Colon Cancer

Multi-omics machine learning-driven investigation of super-enhancers signatures and prognostic biomarkers in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological subtype of malignant lung tumors, characterized by high incidence and mortality rates. Super-enhancers (SEs) are involved in regulating tumor transcription and promote tumorigenesis and progression. However, their role in LUAD remains underexplored. This study uses bulk, single-cell, and spatial transcriptomics analyses to uncover their tu...

Machine learning-based prognostic model and single-cell transcriptomic integration for identifying brain metastasis-associated malignant subpopulations and potential therapeutic targets in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer. Brain metastasis (BrM) is a major cause of mortality in patients with LUAD. The tumor microenvironment (TME) of LUAD BrM remains incompletely characterized, and there is an urgent need to identify biologically relevant biomarkers and potential therapeutic vulnerabilities. METHODS: Single-cell transcriptomi...

Label-Free Prediction of EGFR Mutation Status Using Fluorescence Lifetime Imaging and Deep Learning in Lung Adenocarcinoma.

UNLABELLED: Accurate prediction of epidermal growth factor receptor (EGFR) mutations is essential for guiding targeted therapy in non-small cell lung ...

Pharmacogenomics in oncology: mutation-targeted therapy and biomarker integration in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most common cancer-related cause of death among all countries globally, mostly because of late diagnosis, he...

The slope of enlightenment: Towards a more realistic understanding of the strengths and weaknesses of artificial intelligence for colorectal polyp assessment.

Artificial intelligence (AI) has rapidly evolved into a transformative adjunct to gastrointestinal (GI) endoscopy, particularly through deep-learning-...

Novel application of attention-enhanced hybrid efficient net for multi-scale histopathological cancer classification.

Examination of high-resolution whole-slide images requires an analysis of the histopathological images, which is essential in the precise diagnosis of...

Artificial intelligence with mucosal exposure devices versus artificial intelligence alone in detection of colorectal adenomas: a systematic review and meta-analysis.

BACKGROUND AND AIMS: Both artificial intelligence (AI) and mucosal exposure devices (MEDs) have been shown to improve adenoma detection rate and reduc...

Machine learning and multi-omic empowered risk stratification and therapeutic framework targeting nucleotide metabolism and mast cell for stomach adenocarcinoma patients.

BACKGROUND: Mast cell and nucleotide metabolism(NM) encodes the Stomach adenocarcinoma (STAD) progression and tumor immune microenvironment(TME) heter...

Specimen quality shapes the actionable genomic landscape in comprehensive cancer genomic profiling.

Comprehensive genomic profiling (CGP) is widely used to identify actionable alterations and guide precision oncology, yet only a minority of tested pa...

Adaptive graph learning of microbial phylogeny enables accurate and interpretable microbiome-based host phenotype prediction.

The human microbiome is inherently structured by phylogeny, yet most predictive models treat microbial taxa as independent features, thereby underusin...

Machine learning integration of tissue-specific metagenomic signatures for colorectal cancer diagnosis.

Colorectal cancer (CRC) represents a significant global health burden. Leveraging machine learning (ML) with metagenomic and tissue-specific data pres...

Promise and pragmatism of AI in global-scale digital pathology: pan-cancer approaches for clinical practice.

Artificial intelligence (AI) tools for digital pathology have crossed the threshold from pilot project to clinical deployment, with regulatory approva...

Uncovering the mechanism of DCLK3 in colorectal cancer progression through WGCNA and machine learning.

BACKGROUND: Colorectal cancer (CRC) is a prevalent malignant tumor of the digestive tract with high morbidity and mortality rates. Although doublecort...

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagn...

Procedure-specific prediction of surgical difficulty in laparoscopic and robotic right hemicolectomy: Interpretable machine learning models.

BACKGROUND: Laparoscopic (L-RHC) and robotic (R-RHC) right hemicolectomy are standard treatments for colon cancer, but procedure-specific prediction o...

Unveiling Large-Scale Kinase-Centric Protein-Protein Interactions through a Knowledge-Informed Workflow.

Protein phosphorylation regulates signaling, yet atomic-level substrate specificity remains elusive due to sparse structural data and phosphorylation-...

AI-Optimized 3D-Printed Dual-Layer Implant: Resolving Chemo-Immunotherapy Sequential Administration Dilemma for Colorectal Cancer Peritoneal Metastasis.

The synergistic integration of chemotherapy and immunotherapy represents the most promising strategy for enhancing therapeutic efficacy in cancer trea...

Colorectal Cancer Screening in Hereditary and Familial High-Risk Populations: Best Practices and Future Directions.

Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide yet is largely preventable through effective scree...

Evaluating human-AI interaction in optical diagnosis of early colorectal carcinoma through endoscopist responses to simulated artificial intelligence: a multinational online study.

Background Suboptimal human - artificial intelligence (AI) interaction is a potential roadblock for implementation of AI in clinical practice. We aime...

Spiky Magnetic Titania Particles for Integrated Exosome Capture and Metabolic Profiling Toward Cancer Diagnosis.

Exosomal metabolite profiling represents a promising non-invasive approach for cancer diagnosis. However, its widespread application has been constrai...