Colon Cancer

Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment.

INTRODUCTION: Prostate adenocarcinoma (PRAD) is an extremely widespread site of urological malignancy and is the second most common male cancer in the world. Currently, research progress in immunotherapy for prostate treatment is slower compared to other tumours, which is mainly considered to be caused by the low rate of immune response in prostate cancer as a cold tumour. Recent studies have show...

Classification of non-small cell lung cancer by histologic subtype using deep learning in public and private data sets of computed tomography images.

OBJECTIVE: To develop a deep learning system to classify non-small cell lung cancer (NSCLC) by histologic subtype-adenocarcinoma or squamous cell carcinoma (SCC)-from computed tomography (CT) images in which the tumor regions were segmented, comparing our results with those of similar studies conducted in other countries and evaluating the accuracy of automated classification by using data from th...

Integrating next-generation sequencing and artificial intelligence for the identification and validation of pathogenic variants in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is recognized as a multifactorial disease, where both genetic and environmental factors play critical roles in its...

Computer-aided diagnosis for colorectal polyp in comparison with endoscopists: Systematic review and meta-analysis.

OBJECTIVES: Computer-aided diagnosis (CADx) is anticipated to enhance the prediction of colorectal polyp histology. This study aims to compare the dia...

Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease.

INTRODUCTION: Gastrointestinal disorders (GIDs) affect nearly 40% of the global population, with gut microbiome-metabolome interactions playing a cruc...

Novel exosome-associated LncRNA model predicts colorectal cancer prognosis and drug response.

BACKGROUND: Exosomes are extracellular vesicles that carry various biological substances and have potential as functional mediators in cancers. Howeve...

A computed tomography-based radiomics prediction model for BRAF mutation status in colorectal cancer.

PURPOSE: The aim of this study was to develop and validate CT venous phase image-based radiomics to predict BRAF gene mutation status in preoperative ...

Associations between calcium intake and T cell infiltration in colorectal tumours.

Higher T cell infiltration in colorectal tumours has been associated with better prognosis. Evidence indicates that calcium signalling is essential fo...

Tumor budding and poorly differentiated clusters as a biological continuum in colorectal cancer invasion and prognosis.

Tumor budding (TB) and poorly differentiated clusters (PDCs) are features of infiltrative growth patterns and powerful independent prognostic factors ...

Dual nanozyme sensor enables rapid at-home cancer surveillance.

Early detection of cancer is critical for improving patient survival rates, and current in vitro diagnostic methods show great potential, particularly...

Deep learning assisted identification of SCUBE2 and SLC16 A5 combination in RNA-sequencing data as a novel specific potential diagnostic biomarker in prostate cancer.

Despite the extensive use of biomarkers like PSA, AMACR, and PCA3, prostate cancer (PCa) is still a major clinical challenge, demanding the developmen...

Classification of image-enhanced endoscopy in colon tumors.

Colorectal cancer accounts for 10% of global cancer cases in each year, making accurate evaluation and resection crucial. Imaging-enhanced endoscopy h...

Annotation-Free Whole-Slide Image Analysis Method to Assess Immune Infiltration in Colorectal Cancer.

PURPOSE: Tumor-infiltrating lymphocytes (TILs) play a crucial role in host antitumor processes. High level of TILs is associated with better outcomes ...

Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data.

IntroductionEarly diagnosis of colorectal cancer (CRC) poses a significant clinical challenge. This study aims to develop machine learning (ML) models...

A biorefinery approach for the simultaneous obtention of essential oils, organic acids and polyphenols from citrus peels: Phytochemical characterization and bioactive potential.

This investigation evaluates the valorization of citrus peels (lemon, tangerine, and orange) to recover both on-polar and polar fractions simultaneous...

Artificial intelligence and colorectal neoplasia detection performances in patients with positive fecal immunochemical test: Meta-analysis and systematic review.

OBJECTIVES: The combination of fecal immunochemical test (FIT) followed by colonoscopy has established itself as one of the preferred population-based...

Artificial intelligence-based muscle analysis risk assessment of treatment-related toxicity in metastatic colorectal cancer.

IntroductionUp to 60% of adults with metastatic colorectal cancer (mCRC) receiving combination cytotoxic chemotherapy may experience loss of skeletal ...

LTE: Enhancing colorectal cancer patient selection for cytoreductive surgery: The role of artificial intelligence and integration of pathological peritoneal cancer index in improving clinical outcomes.

Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases.

BACKGROUND: To develop a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CECT) to assess the rat sarcoma (RAS) onc...

GastroEndoNet: Comprehensive endoscopy image dataset for GERD and polyp detection.

The gastrointestinal (GI) system is fundamental to human health, supporting digestion, nutrient absorption, and waste elimination. Disruptions in GI f...