AIMC Topic: Colorectal Neoplasms
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A novel approach to overcome black box of AI for optical diagnosis in colonoscopy.
Scientific reports
Jul 1, 2025
Accurate real-time optical diagnosis that distinguishes neoplastic from non-neoplastic colorectal lesions during colonoscopy can lower the costs of pathological assessments, prevent unnecessary polypectomies, and help avoid adverse events. Using a mu...
Taking the Guess Work Out of Endoscopic Polyp Measurement: From Traditional Methods to AI.
Journal of clinical gastroenterology
Jul 1, 2025
Colonoscopy is a crucial tool for evaluating lower gastrointestinal disease, monitoring high-risk patients for colorectal neoplasia, and screening for colorectal cancer. In the United States, over 14 million colonoscopies are performed annually, with...
The implementation of computer-aided detection in an initial endoscopy training improves the quality measures of trainees' future colonoscopies: a retrospective cohort study.
Surgical endoscopy
Jun 30, 2025
INTRODUCTION: The implementation of computer-aided detection (CADe) systems has resulted in a growing number of young endoscopists being trained using AI-enhanced devices. The potential impact of AI-enhanced training on the trainees' future performan...
Using a Multilingual AI Care Agent to Reduce Disparities in Colorectal Cancer Screening for Higher Fecal Immunochemical Test Adoption Among Spanish-Speaking Patients: Retrospective Analysis.
Journal of medical Internet research
Jun 25, 2025
BACKGROUND: Colorectal cancer (CRC) screening rates remain disproportionately low among Hispanic and Latino populations compared to non-Hispanic White populations. While artificial intelligence (AI) shows promise in health care delivery, concerns exi...
Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study.
JMIR cancer
Jun 19, 2025
BACKGROUND: Colorectal cancer is now the leading cause of cancer-related deaths among young Americans. Accurate early prediction and a thorough understanding of the risk factors for early-onset colorectal cancer (EOCRC) are vital for effective preven...
Integrated muti-omics data and machine learning reveal CD151 as a key biomarker inducing chemoresistance in metabolic syndrome-related early-onset left-sided colorectal cancer.
Functional & integrative genomics
Jun 9, 2025
Emerging evidence has suggested a potential pathological association between early-onset left-sided colorectal cancer (EOLCC) and metabolic syndrome (MetS). However, the underlying genetic and molecular mechanisms remain insufficiently elucidated. Th...
Neurodegeneration Promotes Tumorigenesis in Colorectal Cancer: Insights From Single-Cell and Spatial Multiomics.
JCO precision oncology
Jun 6, 2025
PURPOSE: Colorectal cancer (CRC) ranks third in global incidence and second in mortality, with rates increasing among younger populations. The enteric nervous system (ENS) is crucial for gastrointestinal function, and its dysfunction is associated wi...
Targeting INF2 with DiosMetin 7-O-β-D-Glucuronide: a new stratagem for colorectal cancer therapy.
BMC cancer
Jun 1, 2025
BACKGROUND AND PURPOSE: Colorectal cancer (CRC) is the third most prevalent malignancy in the gastrointestinal tract and the second leading cause of cancer-related deaths. Despite the identification of numerous biomarkers, their non-specific distribu...
Development and Validation of a Lifestyle-Based 10-Year Risk Prediction Model of Colorectal Cancer for Early Stratification: Evidence from a Longitudinal Screening Cohort in China.
Nutrients
May 31, 2025
Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with growing evidence linking risk to lifestyle and dietary factors. However, nutrition-related exposures have rarely been integrated into existing CRC ...
Dual-energy CT combined with histogram parameters in the assessment of perineural invasion in colorectal cancer.
International journal of colorectal disease
May 27, 2025
PURPOSE: The purpose is to evaluate the predictive value of dual-energy CT (DECT) combined with histogram parameters and a clinical prediction model for perineural invasion (PNI) in colorectal cancer (CRC).
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