Colon Cancer

Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the disease. While some approaches investigating the role of multi-omics in PDAC are emerging, the analysis of methylation data is under exploited.

Computationally Enabled Polychromatic Polarized Imaging Enables Mapping of Matrix Architectures that Promote Pancreatic Ductal Adenocarcinoma Dissemination.

Pancreatic ductal adenocarcinoma (PDA) is a highly metastatic and lethal disease. In PDA, extracellular matrix (ECM) architectures, known as tumor-associated collagen signatures (TACSs), regulate invasion and metastatic spread in both early dissemination and late-stage disease. As such, TACS has been suggested as a biomarker to aid in pathologic assessment. However, despite its significance, appro...

A literature review on the role of artificial intelligence-based chatbots in patient education in colorectal surgery.

INTRODUCTION: Artificial intelligence-based chatbots are becoming increasingly used in patient education, in the realm of colorectal diseases. Perhaps...

Generating research hypotheses to overcome key challenges in the early diagnosis of colorectal cancer - Future application of AI.

We intend to explore the capability of ChatGPT 4.0 in generating innovative research hypotheses to address key challenges in the early diagnosis of co...

[Artificial intelligence empowers functional preservation and safety guarantee in laparoscopic colorectal surgery].

Transabdominal and transanal endoscopic approaches have become mainstream in colorectal surgery. With the substantial improvement in survival outcomes...

Simple is what you need for efficient and accurate medical image segmentation

While modern segmentation models often prioritize performance over practicality, we advocate a design philosophy prioritizing simplicity and efficie...

Characterization of m6A-Related Genes in Tumor-Associated Macrophages for Prognosis, Immunotherapy, and Drug Prediction in Lung Adenocarcinomas Based on Machine Learning Algorithms.

Tumor-associated macrophages (TAMs) are a vital immune component within the tumor microenvironment (TME) of lung adenocarcinoma (LUAD), exerting signi...

Predicting Early-Onset Colorectal Cancer with Large Language Models

The incidence rate of early-onset colorectal cancer (EoCRC, age < 45) has increased every year, but this population is younger than the recommended ...

Multimarker Assessment of B-Cell and Plasma Cell Subsets and Their Prognostic Role in the Colorectal Cancer Microenvironment.

PURPOSE: Although the association between cytotoxic T lymphocytes and favorable prognosis in colorectal cancer is well established, the prognostic sig...

MoXGATE: Modality-aware cross-attention for multi-omic gastrointestinal cancer sub-type classification

Cancer subtype classification is crucial for personalized treatment and prognostic assessment. However, effectively integrating multi-omic data rema...

Single GPU Task Adaptation of Pathology Foundation Models for Whole Slide Image Analysis

Pathology foundation models (PFMs) have emerged as powerful tools for analyzing whole slide images (WSIs). However, adapting these pretrained PFMs f...

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework

Although the Segment Anything Model (SAM) is highly effective in natural image segmentation, it requires dependencies on prompts, which limits its a...

Aiding Medical Diagnosis through Image Synthesis and Classification

Medical professionals, especially those in training, often depend on visual reference materials to support an accurate diagnosis and develop pattern...

Enhanced Early Detection of Colorectal Cancer via Blood Biomarker Combinations Identified Through Extracellular Vesicle Isolation and Artificial Intelligence Analysis.

Colorectal cancer (CRC) remains a major cause of cancer-related deaths worldwide, with early detection being crucial for improving survival rates. Des...

Optimizing Pix2Pix GAN With Attention Mechanisms for AI-Driven Polyp Segmentation in IoMT-Enabled Smart Healthcare.

This paper introduces an innovative approach for automated polyp segmentation in colonoscopy images, deploying an enhanced Pix2Pix Generative Adversar...

Deep learning for liver lesion segmentation and classification on staging CT scans of colorectal cancer patients: a multi-site technical validation study.

AIM: To validate a liver lesion detection and classification model using staging computed tomography (CT) scans of colorectal cancer (CRC) patients.

GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification.

A major challenge in applying deep learning to medical imaging is the paucity of annotated data. This study explores the use of synthetic images for d...

Use of computer-assisted detection (CADe) colonoscopy in colorectal cancer screening and surveillance: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement.

This statement conveys the European Society of Gastrointestinal Endoscopy (ESGE) position on the use of computer-aided detection (CADe) with artificia...

Immunohistochemistry and machine learning study of DNA replication-associated proteins in uterine epithelial tumors and precursor lesions.

Endometrioid adenocarcinoma (EA) has been on the increase in recent years in developed countries. Early detection of endometrioid adenocarcinoma in th...

NLP for computational insights into nutritional impacts on colorectal cancer care.

Colorectal cancer (CRC) is one of the most prominent cancers globally, with its incidence rising among younger adults due to improved screening practi...