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The editorial discusses the effectiveness of artificial intelligence (AI) in improving colonoscopy quality for colorectal cancer (CRC) screening. AI, through computer-aided detection (CADe) and characterization (CADx), has shown promise in increasing adenoma detection rates (ADR) and polyp detection rates (PDR). However, there are limitations to AI implementation, such as the need for extensive training data and potential increased procedure time. While some studies have shown significant improvements in colonoscopy markers with AI, others have reported mixed results, indicating the need for further research and consideration of confounding factors before widespread adoption of AI in healthcare. [Extracted from the article]

According to the World Health Organization, lung and colon cancers are known for their high mortality rates which necessitate the diagnosis of these cancers at an early stage. However, the limited availability of data such as histopathology images used for diagnosis of these cancers, poses a significant challenge while developing computer‐aided detection system. This makes it necessary to keep a check on the number of parameters in the artificial intelligence (AI) model used for the detection of these cancers considering the limited availability of the data. In this work, a customised compact and efficient convolution transformer architecture, termed, C3‐Transformer has been proposed for the diagnosis of colon and lung cancers using histopathological images. The proposed C3‐Transformer relies on convolutional tokenisation and sequence pooling approach to keep a check on the number of parameters and to combine the advantage of convolution neural network with the advantages of transformer model. The novelty of the proposed method lies in efficient classification of colon and lung cancers using the proposed C3‐Transformer architecture. The performance of the proposed method has been evaluated on the 'LC25000' dataset. Experimental results shows that the proposed method has been able to achieve average classification accuracy, precision and recall value of 99.30%, 0.9941 and 0.9950, in classifying the five different classes of colon and lung cancer with only 0.0316 million parameters. Thus, the present computer‐aided detection system developed using proposed C3‐Transformer can efficiently detect the colon and lung cancers using histopathology images with high detection accuracy. [ABSTRACT FROM AUTHOR]

Colon cancer (CC) is the second leading cause of cancer-related deaths in the United States. Quality measures have been introduced by the American Gastroenterological Association and Commission on Cancer for optimal management of CC. In this study, we sought to identify factors that may hinder the timely diagnosis and treatment of CC at a safety-net hospital system. Retrospective chart review was performed for patients aged ≥18 y diagnosed with CC from 2018 to 2021. Primary outcomes were time from positive fecal immunochemical test to colonoscopy, time from diagnosis to surgery, and time from diagnosis to adjuvant chemotherapy. Secondary end points were demographic characteristics associated with suboptimal outcomes in any of the above measures. One hundred ninety patients were diagnosed with nonmetastatic CC. The majority were Hispanic and non-English–speaking. 74.1% of patients with a positive fecal immunochemical test received a colonoscopy within 180 d. 59.6% of nonemergent cases received surgery within 60 d of diagnosis. 77% of those eligible received adjuvant chemotherapy within 120 d of diagnosis. No clinically significant demographic factor was associated with delay in colonoscopy, surgery, or adjuvant chemotherapy. Most frequent cause of delay in surgery (38.0%) was optimization of comorbidities. Most frequent cause of delay in adjuvant chemotherapy (71.4%) was delay in surgery itself. No clinically significant demographic factor was associated with experiencing delays in diagnostic colonoscopy, surgery, or adjuvant chemotherapy. [ABSTRACT FROM AUTHOR]

Traditional health care relies on biomedical image categorization to identify and treat various medical conditions. In machine learning and medical imaging, biomedical image classification for colon and lung cancer diagnosis is significant. The work focuses on building novel models and algorithms to accurately detect and categorize tumorous lesions using computer tomography (CT) scans and histopathology slides. These systems use image processing, deep learning (DL), and convolutional neural networks (CNN) to assist medical professionals diagnose cancer sooner and improve patient outcomes. Biomedical image classification using seagull optimization with deep learning (BIC-SGODL) addresses colon and lung cancer diagnosis. The BIC-SGODL method improves cancer diagnosis using hyperparameter optimized DL model. BIC-SGODL utilizes DenseNet to learn complicated features. The convolutional long short-term memory (CLSTM) standard captures spatiotemporal information in sequential picture data. Finally, the SGO method adjusts hyperparameters to improve model performance and generalization. BIC-SGODL performs well with biomedical image dataset simulations. Thus, medical picture cancer diagnosis may be automated using BIC-SGODL. [ABSTRACT FROM AUTHOR]

Introduction: People with low income have worse outcomes throughout the cancer care continuum; however, little is known about income and the diagnostic interval. We described diagnostic pathways by neighborhood income and investigated the association between income and the diagnostic interval. Methods: This was a retrospective cohort study of colon cancer patients diagnosed 2007–2019 in Ontario using routinely collected data. The diagnostic interval was defined as the number of days from the first colon cancer encounter to diagnosis. Asymptomatic pathways were defined as first encounter with a colonoscopy or guaiac fecal occult blood test not occurring in the emergency department and were examined separately from symptomatic pathways. Quantile regression was used to determine the association between neighborhood income quintile and the conditional 50th and 90th percentile diagnostic interval controlling for age, sex, rural residence, and year of diagnosis. Results: A total of 64,303 colon cancer patients were included. Patients residing in the lowest income neighborhoods were more likely to be diagnosed through symptomatic pathways and in the emergency department. Living in low‐income neighborhoods was associated with longer 50th and 90th‐percentile symptomatic diagnostic intervals compared to patients living in the highest income neighborhoods. For example, the 90th percentile diagnostic interval was 15 days (95% CI 6–23) longer in patients living in the lowest income neighborhoods compared to the highest. Conclusion: These findings reveal income inequities during the diagnostic phase of colon cancer. Future work should determine pathways to reducing inequalities along the diagnostic interval and evaluate screening and diagnostic assessment programs from an equity perspective. [ABSTRACT FROM AUTHOR]

Early detection of the adenocarcinoma cancer in colon tissue by means of explainable deep learning, by classifying histological images and providing visual explainability on model prediction. Considering that in recent years, deep learning techniques have emerged as powerful techniques in medical image analysis, offering unprecedented accuracy and efficiency, in this paper we propose a method to automatically detect the presence of cancerous cells in colon tissue images. Various deep learning architectures are considered, with the aim of considering the best one in terms of quantitative and qualitative results. As a matter of fact, we consider qualitative results by taking into account the so-called prediction explainability, by providing a way to highlight on the tissue images the areas that from the model point of view are related to the presence of colon cancer. The experimental analysis, performed on 10,000 colon issue images, showed the effectiveness of the proposed method by obtaining an accuracy equal to 0.99. The experimental analysis shows that the proposed method can be successfully exploited for colon cancer detection and localisation from tissue images. [ABSTRACT FROM AUTHOR]

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN–Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively. [ABSTRACT FROM AUTHOR]

Introduction: Colorectal Cancer (CRC) is a significant health problem worldwide, with more than 70,000 new cancer cases recorded in India in 2020. A better understanding of CRC prognosis is needed. The substantial intratumoural heterogeneity among tumours of different stages has entailed the research for new biomarkers to more clearly identify tumour biology and behaviour. Micro Ribonucleic Acids (miRNAs) are non coding RNAs comprised of approximately 20-25 nucleotides and play an important role in epigenetic regulations. Studies have demonstrated that miRNAs play a critical role in tumourigenesis, metastasis, and tumour response to treatment. Comprehensive knowledge of miRNAs as potential markers of colon cancer diagnosis, prognosis, and predictive factors is crucial. Aim: To assess the miRNA signature (miR21, miR31, and miR34a) in colon cancer tumour samples and to evaluate the association of the miRNA signature with the clinicopathological profile and Pathological Tumour/Node/Metastasis (pTNM) stage of CRC patients. Materials and Methods: This cross-sectional and prospective study was conducted at the Departments of Pathology, Molecular Research and Diagnostics and Surgical Oncology of Sri Shankara Cancer Hospital, Bengaluru, Karnataka, India and was comprised of a total of 69 CRC patients who underwent surgery with a curative intent. The study was conducted over an 18-month period from January 2020 to June 2021. miRNA was extracted from Formalin-fixed Paraffin-embedded (FFPE) samples, and quantitative Polymerase Chain Reaction (qPCR) was done to get corresponding ΔCT (Threshold Cycles) values. The expression of miR21, miR31, and miR34a was evaluated, and their association with different clinicopathological parameters like age, sex, tumour stage, grade, Carcinoembryonic Antigen (CEA) levels, Perineural Invasion (PNI), and Lymphovascular Invasion (LVI) status was studied. The expression of the mentioned miRNAs was assigned low and high values on the basis of median values. Spearman's correlation was done to check for any significant associations. Results: All three miRNAs (miR21, miR31, miR34a) were found to have lower values in the >60 years age group compared to the <60 years age group. Higher values of miRNA were found in male patients than in females, with a p-value of 0.022 for miR21. However, miRNA expression (miR21, miR31, miR34a) did not show any statistically significant correlation with tumour location (p-values 0.543, 0.255, 0.255), lymph node status (p-values 0.676, 0.153, 0.930), TNM stage (p-values 0.273, 0.509, 0.898), LVI (p-values 0.233, 0.233, 0.733), PNI (p-values 0.686, 0.263, 0.756), and serum CEA level (p-values 0.543, 0.255, 0.255). Conclusion: The present study showed the possible tumoursuppressive role of miR34a in CRC. Although miR21 acts as an oncogenic miRNA in many cancers, in CRC, its expression differs between males and females, with most tumours in males exhibiting high expression. [ABSTRACT FROM AUTHOR]

Background • Early detection of colorectal cancer (CRC) can lead to earlier diagnosis and intervention, thereby improving patient survival. Existing techniques fall short of clinical needs. Thus, early detection of CRC still needs a cost-effective, efficient, and widely accepted screening tool. Objective • This study aimed to evaluate the performance of a Multi-gene Methylation Detection Kit for Human Colorectal Cancer in a series of standards and clinical samples. Design/Outcome Measures • A series of DNA standards and 88 patients were included. According to the kit’s instructions, a simplified multiplex quantitative polymerase chain reaction method was used to detect the methylation level of the samples. The accuracy, limit of detection, interference factors, sensitivity, and other performance parameters of the kit were studied. Results • Statistical analysis of the test results of all standards in the verification experiment showed that the positive and negative coincidence rates were 100%. The results for the kit’s minimum detection limit and minimum nucleic acid input met the expected standards. The kit’s sensitivity, specificity and accuracy were 89.36%, 97.56% and 93.18%, respectively for clinical samples. Conclusion • The Multi-gene Methylation Detection kit for Colorectal Cancer has a high detection performance for CRC, and this non-invasive, convenient and high- performance method for early detection of CRC may address current limitations in CRC screening and meet the clinical expectations. [ABSTRACT FROM AUTHOR]

Background: An association between diverticulitis and colon cancer has been proposed. The evidence is conflicting, and the guidelines differ regarding recommended follow-up with colonoscopy after an episode of diverticulitis. To guide regimes for follow-up, this study aimed to investigate if patients with diverticulitis have an increased risk of colon cancer. Methods: This study is reported according to the RECORD statement. We performed a cohort study with linked data from nationwide Danish registers. The inclusion period was 1997–2009, and the complete study period was 1995–2013. The primary outcome was the risk of developing colon cancer estimated using a Cox regression analysis with time-varying covariates. We performed a sensitivity analysis on a cohort of people with prior colonoscopies, comparing the risk of colon cancer between the diverticulitis group and the control group. Results: We included 29,173 adult males and females with diverticulitis and 145,865 controls matched for sex and age. The incidence proportion of colon cancer was 2.1% (95% confidence interval (CI) 1.9–2.3) in the diverticulitis group and 1.5% (95% CI 1.4–1.5) in the matched control group (hazard ratio 1.6; 95% CI 1.5–1.8). The risk of having a colon cancer diagnosis was significantly increased in the first six months after inclusion (hazard ratio 1.7; 95% CI 1.5–1.8), and hereafter there was a lower risk in the diverticulitis group compared with controls (hazard ratio 0.8; 95% CI 0.7–0.9). This protective effect lasted eight years. The increased risk of colon cancer during the first six months after diverticulitis was also found in the cohort with prior colonoscopies. Conclusions: The risk of a colon cancer diagnosis was significantly increased for patients with diverticulitis 0–6 months after the diagnosis of diverticulitis. Hereafter, we found a protective effect of diverticulitis until eight years later, possibly due to a screening effect. We recommend a follow-up colonoscopy after the first diagnosis of diverticulitis. [ABSTRACT FROM AUTHOR]

Colorectal cancer is one of the most lethal cancers because of late diagnosis and challenges in the selection of therapy options. The histopathological diagnosis of colon adenocarcinoma is hindered by poor reproducibility and a lack of standard examination protocols required for appropriate treatment decisions. In the current study, using state-of-the-art approaches on benchmark datasets, we analyzed different architectures and ensembling strategies to develop the most efficient network combinations to improve binary and ternary classification. We propose an innovative two-stage pipeline approach to diagnose colon adenocarcinoma grading from histological images in a similar manner to a pathologist. The glandular regions were first segmented by a transformer architecture with subsequent classification using a convolutional neural network (CNN) ensemble, which markedly improved the learning efficiency and shortened the learning time. Moreover, we prepared and published a dataset for clinical validation of the developed artificial neural network, which suggested the discovery of novel histological phenotypic alterations in adenocarcinoma sections that could have prognostic value. Therefore, AI could markedly improve the reproducibility, efficiency, and accuracy of colon cancer diagnosis, which are required for precision medicine to personalize the treatment of cancer patients. [ABSTRACT FROM AUTHOR]

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The colloidal synthesis of CdSe/ZnSe core/shell quantum dots (QDs) under nitrogen gas flow is presented in this study. The optical, morphology and structural properties of CdSe/ZnSe core/shell QDs are varied by changing the growth temperature and the cadmium source (Cd2+). The Fourier transform infra‐red studies validate that CdSe/ZnSe QDs capped with functional −COOH groups were formed. The morphological and composition studies conducted using high resolution transmission microscope, selected area electron diffraction andenergy dispersive spectroscope confirmed the formation of pure and mono‐disperse QDs while photoluminescence and ultra‐violet visible spectroscopy displayed their improved fluorescent properties. Caco‐2 cell line was cultured from colon cancer and cytotoxic studies were conducted to test for cell viability of various capped 3‐MPA CdSe/ZnSe QDs at various concentrations. The formed CdSe/ZnSe QDs prominently bear the zinc‐blende structure with their particle size lying well below the Bohr excitation radii. Their hydrophilic nature and the availability of functional groups on the CdSe/ZnSe QDs makes them excellent potential labeling and therapeutic agents for the colon cancer disease diagnosis. [ABSTRACT FROM AUTHOR]

Background: Many studies compare state‐level outcomes to estimate changes attributable to Medicaid expansion. However, it is imperative to conduct more granular, demographic‐level analyses to inform current efforts on cancer prevention among low‐income adults. Therefore, the authors compared the volume of patients with cancer and disease stage at diagnosis in Ohio, which expanded its Medicaid coverage in 2014, with those in Georgia, a nonexpansion state, by cancer site and health insurance status. Methods: The authors used state cancer registries from 2010 to 2017 to identify adults younger than 64 years who had incident female breast cancer, cervical cancer, or colorectal cancer. Multivariable Poisson regression was conducted by cancer type, health insurance, and state to examine the risk of late‐stage disease, adjusting for individual‐level and area‐level covariates. A difference‐in‐differences framework was then used to estimate the differences in risks of late‐stage diagnosis in Ohio versus Georgia. Results: In Ohio, the largest increase in all three cancer types was observed in the Medicaid group after Medicaid expansion. In addition, significantly reduced risks of late‐stage disease were observed among patients with breast cancer on Medicaid in Ohio by approximately 7% and among patients with colorectal cancer on Medicaid in Ohio and Georgia after expansion by approximately 6%. Notably, the authors observed significantly reduced risks of late‐stage diagnosis among all patients with colorectal cancer in Georgia after expansion. Conclusions: More early stage cancers in the Medicaid‐insured and/or uninsured groups after expansion suggest that the reduced cancer burden in these vulnerable population subgroups may be attributed to Medicaid expansion. Heterogeneous risks of late‐stage disease by cancer type highlight the need for comprehensive evaluation frameworks, including local cancer prevention efforts and federal health policy reforms. Plain Language Summary: This study looked at how Medicaid expansion affected cancer diagnosis and treatment in two states, Ohio and Georgia.The researchers found that, after Ohio expanded their Medicaid program, there were more patients with cancer among low‐income adults on Medicaid.The study also found that, among people on Medicaid, there were lower rates of advanced cancer at the time of diagnosis for breast cancer and colon cancer in Ohio and for colon cancer in Georgia.These findings suggest that Medicaid expansion may be effective in reducing the cancer burden among low‐income adults. To address gaps in the current understanding of the impact of Medicaid expansion among more granular demographic groups in states that did and did not expand their Medicaid programs, the authors compared the volume of patients with cancer and disease stage at diagnosis in Ohio, which expanded its Medicaid program in 2014, versus those measures in Georgia, a nonexpansion state, by cancer site and health insurance type. More early‐stage cancer cases were identified in the Medicaid group after expansion, suggesting that reduced cancer burden among low‐income adults may be attributable to Medicaid expansion among low‐income adults and highlighting the need for comprehensive evaluation frameworks, including local cancer prevention efforts and Medicaid expansion. [ABSTRACT FROM AUTHOR]

The early-stage diagnosis of cancer is a crucial clinical need. The inadequacies of surgery tissue biopsy have prompted a transition to a less invasive profiling of molecular biomarkers from biofluids, known as liquid biopsy. Exosomes are phospholipid bilayer vesicles present in many biofluids with a biologically active cargo, being responsible for cell-to-cell communication in biological systems. An increase in their excretion and changes in their cargo are potential diagnostic biomarkers for an array of diseases, including cancer, and they constitute a promising analyte for liquid biopsy. The number of exosomes released, the morphological properties, the membrane composition, and their content are highly related to the physiological and pathological states. The main analytical challenge to establishing liquid biopsy in clinical practice is the development of biosensors able to detect intact exosomes concentration and simultaneously analyze specific membrane biomarkers and those contained in their cargo. Before analysis, exosomes also need to be isolated from biological fluids. Microfluidic systems can address several issues present in conventional methods (i.e., ultracentrifugation, size-exclusion chromatography, ultrafiltration, and immunoaffinity capture), which are time-consuming and require a relatively high amount of sample; in addition, they can be easily integrated with biosensing systems. A critical review of emerging microfluidic-based devices for integrated biosensing approaches and following the major analytical need for accurate diagnostics is presented here. The design of a new miniaturized biosensing system is also reported. A device based on hollow-fiber flow field-flow fractionation followed by luminescence-based immunoassay is applied to isolate intact exosomes and characterize their cargo as a proof of concept for colon cancer diagnosis. [ABSTRACT FROM AUTHOR]

Necrotizing fasciitis (NF) is a life-threatening soft tissue infection, typically caused by preexisting conditions such as trauma, complicated intraabdominal infections, or even small wounds. However, it is very rare for NF to occur as a result of perforated colon cancer (CC). Diagnosis primarily relies on clinical findings, imaging, and laboratory tests. Early diagnosis and treatment are crucial for patient survival. In this study, we present a case of an 82-year-old female a known case of CC diagnosed 1 month ago. She presented with hip pain persisting for 10 days duration, along with skin changes over the proximal anterolateral aspect of the thigh. The patient was diagnosed with NF associated with an iliopsoas abscess caused by perforated CC that was managed with surgical debridement, left hemicolectomy, and end colostomy along with broad-spectrum antibiotics. [ABSTRACT FROM AUTHOR]

HT-29 has an epithelial appearance as a human colorectal cancer cell line. Early detection of colorectal cancer can enhance survival rates. This study aims to detect and count HT-29 cells using a deep-learning approach (ResNet-50). The cell lines were procured from Procell Life Science & Technology Co., Ltd. (Wuhan, China). Further, the dataset is self-prepared in lab experiments, cell culture, and collected 566 images. These images contain two classes; the HT-29 human colorectal adenocarcinoma cells (blue shapes in bunches) and impurities (tinny circular grey shapes). These images are annotated with the help of an image labeller as impurity and cancer cells. Then afterwards, the images are trained, validated, and tested against the deep learning approach ResNet50. Finally, in each image, the number of impurity and cancer cells are counted to find the accuracy of the proposed model. Accuracy and computational expense are used to gauge the network's performance. Each model is tested ten times with a non-overlapping train and random test splits. The effect of data pre-processing is also examined and shown in several tasks. The results show an accuracy of 95.5% during training and 95.3% in validation for detecting and counting HT-29 cells. HT-29 cell detection and counting using deep learning is novel due to the scarcity of research in this area, the application of deep learning, and potential performance improvements over traditional methods. By addressing a gap in the literature, employing a unique dataset, and using custom model architecture, this approach contributes to advancing colon cancer understanding and diagnosis techniques. [ABSTRACT FROM AUTHOR]

Background: Colon cancer is one of the most prevalent cancers of the digestive tract. There is mounting evidence that genes associated with oxidative stress might affect the tumour immune microenvironment during tumour growth, maintenance, and treatment response. However, how oxidative stress-related genes affect prognostic importance, tumour microenvironment features, and treatment outcomes in colon cancer patients has not been fully elucidated. Methods: The Cancer Genome Atlas (TCGA) dataset was used to construct a signature model and nomogram using step and Cox regression approaches to investigate how gene expression affected immunological responses to colon cancer, including the degree of immune infiltration, MSI, and drug sensitivity. Results and conclusions: The nomogram and the signature model had strong prognostic potential for colon cancer, with gene expression highly correlated with multiple immune cells. The first signature model and nomogram including oxidative stress-related genes were constructed for use in clinical decision-making. In addition, SRD5A1, GSR, TXN, TRAF2 and TRAP1 were identified as potential biomarkers for colon cancer diagnosis and indicators for immunotherapy. [ABSTRACT FROM AUTHOR]

Radiologic imaging, especially MRI, has long been the mainstay for rectal cancer staging and patient selection for neoadjuvant therapy prior to surgical resection. In contrast, colonoscopy and CT have been the standard for colon cancer diagnosis and metastasis staging with T and N staging often performed at the time of surgical resection. With recent clinical trials exploring the expansion of the use of neoadjuvant therapy beyond the anorectum to the remainder of the colon, the current and future state of colon cancer treatment is evolving with a renewed interest in evaluating the role radiology may play in the primary T staging of colon cancer. The performance of CT, CT colonography, MRI, and FDG PET-CT for colon cancer staging will be reviewed. N staging will also be briefly discussed. It is expected that accurate radiologic T staging will significantly impact future clinical decisions regarding the neoadjuvant versus surgical management of colon cancer. [ABSTRACT FROM AUTHOR]

Aim: There are ample discussions regarding the timing of treatment, especially in the era after Covid that caused delay to treatment. The aim of this study was to determine whether a delayed start to curative treatment, within 29–56 days after a diagnosis of colon cancer, was noninferior to starting treatment within 28 days, with regard to all‐cause mortality. Method: This is a national register‐based observational noninferiority study, with a noninferiority margin of hazard ratio (HR) 1.1, including all patients treated with curative intent for colon cancer in Sweden between 2008 and 2016. The primary outcome was all‐cause mortality. Secondary outcomes were length of hospital stay, readmissions and reoperations within 1 year after surgery. Exclusion criteria were emergency surgery, disseminated disease at diagnosis, missing diagnosis date and treatment for another cancer 5 years before colon cancer diagnosis. Results: A total of 20 836 individuals were included. A period of 29–56 days from diagnosis to start of curative treatment was noninferior versus starting treatment within 28 days for the primary outcome of all‐cause mortality (HR 0.95, 95% CI 0.89–1.00). Starting treatment within 29–56 days was associated with a shorter length of stay (average 9.2 vs. 10 days) but a higher risk of reoperation compared to within 28 days. Post hoc analyses demonstrated that surgical modality was driving survival rather than time to treatment. Overall survival was greater after laparoscopic surgery (HR 0.78, 95% CI 0.69–0.88). Conclusion: For patients with colon cancer, a period of up to 56 days from diagnosis to the start of curative treatment did not lead to worse overall survival. [ABSTRACT FROM AUTHOR]

Resource-stratified guidelines (RSGs) can inform systemic treatment decisions in the face of limited resources. The objective of this study was to develop a customisable modelling tool to predict the demand, cost, and drug procurement needs of delivering National Comprehensive Cancer Network (NCCN) RSG-based systemic treatment for colon cancer. We developed decision trees for first-course systemic therapy for colon cancer based on the NCCN RSGs. Decision trees were merged with data from the Surveillance, Epidemiology, and End Results programme, the International Agency for Research on Cancer's GLOBOCAN 2020 national estimates for colon cancer incidence, country-level income data, and data on drug costs from Redbook (USA), the Pharmaceutical Benefits Scheme (Australia), and the Management Sciences for Health 2015 International Medical Products price guide to estimate global treatment needs and costs, and forecast drug procurement. Simulations and sensitivity analyses were used to explore the effect of scaling up services globally and the effect of alternative stage distributions on treatment demand and cost. We generated a customisable model, in which estimates can be tailored to local incidence, epidemiological, and costing data. First-course systemic therapy is indicated in 608 314 (53·6%) of 1 135 864 colon cancer diagnoses in 2020. Indications for first-course systemic therapy are projected to rise to 926 653 in 2040; the indications in 2020 might be as high as 826 123 (72·7%), depending on stage distribution assumptions. Adhering to NCCN RSGs, patients with colon cancer in low-income and middle income countries (LMICs) would constitute 329 098 (54·1%) of 608 314 global systemic therapy demands, but only 10% of global expenditure on systemic therapies. The total cost of NCCN RSG-based first-course systemic therapy for colon cancer in 2020 would be between about US$4·2 and about $4·6 billion, depending on stage distribution. If all patients with colon cancer in 2020 were treated according to maximal resources, global expenditure on systemic therapy for colon cancer would rise to around $8·3 billion. We have developed a customisable model that can be applied at global, national, and subnational levels to estimate systemic treatment needs, forecast drug procurement, and calculate expected drug costs on the basis of local data. This tool can be used to plan resource allocation for colon cancer globally. None. [ABSTRACT FROM AUTHOR]

Purpose: Colon cancer presents challenges to clinical diagnosis and management due to its high heterogeneity. For more efficient and convenient diagnosis and treatment of colon cancer, we are committed to characterizing the molecular features of colon cancer by pioneering a classification system based on metabolic pathways. Methods: Based on the 113 metabolic pathways and genes collected in the previous stage, we scored and filtered the metabolic pathways of each sample in the training set by ssGSEA, and obtained 16 metabolic pathways related to colon cancer recurrence. In consistent clustering of training set samples with recurrence-related metabolic pathway scores, we identified two robust molecular subtypes of colon cancer (MC1 and MC2). Furthermore, we performed multi-angle analysis on the survival differences of subtypes, metabolic characteristics, clinical characteristics, functional enrichment, immune infiltration, differences with other subtypes, stemness indices, TIDE prediction, and drug sensitivity, and finally constructed colon cancer prognostic model. Results: The results showed that the MC1 subtype had a poor prognosis based on higher immune activity and immune checkpoint gene expression. The MC2 subtype is associated with high metabolic activity and low expression of immune checkpoint genes and a better prognosis. The MC2 subtype was more responsive to PD-L1 immunotherapy than the MC1 subclass. However, we did not observe significant differences in tumor mutational burden between the two. Conclusion: Two molecular subtypes of colon cancer based on metabolic pathways have distinct immune signatures. Constructing prognostic models based on subtype differential genes provides valuable reference for personalized therapy targeting unique tumor metabolic signatures. [ABSTRACT FROM AUTHOR]

Developing computer-aided approaches for cancer diagnosis and grading is currently receiving an increasing demand: this could take over intra- and inter-observer inconsistency, speed up the screening process, increase early diagnosis, and improve the accuracy and consistency of the treatment-planning processes.The third most common cancer worldwide and the second most common in women is colorectal cancer (CRC). Grading CRC is a key task in planning appropriate treatments and estimating the response to them. Unfortunately, it has not yet been fully demonstrated how the most advanced models and methodologies of machine learning can impact this crucial task.This paper systematically investigates the use of advanced deep models (convolutional neural networks and transformer architectures) to improve colon carcinoma detection and grading from histological images. To the best of our knowledge, this is the first attempt at using transformer architectures and ensemble strategies for exploiting deep learning paradigms for automatic colon cancer diagnosis. Results on the largest publicly available dataset demonstrated a substantial improvement with respect to the leading state-of-the-art methods. In particular, by exploiting a transformer architecture, it was possible to observe a 3% increase in accuracy in the detection task (two-class problem) and up to a 4% improvement in the grading task (three-class problem) by also integrating an ensemble strategy. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this retrospective study was to predict circumferential resection margin (CRM) involvement on preoperative CT, and prognostic impact of CRM assessment by CT (ctCRM) in patients with retroperitonealized colon cancer. Methods: This study included patients who underwent resection for ascending or descending colon cancer between July 2010 and February 2013. Positive ctCRM was defined as tumor distance to the retromesenteric plane of ≤ 1 mm. The origin of positive CRM was divided into primary tumor or other tumor components including lymph nodes, tumor deposits, or extramural venous invasions. Logistic regression analysis was performed to identify preoperative factors to predict pathologic CRM (pCRM). A Cox proportional hazards model was used in multivariable analysis to determine the preoperative factors affecting disease-free survival (DFS). Results: A total of 274 patients (mean age, 64.0 years ± 11.0 [standard deviation]; 157 men) with retroperitonealized colon cancer were evaluated. Of 274 patients, 67 patients (24.5%) had positive CRM on surgical pathology. The accuracy of preoperative CT in predicting pCRM was 79.6% (218/274). Among preoperative factors, only CRM assessment on CT was independently associated with pCRM (p < 0.001). Positive ctCRM by primary tumor was an independent factor for DFS (HR, 3.362 [1.714–6.593]) and systemic recurrence (HR, 3.715 [1.787–7.724], but not for local recurrence on multivariable analyses. Conclusions: Preoperative CT can accurately predict pCRM, and positive ctCRM by primary tumor is an independent risk factor for DFS and systemic recurrence, but not for local recurrence in retroperitonealized colon cancer. Key Points: • Preoperative CT can predict pathologic circumferential resection margin (CRM) with approximately 80% of accuracy in patients with retroperitonealized colon cancer. • Positive CRM by a primary tumor on preoperative CT is a poor prognostic factor for disease-free survival and systemic recurrence in patients with retroperitonealized colon cancer. • CRM involvement on CT was not associated with local recurrence in patients with retroperitonealized colon cancer. [ABSTRACT FROM AUTHOR]

Colon cancer is the fourth leading cause of cancer death worldwide, and its progression is accompanied by a complex array of genetic variations. CRISPR/Cas9 can identify new drug-resistant or sensitive mutations in colon cancer, and can use gene editing technology to develop new therapeutic targets and provide personalized treatments, thereby significantly improving the treatment of colon cancer patients. CRISPR/Cas9 systems are driving advances in biotechnology. RNA-directed Cas enzymes have accelerated the pace of basic research and led to clinical breakthroughs. This article reviews the rapid development of CRISPR/Cas in colon cancer, from gene editing to transcription regulation, gene knockout, genome-wide CRISPR tools, therapeutic targets, stem cell genomics, immunotherapy, metabolism-related genes and inflammatory bowel disease. In addition, the limitations and future development of CRISPR/Cas9 in colon cancer studies are reviewed. In conclusion, this article reviews the application of CRISPR-Cas9 gene editing technology in basic research, diagnosis and treatment of colon cancer. [ABSTRACT FROM AUTHOR]

Background: Laparoscopic right hemicolectomy for the right-side colon cancer is well-established and proven to be better than the open approach in terms of post-operative and overall hospital stay. Laparoscopic right hemicolectomy can be done by lateral to medial approach (LA), medial to lateral approach (MA), or tunnel/IRETA approach (TA). No previous study has been conducted to compare the clinical outcomes of all three approaches and the superiority of one approach over the other is still debatable. Aims and Objectives: This retrospective study was conducted to compare all three approaches and to find the ideal one to practice. Materials and Methods: This is a retrospective cohort study carried out to analyze 86 patients who were admitted to the department of General Surgery from March 2015 to December 2021 with a diagnosis of the right-side colon cancer and underwent laparoscopic right hemicolectomy with either of these three approaches. A total of 29 patients had operated with lateral to medial approach (LA), 29 patients with medial approach (MA), and 28 with tunnel (TA) approach. The patient's baseline demographics, perioperative parameters, and post-operative outcomes were compared. Results: Intraoperative blood loss was significantly lower in the medial to lateral approach and tunnel approach. Duration of surgery was less in the medial to lateral approach as compared to the other two approaches and was statistically significant. R0 resection, lymph node dissection, intraoperative and post-operative complications, morbidity, mortality, and conversion to open were similar in all three groups. Conclusion: Laparoscopic medial to lateral to medial to lateral approach is feasible and safe and should be preferred approaches in laparoscopic right hemicolectomy. [ABSTRACT FROM AUTHOR]

Background: The incidence of early-onset (diagnosis before age 50) colorectal cancer (EO-CRC) is increasing in some high-income countries. The present study analyzed this trend in Germany. Methods: The epidemiological data on the incidence of colorectal cancer (ICD-10 C18-C20) in the federal states of Germany were obtained from the German Center for Cancer Registry Data (ZfKD). The regions included were those with adequate documentation, according to the ZfKD criteria, for the years 1999-2018. Cases identified from death certificates alone were excluded. The data on mortality were derived from the official cause of death statistics. Time trends for persons aged 20-49 years were expressed as the average annual percentage change (AAPC) with 95% confidence interval. The incidence was stratified by sex, site (proximal colon, distal colon, rectum), 10-year age group (20-29, 30-39, 40-49), and tumor size (T). Results: EO-CRC accounted for 5.1% (9529 cases) of all colorectal cancers in the selected German regions. The EO-CRC incidence rose annually by 1.16% [0.51; 1.81] in men and 1.32% [0.80; 1.84] in women. The incidence of proximal colon cancer increased in both sexes (men: AAPC 3.26 [2.00; 4.53]; women: AAPC 2.99 [2.17; 3.83]), while the incidence of distal colon cancer remained unchanged. Conclusion: The incidence of EO-CRC in Germany is rising. The reasons are probably multifactorial, reflecting the changing prevalence of risk and protective factors to which one is exposed in the early years of life. [ABSTRACT FROM AUTHOR]

Colorectal cancer is an important health problem with a significant impact on the individual and society. Malignancy (including colorectal cancer) is usually slightly symptomatic in its initial stages. This causes cancer to be discovered in some patients accidentally (either through screening tests in predisposed individuals or during routine investigations for other diseases), while in other patients the colorectal cancer is discovered in late stages, when the symptoms are much more intense due to complications. Unfortunately, such advanced cases of the disease have high rates of morbidity and mortality even with treatment. Current treatment methods are usually complex, interdisciplinary, causing significant suffering (physical, mental) to the individual, while the cost of treatment per patient seems to be extremely high. Until finding therapeutic methods that are effective and accessible to most patients with advanced colorectal cancer, several methods of prophylaxis and early diagnosis should be considered, to reduce as much as possible the devastating impact of this disease. The purpose of this review is to present literature data regarding the current methods of diagnosis and treatment of patients presenting to the doctor with colorectal cancer in advanced stages of complications. [ABSTRACT FROM AUTHOR]

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields. [ABSTRACT FROM AUTHOR]

Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a leading cause of cancer-related death, despite the fact that both traditional and modern methods are capable of comparing images that may encompass cancer regions of various sorts after looking at a significant number of colon cancer images. The fundamental problem for colon histopathologists is differentiating benign from malignant illnesses to having some complicated factors. A cancer diagnosis can be automated through artificial intelligence (AI), enabling us to appraise more patients in less time and at a decreased cost. Modern deep learning (MDL) and digital image processing (DIP) approaches are used to accomplish this. The results indicate that the proposed structure can accurately analyze cancer tissues to a maximum of 99.80%. By implementing this approach, medical practitioners will establish an automated and reliable system for detecting various forms of colon cancer. Moreover, CAD systems will be built in the near future to extract numerous aspects from colonoscopic images for use as a preprocessing module for colon cancer diagnosis. [ABSTRACT FROM AUTHOR]

The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around the world, almost half a million people contract cancer, including colon cancer. Determining the grade of colon cancer mainly depends on analyzing the gland's structure by tissue region, which has led to the existence of various tests for screening that can be utilized to investigate polyp images and colorectal cancer. This article presents a comprehensive survey on the diagnosis of colon cancer. This covers many aspects related to colon cancer, such as its symptoms and grades as well as the available imaging modalities (particularly, histopathology images used for analysis) in addition to common diagnosis systems. Furthermore, the most widely used datasets and performance evaluation metrics are discussed. We provide a comprehensive review of the current studies on colon cancer, classified into deep-learning (DL) and machine-learning (ML) techniques, and we identify their main strengths and limitations. These techniques provide extensive support for identifying the early stages of cancer that lead to early treatment of the disease and produce a lower mortality rate compared with the rate produced after symptoms develop. In addition, these methods can help to prevent colorectal cancer from progressing through the removal of pre-malignant polyps, which can be achieved using screening tests to make the disease easier to diagnose. Finally, the existing challenges and future research directions that open the way for future work in this field are presented. [ABSTRACT FROM AUTHOR]

Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh–Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT's reduced features obtained from the three DL models. Additionally, the three DL models' PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure. [ABSTRACT FROM AUTHOR]

Objectives: Robot-assisted laparoscopic surgeries (RLSs) have become increasingly common in the past decade alongside conventional laparoscopic surgeries (CLSs). In general, RLSs have been reported to be superior to CLSs; therefore, we compared both methods among patients undergoing an elective colectomy for differences in perioperative factors and postoperative complications.Methods: A retrospective analysis was conducted using the 2019 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database. We stratified 5879 patients into two cohorts according to their preoperative diagnosis of either colon cancer or chronic diverticulitis. Patients within each group were further stratified by operative approach.Results: Of the 5879 patients, 3210 colon cancer and 2669 chronic diverticulitis patients underwent an elective colectomy. There were no differences in length of stay and time from operation to discharge between RLSs and CLSs. RLSs, however, had significantly longer operation times (minutes; colon cancer: RLS 242.9 ± 91.0 vs CLS 177.4 ± 78.2, P < 0.001; chronic diverticulitis: RLS 226.2 ± 87.4 vs CLS 181.7 ± 74.4, P < 0.001). Among all of the colon cancer patients, RLS had higher rates of unplanned return to the operating room (P = 0.028) and organ space surgical site infection (P = 0.035). Among chronic diverticulitis patients, RLS was associated with higher rates of postoperative sepsis, anastomotic leak, organ space surgical site infection, and unplanned readmission (all P < 0.05).Conclusions: CLS may be the more efficient operative technique because it was associated with a shorter average operation time and fewer postoperative complications. This paradigm, however, may change as the robotic technology develops and surgeons become more experienced with RLS. [ABSTRACT FROM AUTHOR]

In the last four decades, medicine and healthcare have made revolutionary advances. During this time, the true causes of many diseases were discovered and new diagnostic procedures were devised and new remedies were invented. Globally, cancer is one of the serious diseases, which has become a widespread medical issue. A credible and early finding is especially important to reduce the risk of death. In any way, it is a difficult task that relies on the expertise of histopathologists. If a histologist is unprepared, a patient's life may be put in danger. Deep learning has gotten a lot of attention recently and is being used in medical imaging analysis. Artificial Intelligence (AI) can be used to automate cancer detection. To better classify and for quality improvement of histopathology images, visualization techniques GradCam and SmoothGard are applied. This objective can be achieved by evaluating histopathological images of five types of colon and lung tissues using MobileNetV2 and InceptionResnetV2 models. These proposed models have accurately identified cancer tissues to a maximum of 99.95%. These models will assist medical professionals in the advancement of an automated and authentic system for detecting different types of colon and lung cancers. [ABSTRACT FROM AUTHOR]

Colorectal cancer is one of the most aggressive types of cancer with about two million new cases and one million deaths in 2020. The side effects of the available chemotherapies and the possibility of developing resistance against treatment highlight the importance of developing new therapeutic options. The development in the field of nanotechnology have introduced the application of nanoparticles (NPs) as a promising approach in the diagnosis and treatments of colorectal cancer and other types of cancer. Gold nanoparticles (AuNPs) are currently one of the most studied materials as they possess unique tunable properties allowing them to play a role in colorectal cancer bioimaging, diagnosis, and therapy. The high surface-to-volume ratio of AuNPs mediates their utilization in drug delivery as well as functionalization to provide specific targeting. Moreover, depending on their physical properties (size, shape), AuNPs can be modified to fit the intended application. However, there are contradictory results around the pharmacokinetics of AuNPs including their biodistribution, clearance, and toxicity. This variation of opinions is most likely due to the development of different AuNPs that vary in shape, size, and surface chemistry, in addition to the conditions under which each research was carried out. The conflicting data represent a challenge in the clinical use of AuNPs suggesting the need to understand the toxicity, fate, and long-term exposure of AuNPs in vivo. Thus, there is an unmet need for the establishment of a publicly available data base for extensive analysis. In this review, we discuss the recent advances in AuNP applications in the treatment and diagnosis of colorectal cancer, mechanisms of action, and clinical challenges. [ABSTRACT FROM AUTHOR]

According to a recent report by GLOBOCAN, colorectal cancer is the third most common and second most deadly cancer in 2020. In our previous proteomic study, we found that the expression of GSTM2 in colon tissues was significantly lower than that in para-cancer tissues, and its lower expression was associated with reduced overall survival rate of patients, suggesting that this gene might play a role in the occurrence of colon cancer. As a member of the detoxifying enzyme family, GSTM2 is likely to play an important role in the initiation of tumors. Whereas, the functions of GSTM2 in colon cancer are barely known. In this study, using the RNA-Seq datasets of colon cancer patients from public database (ntumor = 457, nnormal = 41), we confirmed the reduced expression of GSTM2 and its prognostic value in colon cancer. Furthermore, we used our own Chinese cohort (ntumor = 100, nnormal = 72) verified the lower GSTM2 expression in colon cancer, and also its effects on patient prognosis. Subsequently, we uncovered two potential reasons for the lower expression of GSTM2 in colon cancer tissues, including the deep deletion of GSTM2 on genome, and the up-regulation of RAD21 or SP1. Moreover, we disclosed that GSTM2 might be involved in several immune- related pathways in colon cancer, such as chemokine signaling and leukocyte transendothelial migration. Finally, we revealed that the GSTM2 expression was closely related to the immune-related scores of colon cancer and the infiltration ratios of various immune cells, suggesting that GSTM2 might regulate the development of colon cancer by modulating immune microenvironment. In conclusion, we uncovered the prognostic value of GSTM2 based on the public data and our own data, revealed its potential regulatory role in tumor immune microenvironment, and disclosed the probable reasons for its lower expression in colon cancer. The findings of our study provide a potential prognostic biomarker and drug target for clinical diagnosis and treatment of colon cancer. [ABSTRACT FROM AUTHOR]

BACKGROUND: Failure to follow up on patients with rectal bleeding is common and may result in a delay in diagnosis of colorectal cancer or in missing high-risk adenomas. The authors' purpose was to create an electronic patient safety net for those diagnosed with rectal bleeding but who did not have colonoscopy to ensure proper detection of colonic abnormalities, including colon cancer. METHODS: In an integrated health delivery system serving < 4.6 million patients in Southern California, from 2014 to 2019, the authors electronically identified patients with rectal bleeding aged 45 to 80 years but with no recently documented colonoscopy. These cases were reviewed by a gastroenterologist to determine if colonoscopy was appropriate. The physician looked for known documentation as to the cause of rectal bleeding and verified no contraindications to the procedure; if indicated, testing was offered. RESULTS: Using the authors' safety net program, 1430 patients with rectal bleeding who needed and completed a colonoscopy were identified. Of those patients, 7.5% had an advanced adenoma or cancer, with a total of 20 cancers, and 34% had findings that warranted more frequent colonoscopy. CONCLUSIONS: The authors designed a safety net system that was able to capture information on patients with rectal bleeding who had not had a colonoscopy and detected in 34% colonic pathology that would have otherwise gone undetected. The program did not require many resources to implement and had the ability to potentially prevent harm from reaching patients whose rectal bleeding did not get prompt workup. Other health systems and practices should consider implementing a similar system. [ABSTRACT FROM AUTHOR]

Colonomics is a multi-omics dataset that includes 250 samples: 50 samples from healthy colon mucosa donors and 100 paired samples from colon cancer patients (tumor/adjacent). From these samples, Colonomics project includes data from genotyping, DNA methylation, gene expression, whole exome sequencing and micro-RNAs (miRNAs) expression. It also includes data from copy number variation (CNV) from tumoral samples. In addition, clinical data from all these samples is available. The aims of the project were to explore and integrate these datasets to describe colon cancer at molecular level and to compare normal and tumoral tissues. Also, to improve screening by finding biomarkers for the diagnosis and prognosis of colon cancer. This project has its own website including four browsers allowing users to explore Colonomics datasets. Since generated data could be reuse for the scientific community for exploratory or validation purposes, here we describe omics datasets included in the Colonomics project as well as results from multi-omics layers integration. Measurement(s) Colon Gene expression • Colon DNA methylation • Colon Genptyping data • Colon Copy Number Variation • Colon miRNAs • Colon Whole Exome Sequencing Technology Type(s) Affymetrix Human Genome U219 Array Plate platform • Illumina Infinium HumanMethylation 450k BeadChip • Affymetrix Genome-Wide Human SNP 6.0 array • Small RNA Assay of the Agilent 2100 Bioanalyzer • Agilent kit Sure Select XT Human All Exon 50MB Factor Type(s) batch • background Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment colonic mucosa Sample Characteristic - Location Catalonia Autonomous Community, Spain [ABSTRACT FROM AUTHOR]

The essay discusses the importance of men sharing their cancer diagnoses publicly to inspire others and raise awareness about the disease. It highlights the cases of Defense Secretary Lloyd Austin and actor Chadwick Boseman, who faced criticism for keeping their diagnoses private. The essay also emphasizes the racial disparities in cancer rates, particularly in prostate and colorectal cancer among Black men. It mentions public figures like Al Roker and Trey Mancini who have shared their cancer journeys to encourage others to get screened. The author, who is a cancer survivor, advocates for men to break the silence surrounding cancer and support one another. [Extracted from the article]

We explore the task of tissue classification for colon cancer histology in a low label regime comparing a semi-supervised and a supervised learning strategy in a series of experiments. Further, we investigate the model robustness w.r.t. distribution shifts in the unlabeled data and domain shifts across different scanners to prove their practicality in a histology context. By utilizing unlabeled data in addition to nl = 1000 labeled tiles per class, we yield a substantial increase in accuracy from 89.9% to 91.4%. [ABSTRACT FROM AUTHOR]

Coronavirus disease-2019 (COVID-19) is a new types of coronavirus which have turned into a pandemic within a short time. Reverse transcription–polymerase chain reaction (RT-PCR) test is used for the diagnosis of COVID-19 in national healthcare centers. Because the number of PCR test kits is often limited, it is sometimes difficult to diagnose the disease at an early stage. However, X-ray technology is accessible nearly all over the world, and it succeeds in detecting symptoms of COVID-19 more successfully. Another disease which affects people's lives to a great extent is colorectal cancer. Tissue microarray (TMA) is a technological method which is widely used for its high performance in the analysis of colorectal cancer. Computer-assisted approaches which can classify colorectal cancer in TMA images are also needed. In this respect, the present study proposes a convolutional neural network (CNN) classification approach with optimized parameters using gradient-based optimizer (GBO) algorithm. Thanks to the proposed approach, COVID-19, normal, and viral pneumonia in various chest X-ray images can be classified accurately. Additionally, other types such as epithelial and stromal regions in epidermal growth factor receptor (EFGR) colon in TMAs can also be classified. The proposed approach was called COVID-CCD-Net. AlexNet, DarkNet-19, Inception-v3, MobileNet, ResNet-18, and ShuffleNet architectures were used in COVID-CCD-Net, and the hyperparameters of this architecture was optimized for the proposed approach. Two different medical image classification datasets, namely, COVID-19 and Epistroma, were used in the present study. The experimental findings demonstrated that proposed approach increased the classification performance of the non-optimized CNN architectures significantly and displayed a very high classification performance even in very low value of epoch. [ABSTRACT FROM AUTHOR]

Colon cancer is a prevalent invasive neoplasm in the gastrointestinal system with a high degree of malignancy. Despite extensive research, the underlying mechanisms of its recurrence and metastasis remain elusive.Rho GTPase activating protein 4 (ARHGAP4), a member of the small GTPases protein family, may be closely related to tumor metastasis, and its expression is increased in colon cancer. However, the role of ARHGAP4 in colon cancer metastasis is uncertain. This study investigates the impact of ARHGAP4 on the metastasis of colon cancer cells. Our objective is to determine the role of ARHGAP4 in regulating the invasive behavior of colon cancer cells. We downloaded colon adenocarcinoma (COAD) data from the Cancer Genome Atlas (TCGA), and performed differential analysis and survival analysis. By using the CIBERSORT algorithm, we evaluated the proportion of infiltrating immune cells in colon cancer. We further analyzed whether ARHGAP4 is associated with T cell exhaustion. Finally, we investigated the impact of ARHGAP4 knockdown on the migration and invasion of colon cancer cells through in vitro cell experiments. Additionally, we utilized western blotting to assess the expression of protein related to the TGF-β signaling pathway and epithelial-mesenchymal transition (EMT). We found that ARHGAP4 is upregulated in colon cancer. Subsequent survival analysis revealed that the high-expression group had significantly lower survival rates compared to the low-expression group. Immune infiltration analysis showed that ARHGAP4 was not only positively correlated with CD8+ T cells, but also positively correlated with T cell exhaustion markers programmed cell death 1 (PDCD-1), cytotoxic T-lymphocyte associated protein 4 (CTLA-4), and lymphocyte activating 3 (LAG-3). In vitro cell experiments, the knockdown of ARHGAP4 inhibited the migration and invasion of colon cancer cells. Among EMT-related proteins, when ARHGAP4 was knocked down, the expression of E-cadherin was increased, while the expression of N-cadherin and Vimentin was decreased. Meanwhile, the expression of TGF-β1, p -Smad2, and p -Smad3, which are associated with the TGF-β/Smad pathway, all decreased. ARHGAP4 promotes colon cancer metastasis through the TGF-β/Smad signaling pathway and may be associated with T cell exhaustion. It plays an important role in the progression of colon cancer and may serve as a potential target for diagnosis and treatment of colon cancer. • The knockdown of ARHGAP4 inhibited the migration and invasion of colon cancer cells. • Knockdown of ARHGAP4 increased the expression of E-cadherin and decreased the expression of N-cadherin and Vimentin. • Knockdown of ARHGAP4 decreased the expression of TGF-β1, p -Smad2 and p -Smad3. • ARHGAP4 may be associated with T cell exhaustion. • ARHGAP4 promotes colon cancer metastasis through the TGF/β signaling pathway. [ABSTRACT FROM AUTHOR]