Colon Cancer

Unveiling the power of TIIC: A prognostic tool for esophageal adenocarcinoma.

BACKGROUND: Esophageal adenocarcinoma (EAC) remains a lethal malignancy with limited prognostic tools for guiding immunotherapy. Tumor-infiltrating immune cells (TIICs) play a critical role in EAC prognosis and treatment response. METHODS: We integrated single-cell RNA sequencing and bulk transcriptome data from TCGA and GEO databases. TIIC-specific RNAs were identified via tissue specificity inde...

BAASNet: boundary-aware deep learning for accurate polyp segmentation in colonoscopy.

Colorectal polyps are primarily detected through colonoscopy, which plays a central role in early cancer prevention. Precise polyp segmentation supports treatment planning and diagnostic accuracy by providing masks that encode clinically relevant structures. Recent advancements in deep learning have led to several polyp segmentation models. However, performance remains hindered by challenges such ...

Predictive Value of Endoscopy in the Identification of Complete Response After Neoadjuvant Therapy for Rectal Adenocarcinoma.

BACKGROUND: Large-scale evidence on the role of endoscopy alone as the assessment tool to identify complete response after neoadjuvant therapy in loca...

Integrative cfDNA profiling from low-pass whole-genome sequencing enables tissue-of-origin prediction in cancer.

Cancer type classification is challenging due to tumor heterogeneity and undefined tissue of origin (TOO), particularly in cancers of unknown primary ...

Prognostic risk modeling based on integrated multi-omics analysis identifies CRY2 as a key regulator in tumor immunity and patient survival in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) exhibits substantial metabolic heterogeneity. This study developed a robust prognostic signature integrating ferro...

See more, detect more: investigating colonic inspection techniques and polyp detection using machine learning.

BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer mortality despite widespread colonoscopy screening. Colonoscopy effectiveness, which ...

Spatially resolved tissue architecture and computational pathology in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a complex disease characterized by high levels of cellular heterogeneity and pronounced microenvironmental ...

Patient-Level Cross-validated nnU-Net for Multiclass Segmentation of Lung Parenchyma and Solid Adenocarcinoma on Thoracic CT.

Automated segmentation of lung parenchyma and solid lung adenocarcinoma on thoracic computed tomography (CT) is needed for reproducible quantitative i...

Integrative multi-omics reveals a prognostic and therapeutic landscape of synthetic lethality-associated signatures in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) exhibits considerable heterogeneity and therapeutic resistance. Here, we integrated single-cell RNA sequencing, spatial tra...

CHRNA5 orchestrates tumorigenesis and immune invasion through the CaMKII/AKT/NF-κB-CCL20 axis in pancreatic ductal adenocarcinoma.

AIMS: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by poor prognosis, extensive perineural invasion (PNI), ...

Integrating network toxicology, machine learning, and single-cell sequencing systems to analyze autophagy core genes in lung adenocarcinoma.

The heterogeneity and complex tumor microenvironment of lung adenocarcinoma lead to poor prognosis. Autophagy, as a key cellular process, interacts wi...

DAFNet: A multi-modal deep learning model based on whole-lung CT for the automated prediction of the air space spread of lung adenocarcinoma.

Spread through air spaces (STAS) is a characteristic invasive pattern of lung adenocarcinoma (LUAD), which is associated with a high recurrence rate a...

Tertiary Lymphoid Structures as Predictors of Recurrence in Colorectal Cancer: Development and Validation of a Machine Learning-Based Scoring Model.

BACKGROUND: Tertiary lymphoid structures (TLS) are prognostic immune aggregates in the tumor microenvironment, but the value of location-specific TLS ...

Clinical utility beyond detection rates: Interpreting artificial intelligence in FIT-positive colonoscopy.

Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration.

Quantitative systems pharmacology (QSP) models require calibration data from literature, yet manual curation is inconsistently documented and large la...

User perceptions of machine learning models as decision support for colorectal cancer multidisciplinary team conferences (AID-SIM-2): a qualitative simulation study.

BACKGROUND: Multidisciplinary team (MDT) conferences are considered a cornerstone of decision-making in cancer diagnostics and care. However, the curr...

Unraveling the roles of DKK1 and S100P in pancreatic ductal adenocarcinoma: Insights from machine learning, single-cell/spatial transcriptomics, and functional validation.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a 5-year survival rate of only 13%. Despite recent advances in diagnosi...

AI-driven discovery and validation of a GIPC1 PDZ domain inhibitor for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) remains highly aggressive, with a five-year survival rate under 13.3%, due to late diagnosis, rapid progressio...

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (R...

Dynamic evolution of chaperone-mediated autophagy is associated with tumor microenvironment remodeling and prognostic stratification in lung adenocarcinoma: insights from single-cell transcriptomics, ensemble machine learning, and experimental validation.

BACKGROUND: Lung adenocarcinoma (LUAD) shows prognostic heterogeneity, and tumor-node-metastasis (TNM) staging is limited for individualized managemen...