Colon Cancer

The interplay between Peptostreptococcus and Fusobacterium as novel signatures in colorectal cancer recurrence.

BACKGROUND: After resection surgery, recurrence occurs in more than 30% of colorectal cancer (CRC) patients. Previous studies have highlighted the role of the gut microbiota in CRC initiation and progression; however, the microbial features associated with recurrence remain not completely understood. In particular, integrated multi-omics biomarkers, the interactions between microorganisms, as well...

Multiview 2.5D Deep Learning Outperforms 2D and 3D Models for Preoperative Prediction of Visceral Pleural Invasion in Stage IA Lung Adenocarcinoma.

OBJECTIVE: This study evaluated the predictive performance of 2 novel 2.5-dimensional (2.5D) deep learning (DL) models for visceral pleural invasion (VPI) in clinical stage IA lung adenocarcinoma, comparing them with traditional 2D and 3D models. MATERIALS AND METHODS: A multicenter retrospective analysis included 804 patients from 2 Chinese hospitals with pathologically confirmed stage IA lung ad...

Machine Learning for Predicting Colorectal Cancer-Specific Mortality: The Role of Socioeconomic Inequalities in Public Policy.

BACKGROUND: Colorectal cancer remains a leading cause of mortality worldwide. We investigated whether adding socioeconomic information to machine lear...

A Machine Learning-Based Clinical Tool for Predicting Inadequate Bowel Preparation: Development and Validation.

OBJECTIVES: Colonoscopy is an essential tool for diagnosing and treating colorectal diseases, with bowel preparation serving as a key determinant of i...

AI-Derived Electronic Tumor Marker For Cancer Antigen 19-9 Nonproducers With Pancreatic Ductal Adenocarcinoma.

IMPORTANCE: Cancer antigen 19-9 (CA19-9) is used to assess treatment response among patients with pancreatic ductal adenocarcinoma (PDAC); however, ne...

Prospective evaluation of artificial intelligence-assisted monitoring of the effective withdrawal time on adenoma detection.

BACKGROUND/AIMS: Standard withdrawal time (SWT) is a recognized colonoscopy quality metric but may not capture mucosal examination quality. We develop...

dMMR prediction from colorectal cancer histopathology: Leveraging non-tumor and low-magnification regions.

BACKGROUND AND OBJECTIVE: Colorectal cancer is the second leading cause of cancer-related mortality worldwide, posing a substantial burden on healthca...

BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines.

BACKGROUND: Cancer heterogeneity results in patients with the same diagnosis responding differently to drugs, making treatments extremely challenging....

Artificial intelligence-assisted polyp detection in colonoscopy - Methodological considerations for annotation standardization.

Concordant and Fluid-Specific Glycosylation Alterations in Serum and Saliva Enable Non-Invasive Diagnosis of Colorectal Cancer.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, highlighting the need for accurate, noninvasive biomarkers. This study in...

Ganglioside-Mediated Neural-Acinar Crosstalk Facilitates Pancreatic Carcinogenesis via Metaplastic Niche Remodeling.

BACKGROUND & AIMS: Neural networks constitute a crucial component of the tumor microenvironment that remains underexplored in pancreatic carcinogenesi...

The first reported case using real-time artificial intelligence with single-port robotics for colorectal surgery.

BACKGROUND: With the increased utilization of artificial intelligence (AI) tools in the operating room, we aimed to integrate real-time visual cues pr...

Robust Histopathology Subtyping via Perturbation Fidelity in Deep Classifier.

Deep learning for invasive lung adenocarcinoma subtyping remains vulnerable to real-world imaging perturbations. We present a margin consistency frame...

Predicting intrahepatic recurrence of colorectal cancer liver metastases after curative hepatectomy using a machine learning model with data integration of ultrasound radiomics and clinicopathological parameters.

OBJECTIVES: To develop and validate a machine learning model integrating ultrasound radiomics and clinicopathological parameters to predict intrahepat...

The economic imperative of artificial intelligence in maternal and neonatal health: a review of evaluation benefits, frameworks, challenges, future perspectives, and limitations.

Integrating artificial intelligence (AI) into maternal and neonatal health (MNH) offers significant opportunities for enhancing patient care through a...

Integrative Approaches in Lung Cancer Diagnosis: Bridging Molecular Biomarkers and AI Driven Imaging.

Though critical, traditional diagnostic approaches such as X-ray, CT scans, bronchoscopy and tissue biopsy don't reliably detect lung cancer at early ...

A machine learning framework predicts oncogenic driver mutations from SNP profiles in lung adenocarcinoma.

Computational Innovations in Cancer Research and How Computing is Transforming Drug Discovery and Development: A Review.

INTRODUCTION: Cancer is a major global health concern, causing millions of deaths each year due to the uncontrolled growth and spread of abnormal cell...

Design of novel PI3Kα and PI3Kγ inhibitors for cancer treatment using pharmacophore, protein-ligand contacts, and machine learning methods.

Cancer is a complex disease characterised by the unregulated growth of abnormal cells. The intracellular signaling pathway, specifically phosphatidyli...

Colonoscopy surveillance in Lynch syndrome: what it prevents and what it does not.

Lynch syndrome (LS), synonymous with hereditary non-polyposis colorectal cancer (HNPCC), is caused by germline pathogenic variants in MLH1, MSH2, MSH6...