scholarly journals Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Ahmad Chaddad ◽  
Camel Tanougast
Keyword(s):  
Colorectal Cancer ◽  
Performance Metrics ◽  
Principal Component ◽  
Texture Features ◽  
Intraepithelial Neoplasia ◽  
Cell Types ◽  
Abnormal Cell ◽  
Contour Segmentation ◽  
3D Texture ◽  
The Continuum

Abnormal cell (ABC) is a markedly heterogeneous tissue area and can be categorized into three main types: benign hyperplasia (BH), carcinoma (Ca), and intraepithelial neoplasia (IN) or precursor cancerous lesion. In this study, the goal is to determine and characterize the continuum of colorectal cancer by using a 3D-texture approach. ABC was segmented in preprocessing step using an active contour segmentation technique. Cell types were analyzed based on textural features extracted from the gray level cooccurrence matrices (GLCMs). Significant texture features were selected using an analysis of variance (ANOVA) of ABC with a p value cutoff of p<0.01. Features selected were reduced with a principal component analysis (PCA), which accounted for 97% of the cumulative variance from significant features. The simulation results identified 158 significant features based on ANOVA from a total of 624 texture features extracted from GLCMs. Performance metrics of ABC discrimination based on significant texture features showed 92.59% classification accuracy, 100% sensitivity, and 94.44% specificity. These findings suggest that texture features extracted from GLCMs are sensitive enough to discriminate between the ABC types and offer the opportunity to predict cell characteristics of colorectal cancer.

Role of inflammation in the development of colorectal cancer

2020 ◽  
Vol 20 ◽  
Author(s):  
Sridhar Muthusami ◽  
R. Ileng Kumaran ◽  
Kokelavani Nampalli Babu ◽  
Sneha Krishnamoorthy ◽  
Akash Guruswamy ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Chronic Inflammation ◽  
Interleukin 1 ◽  
Cell Types ◽  
Model Systems ◽  
Cytokine Gene Expression ◽  
Necrosis Factor Alpha ◽  
Tnf Α ◽  
Proinflammatory Molecules

: Chronic inflammation can lead to the development of many diseases including cancer. Inflammatory bowel disease (IBD) that includes both ulcerative colitis (UC) and Crohn's disease (CD) are risk factors for the development of colorectal cancer (CRC). Many cytokines produced primarily by the gut immune cells either during or in response to localized inflammation in the colon and rectum are known to stimulate the complex interactions between the different cell types in the gut environment resulting in acute inflammation. Subsequently, chronic inflammation together with genetic and epigenetic changes has been shown to lead to the development and progression of CRC. Various cell types present in the colon such as enterocytes, Paneth cells, goblet cells and macrophages express receptors for inflammatory cytokines and respond to tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6 and other cytokines. Among the several cytokines produced, TNF-α and IL-1β are the key proinflammatory molecules that play critical roles in the development of CRC. The current review is intended to consolidate the published findings to focus on the role of proinflammatory cytokines, namely TNF-α and IL-1β, on inflammation (and the altered immune response) in the gut, to better understand the development of CRC in IBD, using various experimental model systems, preclinical and clinical studies. Moreover, this review also highlights the current therapeutic strategies available (monotherapy and combination therapy), to alleviate the symptoms or treat inflammationassociated CRC by using monoclonal antibodies or aptamers to block proinflammatory molecules, inhibitors of tyrosine kinases in inflammatory signaling cascade, competitive inhibitors of proinflammatory molecules, and the nucleic acid drugs like small activating RNAs (saRNAs) or microRNA (miRNA) mimics to activate tumor suppressor or repress oncogene/proinflammatory cytokine gene expression.

scholarly journals Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 51
Author(s):  
Emanuela Paladini ◽  
Edoardo Vantaggiato ◽  
Fares Bougourzi ◽  
Cosimo Distante ◽  
Abdenour Hadid ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Digital Pathology ◽  
Texture Features ◽  
Tissue Type ◽  
Cancer Tissue ◽  
Learning Methods ◽  
Feature Based ◽  
Type Classification ◽  
Whole Slide Images

In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

scholarly journals UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm

2021 ◽  
Vol 13 (6) ◽  
pp. 1205
Author(s):  
Caidan Zhao ◽  
Gege Luo ◽  
Yilin Wang ◽  
Caiyun Chen ◽  
Zhiqiang Wu
Keyword(s):  
Recognition Performance ◽  
Principal Component ◽  
Texture Features ◽  
Training Model ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Dynamic Feature ◽  
Complex Environments ◽  
Time Frequency ◽  
Model Training

A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.

A study on the genomic alterations of transformation from colorectal adenoma to colorectal cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15607-e15607
Author(s):  
Qingjian Chen ◽  
Pan Yang ◽  
Linna Luo ◽  
Wenhua Fan ◽  
Chen Wei ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Patients ◽  
Tissue Sample ◽  
Intraepithelial Neoplasia ◽  
Stage I ◽  
Tissue Samples ◽  
Diagnostic Potential ◽  
Notch Pathways ◽  
Colorectal Cancer Patients ◽  
Villous Adenomas

e15607 Background: Colorectal cancer is one of the most common malignancies worldwide. Approximately 85% of colorectal cancers are thought to result from adenoma. However, the molecular mechanism of adenoma transformation into colorectal cancer is still unclear. Methods: Ninety-nine adenoma patients aged from 25 to 78 years old were enrolled in this study. We collected tissue sample from each patient and 77 matched blood samples. Pathological subtypes included tubular villous adenomas, villous adenomas, tubular adenomas, high-grade intraepithelial neoplasia, and polyps. Eighty-one stage I colorectal cancer patients (CRC I) were also enrolled in this study. All samples underwent Next-generation sequencing with a panel of 405 cancer related genes. Results: Mutational profiles of adenoma and CRC I patients were compared. The top 5 most frequently mutated genes in adenoma were APC (71%), KRAS (41%), ATM (33%), RIF1 (31%), SYNE1 (28%). While in CRC I patients, top 5 mutated genes were APC (78%), TP53 (57%), TTN (35%), KRAS (33%) and TCF7L2 (22%). There were significant differences between TP53 and TTN by chi-square test. The frequency, number and TMB of mutations in stage I colorectal cancer patients were significantly higher than those in various adenoma subtypes. Stage I colorectal cancer patients have more mutated genes enriched in the Wnt and Notch pathways than adenoma patients. We analyzed mutation signatures in CRC I and adenoma patients, and CRC I were more focused on mutation signatures of mismatch repair such as signature 1, signature 6, signature 10, and signature 15. A total of 391 mutations were identified in tissue samples, while 130 mutations were found in plasma cell-free DNA, with 116 mutations shared between them. The two genes with the highest consistency between tissue and blood were PAX7 and KMT2D. Conclusions: TP53 and TTN are associated with the transition from CRC I to adenoma, and Wnt and Notch pathways may also be involved. PAX7 and KMT2D mutations frequently found in adenoma tissue and blood cfDNA demonstrate the diagnostic potential of these two genes in clinic.

scholarly journals Pharmacologically targeting tribbles gene expression in colorectal cancer

2021 ◽  
Vol 31 (Supplement_2) ◽  
Author(s):  
Victor Yassuda ◽  
Ana Luísa De Sousa-Coelho
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Cell Types ◽  
Disease Recurrence ◽  
Rosemary Extract ◽  
Acid Oxidation ◽  
Data Sets ◽  
Compensatory Mechanism ◽  
Dependent Manner ◽  
Pharmacological Potential

Abstract Background TRIB1, TRIB2 and TRIB3 belong to the mammalian Tribbles family of pseudokinases proteins. Several studies reported Tribbles oncogenic role in different types of cancer, including colorectal cancer (CRC). Though current CRC treatment can be curative, patients are in risk of disease recurrence, meaning novel pharmacological targets and strategies are required. Our goal was to analyze Tribbles gene expression in CRC in response to different drugs. Methods Tribbles transcript levels were obtained from GEO profiles database (NCBI). Gene data sets (GDS) were selected based on experimental drug treatment description. Statistical analysis was performed at GraphPadPrism. Results Compared to non-treated control, TRIB2 expression was ∼2-fold increased in colorectal adenocarcinoma samples from patients treated with cyclooxygenase-2 inhibitor celecoxib (GDS3384), though not statistically significant (P &lt; 0.1). TRIB1 was unaltered and data for TRIB3 was not available. By contrast, all Tribbles showed differential expression after treatment of SW620 colon cancer cells with supercritical rosemary extract in progressive increasing doses (0, 30, 60, 100 μg/mL) (P &lt; 0.01;GDS5416). While both TRIB1 and TRIB3 were moderately increased in a dose-dependent manner (∼18% and 13%, respectively), TRIB2 was maximally down-regulated by ∼15% after 60 μg/mL. Conclusions Although celecoxib exhibits antiproliferative effects in different cancer cell types, TRIB2 gene expression showed a trend to be induced after treatment, in contrast to several genes involved in fatty acid oxidation that were down-regulated, which could result from a compensatory mechanism based on a metabolic shift. Since TRIB1/TRIB3 and TRIB2 were oppositely modulated in response to rosemary extract, additional studies are needed to validate its specific pharmacological potential interest for CRC treatment.

scholarly journals Multiplexed 3D atlas of state transitions and immune interactions in colorectal cancer

2021 ◽  
Author(s):  
Jia-Ren Lin ◽  
Shu Wang ◽  
Shannon Coy ◽  
Madison A Tyler ◽  
Clarence Yapp ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prognostic Significance ◽  
Tissue Microarrays ◽  
Cell Types ◽  
Tissue Imaging ◽  
State Transitions ◽  
Small Scale ◽  
Spatial Gradients ◽  
Immunosuppressive Cell ◽  
Small Fields

Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells that invade adjacent tissue and spread to distant sites. Here we use highly multiplexed tissue imaging, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. We find that a thorough spatial analysis requires imaging the entire tumor region, not small fields of view (e.g. those found in tissue microarrays). When this condition is met, the data reveal frequent transitions between histological archetypes (tumor grades and morphologies) correlated with molecular gradients. At the tumor invasive margin, where tumor, normal, and immune cells compete, localized features in 2D such as tumor buds and mucin pools are seen in 3D to be large connected structures having continuously varying molecular properties. Immunosuppressive cell-cell interactions also exhibit graded variation in type and frequency. Thus, whereas scRNA-Seq emphasizes discrete changes in tumor state, whole-specimen imaging reveals the presence of large- and small-scale spatial gradients analogous to those in developing tissues.

Predictably Predictable -  The Role of Catchment Characteristics and Complexity.

2021 ◽  
Author(s):  
Sophia Eugeni ◽  
Eric Vaags ◽  
Steven V. Weijs
Keyword(s):  
Water Resource Management ◽  
Performance Metrics ◽  
Complexity Analysis ◽  
Model Performance ◽  
Principal Component ◽  
The United States ◽  
Hydrologic Model ◽  
Hydrologic Models ◽  
Hydrologic Modelling

&lt;p&gt;Accurate hydrologic modelling is critical to effective water resource management. As catchment attributes strongly influence the hydrologic behaviors in an area, they can be used to inform hydrologic models to better predict the discharge in a basin. Some basins may be more difficult to accurately predict than others. The difficulty in predicting discharge may also be related to the complexity of the discharge signal. The study establishes the relationship between a catchment&amp;#8217;s static attributes and hydrologic model performance in those catchments, and also investigates the link to complexity, which we quantify with measures of compressibility based in information theory.&amp;#160;&lt;/p&gt;&lt;p&gt;The project analyzes a large national dataset, comprised of catchment attributes for basins across the United States, paired with established performance metrics for corresponding hydrologic models. Principal Component Analysis (PCA) was completed on the catchment attributes data to determine the strongest modes in the input. The basins were clustered according to their catchment attributes and the performance within the clusters was compared.&amp;#160;&lt;/p&gt;&lt;p&gt;Significant differences in model performance emerged between the clusters of basins. For the complexity analysis, details of the implementation and technical challenges will be discussed, as well as preliminary results.&lt;/p&gt;

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