Colon Cancer

Addressing Colonoscopy Burden Due to Artificial Intelligence Devices for Polyp Detection.

Development and Validation of Time-to-Event Machine Learning Models for Predicting Disease-Free Survival in Patients with Locally Advanced Colorectal Cancer: A Multicenter Cohort Study.

BACKGROUND: The postoperative prognosis of locally advanced colorectal cancer (LACRC) exhibits signi...

Skill degradation, false-positive burden, and translational gap of artificial intelligence-assisted colonoscopy.

Structured Integration of an Artificial Intelligence-Based System for the Optical Diagnosis of Colorectal Polyps.

BACKGROUND/AIMS: Recent advances in computer-aided diagnosis (CADx) systems have demonstrated expert...

Clinical Efficacy of Real-Time Artificial Intelligence-Assisted Colonoscopy in Colorectal Polyp Detection: A Prospective Multicenter Randomized Controlled Trial.

BACKGROUND/AIMS: Early detection and removal of colon polyps are critical for preventing colorectal ...

Integrated multi-omics analyses identified the H3K18la-based spatiotemporal characteristics and risk-stratified treatment strategy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, characterized by hig...

White Light, Magnifying Endoscopy, Endocytoscopy, and Artificial Intelligence in Diagnosis of Early Colorectal Cancer: A Comparative Study.

OBJECTIVES: Early detection of colorectal cancer is critical for improving prognosis. However, asses...

Colonoscopy Quality and Strategies for Improvement.

Colonoscopy plays a pivotal role in colorectal cancer (CRC) screening and reduces CRC incidence and ...

Novel growth pattern-specific digital marker of TILs improves stratification of lung adenocarcinoma patients.

Lung adenocarcinoma (LUAD) is one of the most prevalent forms of cancer and continues to be associat...

Targeted inhibition of colorectal cancer by nano-dihydroartemisinin liposomes: Insights into the disruption of ABCG2 and BCL2 leading to induction of oxidative stress and metabolic disorder.

PURPOSE: Colorectal cancer is an aggressive malignancy characterized by significant drug resistance ...

18F-FDG PET-based ensemble deep learning model for the prediction of lymphovascular invasion in colorectal cancer patients.

OBJECTIVE: The aim of this study was to investigate the diagnostic performance of the 2.5-dimensiona...

Polyp image segmentation based on parallel dilated convolution and dual attention mechanisms.

Colorectal cancer is usually caused by malignant transformation of early colon polyps. Early polyps ...

KLF7 as a biomarker for the pre-metastatic state promotes colorectal cancer liver metastasis via TGFβ autocrine signaling.

Although metastasis-initiating cells drive metastasis, only a certain subpopulation of these cells c...

Multiperspective tumor heterogeneity metrics for preoperative prediction of IASLC grading in clinical stage IA lung adenocarcinomas: A multicenter study.

BACKGROUND AND OBJECTIVE: Accurate preoperative prediction of the International Association for the ...

Advanced artificial intelligence combined with SERS platforms for diagnosis and therapeutic effects of cancer in clinical applications.

Surface-Enhanced Raman Spectroscopy (SERS) has become a valuable way to detect small amounts of mole...

Deep learning-assisted metabolic fingerprint profiling based on V-groove and wrinkle-shaped 3D surface-enhanced Raman scattering substrate for early colorectal cancer diagnosis.

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, yet...

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 ...

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 ...

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 a...

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 c...

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 scree...