A novel higher order appearance texture analysis to diagnose lung cancer based on a modified local ternary pattern.

BACKGROUND AND OBJECTIVE: Lung cancer is an important cause of death and morbidity around the world. Two of the primary computed tomography (CT) imaging markers that can be used to differentiate malignant and benign lung nodules are the inhomogeneity of the nodules' texture and nodular morphology. The objective of this paper is to present a new model that can capture the inhomogeneity of the detected lung nodules as well as their morphology. METHODS: We modified the local ternary pattern to use three different levels (instead of two) and a new pattern identification algorithm to capture the nodule's inhomogeneity and morphology in a more accurate and flexible way. This modification aims to address the wide Hounsfield unit value range of the detected nodules which decreases the ability of the traditional local binary/ternary pattern to accurately classify nodules' inhomogeneity. The cut-off values defining these three levels of the novel technique are estimated empirically from the training data. Subsequently, the extracted imaging markers are fed to a hyper-tuned stacked generalization-based classification architecture to classify the nodules as malignant or benign. The proposed system was evaluated on in vivo datasets of 679 CT scans (364 malignant nodules and 315 benign nodules) from the benchmark Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and an external dataset of 100 CT scans (50 malignant and 50 benign). The performance of the classifier was quantitatively assessed using a Leave-one-out cross-validation approach and externally validated using the unseen external dataset based on sensitivity, specificity, and accuracy. RESULTS: The overall accuracy of the system is 96.17% with 97.14% sensitivity and 95.33% specificity. The area under the receiver-operating characteristic curve was 0.98, which highlights the robustness of the system. Using the unseen external dataset for validating the system led to consistent results showing the generalization abilities of the proposed approach. Moreover, applying the original local binary/ternary pattern or using other classification structures achieved inferior performance when compared against the proposed approach. CONCLUSIONS: These experimental results demonstrate the feasibility of the proposed model as a novel tool to assist physicians and radiologists for lung nodules' early assessment based on the new comprehensive imaging markers.

Efficient framework for brain tumor detection using different deep learning techniques.

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is tested on three publicly available Magnetic Resonance Imaging (MRI) datasets. The simulation results show that the BRAIN-TUMOR-net achieves the highest accuracy compared to other models. It achieves 100%, 97%, and 84.78% accuracy levels for three different MRI datasets. In addition, the k-fold cross-validation technique is used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation.

COVID-19 and myocarditis: a brief review.

Cardiovascular complications (especially myocarditis) related to COVID-19 viral infection are not well understood, nor do they possess a well recognized diagnostic protocol as most of our information regarding this issue was derived from case reports. In this article we extract data from all published case reports in the second half of 2020 to summarize the theories of pathogenesis and explore the value of each diagnostic test including clinical, lab, ECG, ECHO, cardiac MRI and endomyocardial biopsy. These tests provide information that explain the mechanism of development of myocarditis that further paves the way for better management.

Design, synthesis, molecular docking, in silico ADMET profile and anticancer evaluations of sulfonamide endowed with hydrazone-coupled derivatives as VEGFR-2 inhibitors.

A new series of sulfonamide endowed with hydrazone coupled to dimethyl and/or diethyl malonates were prepared. Various sulfa drugs were diazotized and followed by coupling with active methylene of dimethyl and/or diethyl malonate to afford the new intermediates hydrazones 3a-c and 4a-c. The reactivity of hydrazone derivatives towards hydrazines was investigated. Thus, a novel series of 3,5-dioxopyrazolidine7a-cwere obtained by treatment with hydrazine hydrate. When hydrazones were allowed to react with phenyl hydrazine, the alkyl 2-((4-(N-(substituted)sulfamoyl)phenyl)diazenyl)-3-oxo-3-(2-phenylhydrazinyl)propanoateswere obtained 8a-c and/or 10a-c. Their anticancer activities were evaluated against HepG2, HCT-116 and MCF-7. HepG2 was the most sensitive one. In particular, compounds 7c, 7b and 10c were found to be the most potent derivatives with IC50 = 6.43 ± 0.5, 9.66 ± 0.8, 10.57 ± 0.9 µM, 8.65 ± 0.7, 7.49 ± 0.6, 14.29 ± 1.3 µM and 8.97 ± 0.7, 10.13 ± 0.9, 13.82 ± 1.1 µM respectively. Sorafenib and doxorubicin were used as reference drugs. The most potent derivatives 7a, 7b, 7c, 8c and 10c were tested for their cytotoxicity against normal VERO cell lines. Compounds 7a, 7b, 7c, 8c and 10c are respectively, 2.41, 4.85, 4.08, 3.23 and 5.89 fold times more toxic in HCT116 than in VERO normal cells. Moreover, the most active anti-proliferative derivatives 7a, 7b, 7c, 8c and 10c were subjected to further biological study to evaluate their inhibitory potentials against VEGFR-2. The tested compounds displayed high to good inhibitory activity with IC50 values ranging from 0.14 ± 0.02 to 0.23 ± 0.03 µM. Among them, compounds 7c, 7b and 10c were found to be the most potent derivative that inhibited VEGFR-2 at IC50 values of 0.14 ± 0.02, 0.15 ± 0.02 and 0.15 ± 0.02 µM respectively. sorafenib was used as reference drug. Furthermore, ADMET profile was evaluated for the four most active compounds in comparison to doxorubicin as a reference drug. The data obtained from docking studies were highly correlated with that obtained from the biological screening.

Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI.

OBJECTIVE: Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. METHODS: This paper presents a computer-aided diagnostic (CAD) system for early ARTR detection using (3D + b-value) diffusion-weighted (DW) magnetic resonance imaging (MRI) data. The CAD process starts from kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The evolution is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and on-going kidney-background visual appearances. A B-spline-based three-dimensional data alignment is employed to handle local deviations due to breathing and heart beating. Then, empirical cumulative distribution functions of apparent diffusion coefficients of the segmented DW-MRI at different b-values are collected as discriminatory transplant status features. Finally, a deep-learning-based classifier with stacked nonnegative constrained autoencoders is employed to distinguish between rejected and nonrejected renal transplants. RESULTS: In our initial "leave-one-subject-out" experiment on 100 subjects, [Formula: see text] of the subjects were correctly classified. The subsequent four-fold and ten-fold cross-validations gave the average accuracy of [Formula: see text] and [Formula: see text], respectively. CONCLUSION: These results demonstrate the promise of this new CAD system to reliably diagnose renal transplant rejection. SIGNIFICANCE: The technology presented here can significantly impact the quality of care of renal transplant patients since it has the potential to replace the gold standard in kidney diagnosis, biopsy.

A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images.

We address the problem of prostate lesion detection, localization, and segmentation in T2W magnetic resonance (MR) images. We train a deep convolutional encoder-decoder architecture to simultaneously segment the prostate, its anatomical structure, and the malignant lesions. To incorporate the 3D contextual spatial information provided by the MRI series, we propose a novel 3D sliding window approach, which preserves the 2D domain complexity while exploiting 3D information. Experiments on data from 19 patients provided for the public by the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) show that our approach outperforms traditional pattern recognition and machine learning approaches by a significant margin. Particularly, for the task of cancer detection and localization, the system achieves an average AUC of 0.995, an accuracy of 0.894, and a recall of 0.928. The proposed mono-modal deep learning-based system performs comparably to other multi-modal MR-based systems. It could improve the performance of a radiologist in prostate cancer diagnosis and treatment planning.

A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules.

A novel framework for the classification of lung nodules using computed tomography scans is proposed in this article. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following 2 groups of features: (1) appearance features modeled using the higher order Markov Gibbs random field model that has the ability to describe the spatial inhomogeneities inside the lung nodule and (2) geometric features that describe the shape geometry of the lung nodules. The novelty of this article is to accurately model the appearance of the detected lung nodules using a new developed seventh-order Markov Gibbs random field model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted geometric features. Finally, a deep autoencoder classifier is fed by the above 2 feature groups to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium. We used a total of 727 nodules that were collected from 467 patients. The proposed system demonstrates the promise to be a valuable tool for the detection of lung cancer evidenced by achieving a nodule classification accuracy of 91.20%.