Colon Cancer

Machine learning-based integration develops a lactate metabolism related gene signature for improving outcomes in pancreatic ductal adenocarcinoma.

OBJECTIVES: Given its high global mortality rate, pancreatic ductal adenocarcinoma (PDAC) remains a significant area of investigation. However, a robust gene signature linked to lactate metabolism for PDAC patients has not yet been established. Our objective was therefore to construct a novel lactate metabolism related gene signature (LMRGS) capable of predicting patient outcomes and informing the...

Machine learning in colorectal cancer prediction and diagnosis: a systematic review of models' performance.

INTRODUCTION: Colorectal cancer (CRC) poses a significant global health burden, demanding early and accurate detection strategies. However, Machine Learning (ML) models are increasingly being applied for CRC prediction; yet their performance requires systematic evaluation to guide adoption. PURPOSE: This review evaluates the performance of ML models in predicting and diagnosing CRC, focusing on st...

Machine learning-based prediction of short-term recurrence of colorectal adenomatous polyps following EMR: model development and validation study.

OBJECTIVE: This study aims to develop and validate a model for predicting the 1-year recurrence of adenomatous polyps following endoscopic mucosal res...

Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records.

Colonoscopy screening effectively identifies and removes polyps before they progress to colorectal cancer (CRC), but current follow-up guidelines rely...

Predictive prioritization of enhancers associated with pancreatic disease risk.

Genetic and epigenetic variation in enhancers is associated with disease susceptibility; however, linking enhancers to target genes and predicting enh...

A prospective study evaluating an artificial intelligence-based system for withdrawal time measurement.

Withdrawal time has emerged as a critical quality measure in colonoscopy for colorectal cancer screening. Owing to the high variability in calculating...

Predictive value of artificial intelligence-based quantitative CT feature analysis for diagnosing the pathological types of pulmonary nodules.

OBJECTIVES: Accurate preoperative classification of pulmonary nodules (PNs) is critical for guiding clinical decision-making and preventing overtreatm...

What can we learn about chronic rhinosinusitis through mucus?

PURPOSE OF REVIEW: Sinonasal mucus biomarkers have emerged as a powerful, noninvasive tool to better understand the immunopathology of chronic rhinosi...

Artificial intelligence for colorectal polyp sizing: clinical promise and methodological challenges.

Microbiome-transcriptome-histology triad enhances survival risk stratification in multiple cancers.

Accurate prognostic stratification is essential for optimizing postoperative therapeutic strategies in oncology. While deep learning approaches have s...

Impact of artificial intelligence-assisted colonoscopy on gastroenterology fellow performance: a pragmatic randomized controlled trial.

BACKGROUND AND AIMS: The substantial miss rate during screening and surveillance colonoscopy, particularly for the right side, underscores the need to...

AI for colon cancer: A focus on classification, detection, and predictive modeling.

PURPOSE: Artificial Intelligence (AI) is increasingly recognized for its potential in improving the detection, classification, prediction, and segment...

Radiomics-based artificial intelligence (AI) models in colorectal cancer (CRC) diagnosis, metastasis detection, prognosis, and treatment response prediction.

Colorectal cancer (CRC) is the third most common cause of cancer-related morbidity and mortality in the world. Radiomics and radiogenomics are utilize...

Refining the Classroom: The Self-Supervised Professor Model for Improved Segmentation of Locally Advanced Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related deaths, with accurate staging being critical for treatment planning. Auto...

HAMIL: Hierarchical Attention Multi-Instance Learning for Label-Free Colorectal Cancer Typing.

Colorectal cancer (CRC) is one of the leading gastrointestinal malignancies, underscoring the need for an in-depth analysis of the cellular within the...

ToBaFu: Topology-based fusion model for classification of two-dimensional cancer images.

Medical images play a pivotal role in disease diagnosis. Numerous studies on cancer image analysis focus on end-to-end deep neural networks, neglectin...

Integration of machine learning and SERS technology for precise classification and diagnosis of colon cancer from plasma samples.

Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning offers a transformative label-free approach for colorectal cancer detection,...

Clinical implications of computer-aided real-time size estimation of colorectal polyps during colonoscopy: a prospective study.

Accurate polyp size estimation during colonoscopy is crucial for clinical decision making, follow-up, and implementation of cost-saving strategies. Ob...

Colorectal mucosal exposure area assessment using artificial intelligence: a multicenter prospective observational study.

This study proposed a new quality control indicator for colonoscopy, the cumulative colorectal mucosal exposure area (CCMEA), to assess mucosal exposu...

Identifying meaningful drug response biomarkers from public pharmacogenomic datasets with biologically informed interpretable neural networks.

With the advancements of next-generation sequencing, publicly available pharmacogenomic datasets from cancer cell lines provide a handle for developin...