Colon Cancer

Deep learning-assisted colonoscopy images for prediction of mismatch repair deficiency in colorectal cancer.

BACKGROUND: Deficient mismatch repair or microsatellite instability is a major predictive biomarker for the efficacy of immune checkpoint inhibitors of colorectal cancer. However, routine testing has not been uniformly implemented due to cost and resource constraints.

State-of-the-art performance of deep learning methods for pre-operative radiologic staging of colorectal cancer lymph node metastasis: a scoping review.

OBJECTIVES: To assess the current state-of-the-art in deep learning methods applied to pre-operative radiologic staging of colorectal cancer lymph node metastasis. Specifically, by evaluating the data, methodology and validation of existing work, as well as the current use of explainable AI in this fast-moving domain.

Accurate prediction of colorectal cancer diagnosis using machine learning based on immunohistochemistry pathological images.

Colorectal cancer (CRC) ranks as the third most prevalent tumor and the second leading cause of mortality. Early and accurate diagnosis holds signific...

SSL-CPCD: Self-Supervised Learning With Composite Pretext-Class Discrimination for Improved Generalisability in Endoscopic Image Analysis.

Data-driven methods have shown tremendous progress in medical image analysis. In this context, deep learning-based supervised methods are widely popul...

Surgical Insight-guided Deep Learning for Colorectal Lesion Management.

BACKGROUND: Colonoscopy stands as a pivotal diagnostic tool in identifying gastrointestinal diseases, including potentially malignant tumors. The proc...

Development and Validation of Machine Learning Model Platelet Index-based Predictor for Colorectal Cancer Stage.

INTRODUCTION: Colorectal cancer (CRC) staging is essential for effective treatment planning and prognosis. While platelet indices have shown promise i...

Computed tomography-based fully automated artificial intelligence model to predict extrapancreatic perineural invasion in pancreatic ductal adenocarcinoma.

BACKGROUND: Extrapancreatic perineural invasion (EPNI) increases the risk of postoperative recurrence in pancreatic ductal adenocarcinoma (PDAC). This...

"Artificial histology" in colonic Neoplasia: A critical approach.

BACKGROUND: The histological assessment of colorectal precancer and cancer lesions is challenging and primarily impacts the clinical strategies of sec...

Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma.

Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), highlighting the need for alternative therapies. Th...

Cost-Effectiveness for Artificial Intelligence in Colonoscopy.

Artificial intelligence (AI) is set to transform the field of colonoscopy through the implementation of computer-assisted detection and diagnosis. Whi...

Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.

BACKGROUND: Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-b...

Identification of biomarkers for the diagnosis in colorectal polyps and metabolic dysfunction-associated steatohepatitis (MASH) by bioinformatics analysis and machine learning.

Colorectal polyps are precursors of colorectal cancer. Metabolic dysfunction associated steatohepatitis (MASH) is one of metabolic dysfunction associa...

GFPrint™: A machine learning tool for transforming genetic data into clinical insights.

The increasing availability of massive genetic sequencing data in the clinical setting has triggered the need for appropriate tools to help fully expl...

Creating a standardized tool for the evaluation and comparison of artificial intelligence-based computer-aided detection programs in colonoscopy: a modified Delphi approach.

BACKGROUND AND AIMS: Multiple computer-aided detection (CADe) software programs have now achieved regulatory approval in the United States, Europe, an...

Role of Artificial Intelligence for Colon Polyp Detection and Diagnosis and Colon Cancer.

The broad use of artificial intelligence (AI) and its various applications have already shown significant impact in medicine and in everyday life. In ...

A CT-based deep learning for segmenting tumors and predicting microsatellite instability in patients with colorectal cancers: a multicenter cohort study.

PURPOSE: To develop and validate deep learning (DL) models using preoperative contrast-enhanced CT images for tumor auto-segmentation and microsatelli...

Machine Learning Integration with Single-Cell Transcriptome Sequencing Datasets Reveals the Impact of Tumor-Associated Neutrophils on the Immune Microenvironment and Immunotherapy Outcomes in Gastric Cancer.

The characteristics of neutrophils play a crucial role in defining the tumor inflammatory environment. However, the function of tumor-associated neutr...

CLMS: Bridging domain gaps in medical imaging segmentation with source-free continual learning for robust knowledge transfer and adaptation.

Deep learning shows promise for medical image segmentation but suffers performance declines when applied to diverse healthcare sites due to data discr...

MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation.

Accurate polyp image segmentation is of great significance, because it can help in the detection of polyps. Convolutional neural network (CNN) is a co...

Predictive Factors Driving Positive Awake Test in Carotid Endarterectomy Using Machine Learning.

BACKGROUND: Positive neurologic awake testing during the carotid cross-clamping may be present in around 8% of patients undergoing carotid endarterect...