Colon Cancer

A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images.

OBJECTIVE: To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for classifying gastric adenocarcinoma differentiation on whole-slide images (WSIs) without manual annotation.

Differentiating adenocarcinoma and squamous cell carcinoma in lung cancer using semi automated segmentation and radiomics.

INTRODUCTION: Adenocarcinoma (AD) and squamous cell carcinoma (SCC) are frequently observed forms of non-small cell lung cancer (NSCLC), playing a significant role in global cancer mortality. This research categorizes NSCLC subtypes by analyzing image details using computer-assisted semi-automatic segmentation and radiomic features in model development.

The impact of artificial intelligence on the endoscopic assessment of inflammatory bowel disease-related neoplasia.

Inflammatory bowel disease (IBD) is a group of chronic inflammatory conditions of the gastrointestinal tract resulting from an inappropriate immune re...

OncoE25: an AI model for predicting postoperative prognosis in early-onset stage I-III colon and rectal cancer-a population-based study using SEER with dual-center cohort validation.

BACKGROUND: Although CRC incidence is declining overall, early-onset colorectal cancers are increasing. No prognostic models currently exist for predi...

Surveillance and Surgical Salvage Treatment for Endoscopically Removed T1 Colorectal Cancers.

Endoscopic submucosal dissection (ESD) enables en-bloc resection of large lesions more than 20 mm in size. Therefore, the use of ESD has gained broade...

Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study.

BACKGROUND: Colorectal cancer is now the leading cause of cancer-related deaths among young Americans. Accurate early prediction and a thorough unders...

Fast and accurate lung cancer subtype classication and localization based on Intraoperative frozen sections of lung adenocarcinoma.

Current lung cancer diagnostic techniques primarily focus on tissue subtype classification, yet remain inadequate in distinguishing pathological progr...

Association Between Automated Coronary Artery Calcium From Routine Chest Computed Tomography Scans and Cardiovascular Risk in Patients With Colorectal or Gastric Cancer.

BACKGROUND: As cardiovascular disease (CVD) is the leading cause of noncancer mortality in colorectal or gastric cancer patients, it is essential to i...

Computational theranostics strategy for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge in modern medicine, characterized by its insidious progression, early systemic metas...

Multi-class transformer-based segmentation of pancreatic ductal adenocarcinoma and surrounding structures in CT imaging: a multi-center evaluation.

OBJECTIVE: Accurate segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding anatomical structures is critical for diagnosis, treatment...

Deep learning-based quantification of eosinophils and lymphocytes shows complementary prognostic effects in colorectal cancer patients.

The immune microenvironment of colorectal cancer is a major component of the disease and influences not only tumor progression and patient outcome but...

Non-invasive multi-phase CT artificial intelligence for predicting pre-treatment enlarged lymph node status in colorectal cancer: a prospective validation study.

OBJECTIVES: Benign lymph node enlargement can mislead surgeons into overstaging colorectal cancer (CRC), causing unnecessarily extended lymphadenectom...

Deep learning neural network prediction of postoperative complications in patients undergoing laparoscopic right hemicolectomy with or without CME and CVL for colon cancer: insights from SICE (Società Italiana di Chirurgia Endoscopica) CoDIG data.

BACKGROUND: Postoperative complications in colorectal surgery can significantly impact patient outcomes and healthcare costs. Accurate prediction of t...

Copper Metabolism-Related Genes as Biomarkers in Colon Adenoma and Cancer.

PURPOSE: To elucidate the role of copper (Cu) metabolism in the progression of colon adenoma (CA) to colorectal cancer (CRC) and to identify potential...

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.

Emerging evidence has suggested a potential pathological association between early-onset left-sided colorectal cancer (EOLCC) and metabolic syndrome (...

Identification and validation of blood leukocyte DNA methylation biomarkers for early detection of colorectal neoplasm.

BACKGROUND: Identifying high-risk populations for colorectal cancer (CRC) is critical for precise screening. This study aimed to develop a novel risk ...

Neurodegeneration Promotes Tumorigenesis in Colorectal Cancer: Insights From Single-Cell and Spatial Multiomics.

PURPOSE: Colorectal cancer (CRC) ranks third in global incidence and second in mortality, with rates increasing among younger populations. The enteric...

Deep learning model applied to real-time delineation of colorectal polyps.

BACKGROUND: Deep learning models have shown considerable potential to improve diagnostic accuracy across medical fields. Although YOLACT has demonstra...

FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images.

Cancer is among the most dangerous diseases contributing to rising global mortality rates. Lung cancer, particularly adenocarcinoma, is one of the dea...

Targeting INF2 with DiosMetin 7-O-β-D-Glucuronide: a new stratagem for colorectal cancer therapy.

BACKGROUND AND PURPOSE: Colorectal cancer (CRC) is the third most prevalent malignancy in the gastrointestinal tract and the second leading cause of c...