Colon Cancer

Interpretable prediction of occult lymph node metastasis in pancreatic ductal adenocarcinoma using a model fusing habitat radiomics and deep learning.

PURPOSE: To evaluate the value of integrating habitat radiomics features and deep learning features for predicting occult lymph node metastasis (OLNM) in pancreatic ductal adenocarcinoma (PDAC). METHODS: Data from 212 eligible PDAC patients across two institutions were analyzed. Cohorts were allocated as follows: training (n = 115), internal validation (n = 50), and external validation (n = 47). H...

Colorectal liver metastases: imaging strategies for diagnosis, local treatment planning and systemic treatment response.

Colorectal liver metastases (CRLM) remain a major cause of cancer-related mortality, with imaging playing a critical role in diagnosis, treatment planning, and surveillance. CT and MRI are the mainstays of lesion detection, with MRI offering higher sensitivity, particularly for small or treated lesions. PET/CT and diffusion-weighted imaging (DWI) complement anatomical techniques, especially in sur...

Trusted polyp segmentation via multi-scale evidence ensemble in colonoscopy images.

PURPOSE: Accurate and reliable polyp segmentation is essential for early colorectal cancer detection. Although recent methods employing multi-scale fe...

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genom...

Quantitative CT Perfusion as a prognostic biomarker for chemotherapy response in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: Response evaluation of pancreatic ductal adenocarcinoma (PDAC) with routine contrast-enhanced CT (CECT) using RECIST is currently inadequa...

Efferocytosis-related biomarkers and immune infiltration in pancreatic ductal adenocarcinoma based on single-cell sequencing and machine learning: a comprehensive bioinformatics analysis and experimental validation.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly invasive and lethal malignancy, with its complex tumor microenvironment (TME) severely...

Federated CT foundation models for multi-center detection of lymph node metastasis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with prognosis strongly influenced by the presence of lymph node ...

Artificial intelligence-enhanced three-dimensional radiological analysis for predicting spread through air spaces in early-stage lung adenocarcinoma.

OBJECTIVES: Spread through air space (STAS) is a pathological feature that correlates with poor prognosis, especially in patients with lung adenocarci...

Multiparametric MRI-Based Integrated Analysis of Clinical, Radiomics, Deep Learning, and Machine Learning for Predicting Tumor Proliferation and Prognosis in Locally Advanced Rectal Cancer.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a predictive model integrating clinical, radiomics, deep learning (DL), and machine...

Impact of an AI prognostic tool on clinician performance in colorectal liver metastases.

While thousands of AI prediction models are published annually, few are adopted into routine practice, partly because improved statistical performance...

Automatic Recognition and Prognostic Prediction of Colorectal Liver Metastases Using a Multi-Scale Deep Learning Framework: Model Development and Validation Study.

BACKGROUND: Colorectal cancer liver metastasis (CRLM) presents considerable challenges in both diagnosis and prognosis, as conventional approaches oft...

Significance of tumor infiltrating granulocytes and neutrophil extracellular traps in colorectal cancer.

BACKGROUND: Neutrophils are the most abundant granulocytes in the tumor microenvironment and exert both pro- and anti-cancer effects. Activated neutro...

Imaging-based stratification of Claudin 18.2-positive gastric adenocarcinoma using 18F-FDG PET/CT radiomics.

PURPOSE: This study aimed to develop and validate a non-invasive, multimodal radiomics model based on preoperative 1⁸F-FDG PET/CT to predict CLDN18.2 ...

A palmitoylation-related prognostic risk scoring model and tumor microenvironment characterization in lung adenocarcinoma, using single-cell RNA sequencing data.

BACKGROUND: Lung adenocarcinoma (LUAD) is the predominant pathological subtype of non-small cell lung cancer. Its considerable tumor heterogeneity and...

Deep learning reconstruction for 40-keV virtual monoenergetic CT of colon cancer: evaluation of image quality and edge sharpness.

BACKGROUND: Virtual monoenergetic imaging (VMI) at 40 keV improves iodine attenuation in colon cancer CT but is constrained by severe image noise. Dee...

Construction of chronic inflammation and mitochondrial energy metabolism-associated predictive and therapeutic models for lung adenocarcinoma patients.

Integration of multi-omics and machine learning to identify core genes in PANoptosisof lung adenocarcinoma and their mechanisms in the tumor microenvironment and therapeutic potential.

Lung adenocarcinoma (LUAD) is one of the leading causes of cancer-related deaths worldwide, and its complex tumor microenvironment (TME) is a key barr...

Dual-layer spectral CT for predicting spread through air spaces in lung adenocarcinoma: a dual-center study.

OBJECTIVES: To investigate the value of machine learning classifiers incorporating dual-layer spectral CT (DLCT) parameters for preoperative predictio...

Integration of machine learning-based screening approaches and single-cell sequencing technique to identify cancer stemness genes associated with radiotherapy resistance in lung adenocarcinoma.

Integration of deep learning and radiomic features from multiplex immunohistochemistry images for reproducible Multi-Outcome prediction in a Multi-Center study of colorectal cancer.

OBJECTIVE: To develop and validate a robust, multimodal machine learning framework integrating radiomic and deep learning features from multiplex immu...