Colon Cancer

Comparative study of robotic-assisted vs. laparoscopic surgery for colorectal cancer: a single-center experience.

BACKGROUND: Colorectal cancer (CRC) surgeries are commonly performed using either robotic-assisted colorectal surgery (RACS) or laparoscopic colorectal surgery (LCS). This study aimed to compare clinical and surgical outcomes between RACS and LCS for CRC patients.

Editorial: Modelling esophageal adenocarcinoma.

Artificial intelligence-enhanced navigation for nerve recognition and surgical education in laparoscopic colorectal surgery.

BACKGROUND: Devices that help educate young doctors and enable safe, minimally invasive surgery are needed. Eureka is a surgical artificial intelligen...

Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features.

BACKGROUND: First-line treatment for advanced gastric adenocarcinoma (GAC) with human epidermal growth factor receptor 2 (HER2) is trastuzumab combine...

Interaction of human gut microbiota and local immune system in progression of colorectal adenoma (MIMICA-1): a protocol for a prospective, observational cohort study.

INTRODUCTION: The current understanding of colorectal carcinogenesis is based on the adenoma-carcinoma sequence, where genetics, intestinal microbiota...

A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx.

"PolynetDWTCADx" is a sophisticated hybrid model that was developed to identify and distinguish colorectal cancer. In this study, the CKHK-22 dataset,...

Integrating artificial intelligence with endoscopic ultrasound in the early detection of bilio-pancreatic lesions: Current advances and future prospects.

The integration of Artificial Intelligence (AI) in endoscopic ultrasound (EUS) represents a transformative advancement in the early detection and mana...

Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology.

Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information prese...

The combined evaluation of preoperative serum CEA and postoperative tissue CEA as a prognostic factor in stages 0-IV colorectal cancer: a retrospective cohort study.

BACKGROUND: The roles of preoperative serum carcinoembryonic antigen (sCEA) and postoperative tissue carcinoembryonic antigen (tCEA) have been extensi...

The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.

Colorectal cancer (CRC) is a form of cancer that impacts both the rectum and colon. Typically, it begins with a small abnormal growth known as a polyp...

MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation.

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) ...

AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the United States, largely due to its poor five-year su...

Interpretable Deep-Learning p Prediction for Small Molecule Drugs via Atomic Sensitivity Analysis.

Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociati...

[Telemedicine and AI-supported diagnostics in the daily routine of visceral medicine].

Advances in telemedicine, exemplified by augmented reality (AR) and virtual reality (VR), are rapidly progressing. For instance, AR available over lon...

Analysis of diagnostic genes and molecular mechanisms of Crohn's disease and colon cancer based on machine learning algorithms.

Crohn's disease (CD) is a chronic inflammatory bowel condition, and colon adenocarcinoma (COAD), as one of the most prevalent malignant tumors of the ...

Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT.

Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significa...

Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients.

INTRODUCTION: Pancreaticoduodenectomy (PD) for patients with pancreatic ductal adenocarcinoma (PDAC) is associated with a high risk of postoperative c...

Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis.

RATIONALE AND OBJECTIVES: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledg...

Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach.

Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnos...

Current Status of Artificial Intelligence Use in Colonoscopy.

BACKGROUND: Artificial intelligence (AI) has significantly impacted medical imaging, particularly in gastrointestinal endoscopy. Computer-aided detect...