. System performance was measured regarding Specificity, Sensitivity and Accuracy. The artificial neural network designed with the combined geometric and chromatic features selected by fisher score ranking enabled a diagnostic accuracy of 95% for dermoscopic and 93.75% for clinical images.

Vitamin D receptor gene polymorphisms in rheumatoid arthritis patients associating osteoporosis

Rheumatoid arthritis and osteoporosis are major causes of disability associated with aging and are increasing in public health importance. Biostatisticians work to advance public health through biomedical research. Polymorphisms allow not only the detection of susceptible individuals to a certain disease but also the evaluation of the severity of the disease. The aim of this research is to study the influence of vitamin D receptor polymorphisms on rheumatoid arthritis patients with or without osteoporosis. Osteoporosis is a measurement of rheumatoid arthritis severity in the affected patients. Four single nucleotide polymorphisms (ApaI, BsmI, FokI, and TaqI) were tested by genotyping 17 rheumatoid arthritis patients with osteoporosis and 72 rheumatoid arthritis patients without osteoporosis. Associations were tested using multiplicative, dominant, recessive, and co-dominant models. Also, the linkage disequilibrium between the polymorphisms was measured to detect any indirect variant. The FokI variant was significantly associated with risk of osteoporosis in rheumatoid arthritis.

Automated Imaging System for Pigmented Skin Lesion Diagnosis

Through the study of pigmented skin lesions risk factors, the appearance of malignant melanoma turns the anomalous occurrence of these lesions to annoying sign. The difficulty of differentiation between malignant melanoma and melanocytic naive is the error-bone problem that usually faces the physicians in diagnosis. To think through the hard mission of pigmented skin lesions diagnosis different clinical diagnosis algorithms were proposed such as pattern analysis, ABCD rule of dermoscopy, Menzies method, and 7-points checklist. Computerized monitoring of these algorithms improves the diagnosis of melanoma compared to simple naked-eye of physician during examination. Toward the serious step of melanoma early detection, aiming to reduce melanoma mortality rate, several computerized studies and procedures were proposed. Through this research different approaches with a huge number of features were discussed to point out the best approach or methodology could be followed to accurately diagnose the pigmented skin lesion. This paper proposes automated system for diagnosis of melanoma to provide quantitative and objective evaluation of skin lesion as opposed to visual assessment, which is subjective in nature. Two different data sets were utilized to reduce the effect of qualitative interpretation problem upon accurate diagnosis. Set of clinical images that are acquired from a standard camera while the other set is acquired from a special dermoscopic camera and so named dermoscopic images. System contribution appears in new, complete and different approaches presented for the aim of pigmented skin lesion diagnosis. These approaches result from using large conclusive set of features fed to different classifiers. The three main types of different features extracted from the region of interest are geometric, chromatic, and texture features. Three statistical methods were proposed to select the most significant features that will cause a valuable effect in diagnosis; Fisher score method, t-test, and F-test. The selected features of high-ranking score based on the statistical methods are used for the diagnosis of the two lesion groups using Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) as three different classifiers proposed. The overall System performance was then measured in regards to Specificity, Sensitivity and Accuracy. According to the different approaches that will be mentioned later the best result was showen by the ANN designed with the feature selected according to fisher score method enables a diagnostic accuracy of 96.25% and 97% for dermoscopic and clinical images respectively.

Pigmented Skin Lesion Diagnosis by Automated Imaging System

Pigmented skin lesions are the normal part of the skin, however its anomalous appearance is an annoying sign due to the presence of melanomas one of its malignant forms. Although melanoma is a deadly considerable disease, its early detection is a serious step toward mortality reduction. The proposed research discusses the outcome of introducing312 different features in a non-invasive diagnosis method for pigmented skin lesion diagnosis. To obviate the problem of qualitative interpretation, two different image sets are utilized to examine the proposed system, a set of images acquired by standard camera (clinical images) and another set of dermoscopic images captured from the magnified dermoscope. System contribution appears in using large conclusive set of features fed to different classifiers composing totally complete, new and different approaches for the purpose of disease diagnosis. Miscellaneous types of features used such as geometric, chromatic, and texture features extracted from the region of interest resulted from segmentation process. Then, the most prominent features that can cause an effect are selected by three different methods; Fisher score method, t-test, and F-test. The high-ranking features are used for the diagnosis of the two lesion groups using Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) as three different classifiers proposed. System performance was measured in regards Specificity, Sensitivity and Accuracy. The ANN designed with the feature selected according to fisher score method enables a diagnostic accuracy of 96. 25% and 97% for dermoscopic and clinical images respectively.