Gastroenterology

Deep learning-based dose prediction in proton beam therapy for hepatocellular carcinoma: comparison of network architectures and loss functions.

In proton beam therapy (PBT) for hepatocellular carcinoma (HCC), deep learning (DL)-based dose prediction offers clinical value by providing immediate reference dose distributions as a guideline tool for treatment planners and enabling virtual PBT dose assessment at institutions lacking PBT facilities to support clinical decision-making. This study proposes a dose gradient-aware DL training approa...

Automated Delineation of Couinaud Segments on CT for Future Liver Remnant Volumetry.

Purpose To develop a deep learning model that automatically delineates the eight liver Couinaud segments and the spleen on CT for future liver remnant (FLR) volumetry. Materials and Methods In this retrospective study (January 2001 and October 2025), eight liver Couinaud segments and the spleen were manually labeled on CT scans of patients from Institution-A and the public Medical Segmentation Dec...

Histology-defined activated cancer-associated fibroblasts are associated with poor survival in diffuse-type gastric adenocarcinoma.

Activated cancer-associated fibroblasts (aCAFs), characterized by distinct histological features including fibroblast proliferation and extensive desm...

Beyond Diagnosis: A Systematic Review of Artificial Intelligence and Deep Learning in Monitoring Iron Overload and Organ Toxicity in Beta-Thalassemia.

Transfusion-dependent β-thalassemia (B-TM) is complicated by progressive iron overload, remaining a primary cause of organ toxicity and mortality desp...

Artificial Intelligence Models Using Magnetic Resonance Imaging to Predict Response to Chemoradiotherapy in Rectal Cancer: A Systematic Review.

BACKGROUND: Pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) is a key prog...

A Machine-Learning Approach to Predicting Adenoma Detection and Surveillance Impact of Deeper Sections in Colorectal Polypectomy Specimens.

CONTEXT.—: In the routine practice of pathology, cutting deeper levels into paraffin blocks of colorectal polypectomy specimens that do not show a les...

Applying Artificial Intelligence Technologies to Detect Pancreatic Ductal Adenocarcinoma Using Routine Laboratory Tests: A Pilot Study with the Aid of Machine Learning Suitable for a Small Data Analysis.

Objective This study aimed to investigate whether artificial intelligence could identify pancreatic ductal adenocarcinoma (PDAC) in patients aged <70 ...

A Biomimetic Nanozyme Engineered via Coordination and Confinement for Photothermal-Reinforced Catalytic Detection of Extracellular Vesicles and Diagnosis of Cancer.

Extracellular vesicles (EVs) have emerged as promising biomarkers for liquid biopsy. However, their clinical detection is hampered by heterogeneity an...

Publisher Correction: Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

The density of tumour infiltrating lymphocytes in oesophago-gastric cancer varies with disease stage, geographical region and treatment: a post hoc analysis of nine phase III clinical trials.

BACKGROUND: Tumour infiltrating lymphocytes (TILs) are a key component of the tumour microenvironment. To establish a clinically relevant TILs cut-off...

A framework for quantifying and leveraging uncertainty in pre-trained CT denoising model.

OBJECTIVE: To develop an architecture-agnostic framework that estimates, calibrates, and leverages total uncertainty (aleatoric + epistemic) in pre-tr...

Automatic Detection of Ganglion Cells as a Supporting Tool for Hirschsprung Disease Diagnosis.

Hirschsprung disease (HD) is a congenital disorder characterized by the absence of ganglion cells in the colonic nervous plexuses, resulting in bowel ...

Comprehensive evaluation of cross cancer generalization in histopathology segmentation models across 21 tumor types.

BACKGROUND: Artificial intelligence has significantly advanced computational pathology by enabling high-resolution, clinical-grade tumor segmentation ...

Pancreatic tumor detection in computed tomography images through a rotary positional siamese vision transformer.

Vision Transformers (ViTs) are one of the powerful tools in medical imaging, providing new possibilities for pancreatic cancer diagnosis. In recent ye...

Prediction of tumor regression grading in rectal cancer neoadjuvant chemoradiotherapy: a habitat radiomics analysis of imaging biomarker.

BACKGROUND: Tumor regression grading (TRG) is a core prognostic predictor of treatment outcomes in rectal cancer. Conventional TRG assessment methods ...

Construction of a risk scoring model based on machine learning and validation of the role of the key gene SERPINE1 in the progression of colon cancer.

BACKGROUND AND OBJECTIVE: Colon cancer (CC) is a highly prevalent malignant tumor with a high mortality rate worldwide. Despite recent advancements in...

Deep Learning-Driven Analysis and Quantification of Histopathologic Features in a Dextran Sulfate Sodium-Induced Colitis Mouse Model.

Dextran-based colitis models have been used extensively to study the pathophysiology of inflammatory bowel disease, including ulcerative colitis and C...

Isocaloric liquid and solid meals induce comparable postprandial gastric motility: Implications for oral drug delivery assessed by real-time MRI.

Postprandial gastric motility critically influences the intragastric behavior of oral dosage forms and subsequent drug absorption. Combining real-time...

Dynamic changes in gut microbiota and discovery of prognostic biomarkers in locally advanced pancreatic cancer during chemoradiotherapy.

UNLABELLED: Pancreatic cancer has an exceptionally poor prognosis, with the majority of cases diagnosed at an advanced stage. Concurrent chemoradiothe...

Multi-omics-based machine learning model predicts response and guides treatment in Crohn disease: a case study in nutritional therapy.

BACKGROUND: Biomarkers are needed to predict treatment response and guide therapeutic decisions in Crohn disease (CD). We aimed to develop and validat...