Colon Cancer

Automated extraction of colonoscopy quality indicators from free-text reports using a prompt-based large language model pipeline.

BACKGROUND AND AIMS: Artificial intelligence has increasingly enabled large-scale analysis of clinical documentation, offering new opportunities to improve quality monitoring in endoscopy. In colonoscopy, key quality indicators - such as cecal intubation, bowel preparation, and polyp detection rate - are routinely recorded in free-text reports, which limits automated extraction and large-scale aud...

SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell-Cell Interaction Inference.

Spatial transcriptomics (ST) enables the study of tissue architecture by resolving gene expression in space, but current ST platforms are constrained by limited sequencing depth and indirect single-cell identification. Existing deconvolution methods that integrate single-cell RNA sequencing (scRNA-seq) data with ST often overlook the biological principle that cells in communication with each other...

Deep integration of clinical metadata with [18F]FDG PET/CT imaging for histological subtyping in non-small cell lung cancer: a multi-center study.

PURPOSE: To develop and validate a multimodal deep learning framework that integrates clinical metadata with [18F]FDG PET/CT imaging to resolve overla...

Changes in liver resections and associated outcomes over thirty years: a nationwide analysis using a machine learning approach.

BACKGROUND: In contrast to three decades ago, liver resection (LR) is now an increasingly common procedure. This study aims to evaluate changes in the...

Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.

Deep learning can extract predictive and prognostic biomarkers from histopathology whole-slide images. However, explainable artificial intelligence ap...

Real-world impact of artificial intelligence on adenoma detection: A cross-sectional study at a single center in Lima, Peru.

INTRODUCTION: The use of artificial intelligence (AI) in endoscopic studies has grown in recent years. The present study evaluates the performance of ...

Integrative spatiotemporal transcriptomics identifies a liver metastasis-related initial cell population associated with the SEMA3A-NRP1 axis in pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy in which liver metastasis represents the principal determinant of po...

The potential utility of in-silico approach in identifying phytochemicals against various targets for the management of lung cancer.

Research on novel treatment approaches is crucial for lung cancer, because it is one of the most common and aggressive malignancies with high mortalit...

Transcriptomic profiling identifies immunotherapy-responsive phenotypes in microsatellite-stable metastatic colorectal cancer.

Conventional immune checkpoint inhibitors (ICIs) remain largely ineffective in microsatellite-stable metastatic colorectal cancer (MSS mCRC), where lo...

Multi-omics analysis identifies GINS1 as a prognostic biomarker in lung adenocarcinoma, linked to macrophage polarization and tumor cell survival.

This study aimed to identify essential genes driving lung adenocarcinoma (LUAD) progression by integrating CRISPR-Cas9 dependency data from the Cancer...

Spectral CT-based intratumoral and peritumoral radiomics for predicting invasiveness of ground-glass nodules in lung adenocarcinoma.

OBJECTIVES: To evaluate the potential of spectral detector computed tomography (SDCT) combined with intratumoral and peritumoral radiomics for noninva...

Robust artificial intelligence frameworks for lung cancer subtyping and malignancy detection on thoracic CT.

Accurate characterization of thoracic malignancies on computed tomography (CT) remains challenging because histological subtype differentiation and no...

Multimodal fusion-based prediction of postoperative survival in gallbladder adenocarcinoma: Model development and validation.

Accurate prognostic models are essential for optimizing treatment strategies in gallbladder adenocarcinoma (GBAC). We preliminarily constructed and va...

MICLEAR: Intelligent molecular cytology for intraoperative margin assessment of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly mortal cancer whose only potentially curative treatment is surgical resection. Intraoperative asse...

A comparative study of machine learning models for microbiome-based diagnosis and multi-class staging of colorectal cancer.

Colorectal cancer (CRC) is closely associated with gut microbiota dysbiosis; however, comprehensive benchmarking of machine learning models that integ...

Bridging prediction and reality: AI-based external validation of predictive models for lymph node metastasis in gastric cancer.

INTRODUCTION: The optimal extent of lymphadenectomy in gastric cancer surgery remains a subject of ongoing debate. Our previous modelling work indicat...

Nationwide 30-Day Surgical Outcomes in Korea: An Interim Analysis of the Korean Quality Improvement Platform in Surgery.

BACKGROUND: National surgical registries such as the American College of Surgeons NSQIP and Japan's National Clinical Database have shown that structu...

A weakly supervised deep learning-based recurrence prediction and risk stratification of lung adenocarcinoma from pathology whole-slide images.

BACKGROUND: Accurate prediction of postoperative recurrence in lung adenocarcinoma (LUAD) is essential for guiding clinical decision-making and improv...

Clinically reported covert cerebrovascular disease and risk of neurological disease: a whole-population cohort of 367 988 people using natural language processing.

BACKGROUND: The relevance of covert cerebrovascular disease (CCD) in practice is uncertain, partly because estimation of risk in whole clinical popula...

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lu...