Colon Cancer

Integrating spatial lymph node patterns and multimodal clinicopathological features to predict post-neoadjuvant recurrence in gastric adenocarcinoma: a machine learning nomogram.

Combining AI to reveal CCDC3-mediated pathways of colorectal cancer liver metastasis.

In colorectal cancer liver metastases, chromosomal instability (CIN) serves as a critical hallmark linked to tumor aggressiveness and poor prognosis. This study integrated single-cell RNA sequencing, weighted gene co-expression network analysis, and non-negative matrix factorization to construct a comprehensive CIN index, revealing that CIN-high tumor cells exhibit more aggressive phenotypes and r...

Artificial intelligence for accurate tumor size assessment and non-invasive adenocarcinoma prediction in small-sized lung cancer.

INTRODUCTION: Accurate preoperative imaging is essential for improving the treatment of small lung cancers. Precise identification of non-invasive ade...

Nitenpyram promotes breast cancer progression via PARP1: Integration of multi-omics analyses with experimental validation.

Nitenpyram (NIT) is an insecticide used primarily for flea control in pets, especially cats and dogs. Some studies suggest that NIT is associated with...

Integration of radiomics, deep learning, transcriptomics, and metabolomics reveals prognostic risk stratification and underlying biological mechanisms in colorectal cancer.

Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related death worldwide, yet current prognostic str...

A confidence-based, artificial intelligence pathology model for diagnosis of intrahepatic cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICCA) is a rare but highly lethal adenocarcinoma arising within the hepatic parenchyma. Diagnosis present...

CLWD: a Chinese histopathology dataset for lung adenocarcinoma subtype classification.

Effective diagnosis and treatment of lung adenocarcinoma depends on accurate typing, subtyping, and grading. Herein, we present the CLWD dataset, a va...

An advanced diagnostic framework for discriminating lung cancer tissue subtypes via the synergy of fourier transform infrared spectroscopy and random forest.

Accurate subtyping of lung cancer is essential for improving patient prognosis and enabling personalized treatment. However, current clinical techniqu...

Ensemble learning on serum metabolic fingerprints for early detection of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality worldwide, highlighting the urgent need for non-invasive strategies for...

Integrating Artificial Intelligence into Endoscopy Training.

As artificial intelligence (AI) becomes integrated into gastrointestinal endoscopy, training programs must adapt to prepare learners for AI-assisted p...

A Multiomic Liquid Biopsy for the Earlier Detection of Colorectal Cancer.

UNLABELLED: Timely diagnosis and intervention in colorectal cancer are critical to improving patient outcomes and limiting disease progression. Screen...

GSDME-mediated pyroptosis modulates the immunosuppressive microenvironment in lung adenocarcinoma.

Despite the increasing recognition of pyroptosis, particularly that involving GSDME, its precise impact on tumor prognosis and the immune microenviron...

Digital Microscopy in the Endoscopy Suite: A Reference Standard for Colorectal Polyp Size Measurement.

BACKGROUND AND STUDY AIMS: Accurate determination of colorectal polyp size is critical for surveillance recommendations and for generating reliable gr...

Machine learning models based on tumoral, 4 mm-peritumoral, and combined radiomic features for evaluating the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules.

18F-FDG PET/CT and receptor-positive circulating tumor cells-based machine learning model for predicting poorly differentiated lung adenocarcinoma.

INTRODUCTION: This study aimed to develop and validate a machine learning model that integrates radiomic features from 2-[18F]fluoro-2-deoxy-D-glucose...

Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types.

BACKGROUND: Pathological reports provide comprehensive insights into the clinical and pathological features of different cancer types. However, extrac...

A Deep Learning Framework Integrating Tumor Microenvironmental Features Accurately Predicts Multiple Driver Gene Mutations in Lung Cancer Pathology Images.

UNLABELLED: Deep learning (DL) has the potential to enable the prediction of gene mutations directly from routine histopathology slides in lung cancer...

Integration of Short- and Long-Read RNA Sequencing Enables the Discovery of Circular RNAs.

UNLABELLED: Circular RNAs (circRNA) are associated with crucial hallmarks of tumorigenesis. Select circRNAs contain circular open reading frames (cORF...

A CT radiomics signature enables risk stratification and survival prediction in colorectal liver metastases.

Colorectal liver metastases (CRLM) represent a major clinical challenge because outcomes after hepatic resection vary widely between patients. Preoper...

Preoperative prediction of metachronous liver metastasis in colorectal cancer using a deep learning-based radiomics model with automatic segmentation: a multicenter study.

OBJECTIVES: To develop and validate an integrated clinical-radiomics nomogram predicting the risk of metachronous liver metastasis (MLM) in patients w...