Colon Cancer

Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning.

Baseline genomic data have not demonstrated significant value for predicting the response duration to MAPK inhibitors (MAPKi) in patients with advanced BRAF-mutated melanoma. We used machine learning algorithms and pre-processed genomic data to test whether they could contain useful information to improve the progression-free survival (PFS) prediction. This exploratory analysis compared the predic...

Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.

Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic tools for patients with cancer. Despite their advantages, their clinical utility in real-world settings remains unproven. Assessing EGFR mutations in lung adenocarcinoma demands rapid, accurate and cost-effective tests that preserve tissue for genomic ...

Exploring entropy measures with topological indices on colorectal cancer drugs using curvilinear regression analysis and machine learning approaches.

A topological index is a numerical value derived from the structure of a molecule or graph that provides useful information about the molecule's physi...

Identification and predictive machine learning models construction of gut microbiota associated with lymph node metastasis in colorectal cancer.

This study focuses on the significant role between gut microbiota and lymph node metastasis (LNM) in colorectal cancer (CRC). By conducting 16S rRNA s...

Machine learning assists regulated cell death crucial biomarker selection in adenocarcinoma of the lung: biological data testing and cell assay determination.

BACKGROUND: Lung cancer is a highly aggressive and lethal cancer requiring prognostic and predictive biomarkers for improving patient outcomes. Here, ...

Evaluating cell-free DNA integrity index as a non-invasive biomarker for neoadjuvant chemotherapy in colorectal cancer patients.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is gaining attention as a treatment for advanced colorectal cancer owing to its potential to improve surgic...

AQP9 and IFITM1 as drivers of immune infiltration and tumor progression in IBD-associated colorectal cancer: from computational insights to experimental validation.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract associated with an increased risk of colorectal canc...

Machine learning models for predicting chemotherapy-induced adverse drug reactions in colorectal cancer patients.

BACKGROUND: Chemotherapy-induced adverse drug reactions (ADRs) are common in patients with colorectal cancer. We developed four machine learning model...

A Model Predicting Artificial Intelligence Use by Gastroenterology Nurses in Clinical Practice: A Cross-Sectional Multicenter Survey.

BACKGROUND AND AIMS: Nurses' participation during colonoscopy has been demonstrated to significantly improve the detection rate of polyps and adenomas...

Exploring the impact of neutrophils on lung adenocarcinoma using Mendelian randomization and transcriptomic study.

Tumor immune microenvironment plays a crucial role in determining the prognosis of lung adenocarcinoma (LUAD), with the interaction of immune cells wi...

Folate metabolism-associated CYP26A1 is a clinico-immune target in colorectal cancer.

Folic acid plays a crucial role in cellular regulation and metabolism, commonly included in dietary supplements. Despite this, its involvement in colo...

Comparative analysis of artificial intelligence tools for the dissemination of colorectal cancer screening guidelines: a novel perspective on early screening education.

This study systematically evaluated the effectiveness of three artificial intelligence (AI) tools-ChatGPT-4o, Claude3.5, and DeepSeek-in disseminating...

Diagnostic performance of artificial intelligence based on contrast-enhanced computed tomography in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis.

PURPOSE: This meta-analysis systematically evaluated the diagnostic performance of artificial intelligence (AI) based on contrast-enhanced computed to...

Development of a tongue image-based machine learning tool for the diagnosis of colorectal cancer: a prospective multicentre clinical cohort study.

Colorectal cancer (CRC) remains a persistent major global health burden, with traditional diagnostic methods like colonoscopy suffering from suboptima...

Cohesive data analysis for the identification of prognostic hub genes and significant pathways associated with HER2 + and TN breast cancer types.

Breast cancer is the most prevalent and lethal form of cancer being the utmost common medical concern of women. Breast cancer etiology implicates nume...

Multi-scale fusion semantic enhancement network for medical image segmentation.

The application of sophisticated computer vision techniques for medical image segmentation (MIS) plays a vital role in clinical diagnosis and treatmen...

Identification of exosome-related genes in NSCLC via integrated bioinformatics and machine learning analysis.

Exosomes are crucial in the development of non-small cell lung cancer (NSCLC), yet exosome-associated genes in NSCLC remain insufficiently explored. T...

Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation.

Lung adenocarcinoma (LUAD) is a major challenge in oncology due to its complex molecular structure and generally poor prognosis. The aim of this study...

Developing transferable and universal IR biomarkers for intraoperative colorectal cancer diagnosis via FTIR spectroscopy.

Histological staining has long been the gold standard for cancer detection, but it is limited by subjectivity and delayed results. Fourier Transform I...

Artificial Intelligence-Powered Insights into Polyclonality and Tumor Evolution.

Recent studies have revealed that polyclonality-where multiple distinct subclones cooperate during early tumor development-is a critical feature of tu...