Colon Cancer

Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.

Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (scPathClus). scPathClus first transforms the single-cell gene expression matrix into a pathway enrichment matrix and generates its latent feature matr...

CellOMaps: A compact representation for robust classification of lung adenocarcinoma growth patterns.

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease, characterized by five primary histological growth patterns. The classification of such patterns is crucial due to their direct relation to prognosis but the high subjectivity and observer variability pose a major challenge. Related studies in the literature focus on machine learning methods for growth pattern classification, of...

Efficient polyp detection algorithm based on deep learning.

OBJECTIVE: Colon polyp detection is crucial in reducing the incidence of colorectal cancer. However, due to the diverse morphology of colon polyps, th...

Four Different Artificial Intelligence Models Logistic Regression to Enhance the Diagnostic Accuracy of Fecal Immunochemical Test in the Detection of Colorectal Carcinoma in a Screening Setting.

BACKGROUND/AIM: This study aimed to evaluate the diagnostic accuracy (DA) of four artificial intelligence (AI) models compared to logistic regression ...

Boosting polyp screening with improved point-teacher weakly semi-supervised.

Polyps, like a silent time bomb in the gut, are always lurking and can explode into deadly colorectal cancer at any time. Many methods are attempted t...

A machine learning approach to differentiate stage IV from stage I colorectal cancer.

BACKGROUND AND AIM: The stage at which Colorectal cancer (CRC) diagnosed is a crucial prognostic factor. Our study proposed a novel approach to aid in...

Enhanced non-invasive machine learning approach for early colorectal cancer detection: Predictive modeling and validation in a Jordanian cohort.

BACKGROUND: Colorectal cancer (CRC) ranks as the third most prevalent cancer worldwide, posing significant public health challenges. Late-stage detect...

Development and validation of the Open-Source Automatic Bowel Preparation Scale.

BACKGROUND AND AIMS: Insufficient bowel preparation accounts for up to 42% of missed adenomas in colonoscopy. However, major analysis programs found n...

Broadening the Net: Overcoming Challenges and Embracing Novel Technologies in Lung Cancer Screening.

Lung cancer is one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages where curative treatment ...

An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks.

Colorectal cancer (CRC) represents a major global disease burden with nearly 1 million cancer-related deaths annually. TNM staging has served as the f...

Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing.

Colorectal cancer often arises from precancerous polyps, where accurate size assessment is vital for clinical decisions but challenged by subjective m...

A machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma.

Colon adenocarcinoma (COAD) is one of the most prevalent malignancies worldwide and its prognosis is extremely poor. Angiogenesis has been linked to c...

Lightweight Relational Embedding in Task-Interpolated Few-Shot Networks for Enhanced Gastrointestinal Disease Classification

Traditional diagnostic methods like colonoscopy are invasive yet critical tools necessary for accurately diagnosing colorectal cancer (CRC). Detecti...

The role of mitochondrial dysfunction in the pathogenesis of atherosclerosis: A new exploration from bioinformatics analysis.

Atherosclerosis (AS) is a complex cardiovascular disease associated with mitochondrial dysfunction (MD), which contributes to plaque formation and ins...

Identification of key genes regulating colorectal cancer stem cell characteristics by bioinformatics analysis.

Cancer stem cells (CSCs), distinguished by their abilities to differentiate and self-renew, play a pivotal role in the progression of colorectal cance...

Utilizing spectral detector computed tomography quantitative parameters via multimodality tumor tracking to predict occult lymph node metastasis in clinical stage 1 pure solid lung adenocarcinoma.

BACKGROUND: The presence of occult lymph node metastasis (OLNM) has significant implications for the staging, treatment and prognosis of patients with...

Deep learning for fine-grained molecular-based colorectal cancer classification.

BACKGROUND: Colorectal cancer (CRC) is one of the most common malignancies globally and a major cause of cancer-related deaths. In the molecular diagn...

Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans

Lung cancer is the leading cause of cancer mortality worldwide, and non-invasive methods for detecting key mutations and staging are essential for i...

Deep learning radiomics fusion model to predict visceral pleural invasion of clinical stage IA lung adenocarcinoma: a multicenter study.

AIM: To assess the predictive performance, risk stratification capabilities, and auxiliary diagnostic utility of radiomics, deep learning, and fusion ...

Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach.

INTRODUCTION: Cervical cancer, which includes squamous cell carcinoma and adenocarcinoma, is a leading cause of cancer-related deaths globally, partic...