Colon Cancer

Holistic White-light Polyp Classification via Alignment-free Dense Distillation of Auxiliary Optical Chromoendoscopy

White Light Imaging (WLI) and Narrow Band Imaging (NBI) are the two main colonoscopic modalities for polyp classification. While NBI, as optical chromoendoscopy, offers valuable vascular details, WLI remains the most common and often the only available modality in resource-limited settings. However, WLI-based methods typically underperform, limiting their clinical applicability. Existing approac...

EndoVLA: Dual-Phase Vision-Language-Action Model for Autonomous Tracking in Endoscopy

In endoscopic procedures, autonomous tracking of abnormal regions and following circumferential cutting markers can significantly reduce the cognitive burden on endoscopists. However, conventional model-based pipelines are fragile for each component (e.g., detection, motion planning) requires manual tuning and struggles to incorporate high-level endoscopic intent, leading to poor generalization ...

Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up.

Colorectal cancer remains a major health concern, with colorectal polyps as key precursors. Endoscopic mucosal resection (EMR) is a common treatment, ...

Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer.

BACKGROUND: Esophageal cancer is the sixth most common cancer worldwide, with a high mortality rate. Early prognosis of esophageal abnormalities can i...

AI-Driven Multiscale Study on the Mechanism of Polygonati Rhizoma in Regulating Immune Function in STAD.

Polygonati Rhizoma, a traditional Chinese medicine, has demonstrated immunomodulatory and anticancer properties, yet its precise mechanisms in stomach...

A Digital Score Based on Circulating-Tumor-Cells-Derived mRNA Quantification and Machine Learning for Early Colorectal Cancer Detection.

Circulating tumor cells (CTCs) serve as valuable biomarkers in tumor circulation, carrying essential primary tumor information. The purification of CT...

In silico tool for identification of colorectal cancer from cell-free DNA biomarkers

Colorectal cancer remains a major global health concern, with early detection being pivotal for improving patient outcomes. In this study, we levera...

MIPHEI-ViT: Multiplex Immunofluorescence Prediction from H&E Images using ViT Foundation Models

Histopathological analysis is a cornerstone of cancer diagnosis, with Hematoxylin and Eosin (H&E) staining routinely acquired for every patient to v...

Clinical Implications of The Cancer Genome Atlas Molecular Classification System in Esophagogastric Cancer.

PURPOSE: The Cancer Genome Atlas (TCGA) project defined four distinct molecular subtypes of esophagogastric adenocarcinoma: microsatellite instable (M...

Understanding Stain Separation Improves Cross-Scanner Adenocarcinoma Segmentation with Joint Multi-Task Learning.

Digital pathology has made significant advances in tumor diagnosis and segmentation; however, image variability resulting from tissue preparation and ...

Short Tandem Repeat (STR) Somatic Mutation in Non-Melanoma Skin Cancer (NMSC): Association with Transcriptomic Profile and Potential Implications for Therapy.

BACKGROUND: Studies on somatic mutations in cancer typically report single-nucleotide variants in coding regions, while mutations in short tandem repe...

Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization.

BACKGROUND: Colorectal cancer (CRC) is the third-most prevalent cancer and the cancer with the second-highest mortality rate worldwide, representing a...

Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer-related death globally, with the tumor immune microenvironment (TIME) influencing pro...

Longitudinal changes in body composition during palliative systemic chemotherapy and survival outcomes in metastatic colorectal cancer.

BACKGROUND: In patients with metastatic colorectal cancer, chemotherapy may lead to changes in body composition, including skeletal muscle quantity an...

Using Machine Learning for the Fusion of Tumor Records on a Real-World Dataset.

Cancer registries collect multiple reports describing the same tumor, potentially leading to duplicate or conflicting values across different records....

Endo-CLIP: Progressive Self-Supervised Pre-training on Raw Colonoscopy Records

Pre-training on image-text colonoscopy records offers substantial potential for improving endoscopic image analysis, but faces challenges including ...

Detection of Early-Stage Colorectal Cancer Using Cell-Free oncRNA Biomarkers and Artificial Intelligence.

PURPOSE: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide, and early detection significantly improves treatment ...

Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models.

Malignant pleural effusions (MPEs) are common in advanced lung cancer patients. Cytological examination of pleural fluid is essential for identifying...

Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up.

Given the growing burden of colorectal cancer (CRC) as a global health challenge, it becomes imperative to focus on strategies that can mitigate its i...

Integrating Bulk and Single-Cell Transcriptomics with Machine Learning Reveals a Heme Metabolism-Based Panel for Lung Adenocarcinoma Chemotherapy Resistance.

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related mortality, with heme metabolism playing a critical role in tumor progression and treat...