Lung Cancer

Deep residual network fusing CT images and clinical variables to predict lung adenocarcinoma aggressiveness.

BACKGROUND: Lung adenocarcinoma presenting as ground-glass nodules (GGNs) comprises three invasive subtypes (adenocarcinoma in situ [AIS], minimally invasive adenocarcinoma [MIA], invasive adenocarcinoma [IAC]) with distinct prognoses and management strategies. Preoperative discrimination of these subtypes remains challenging for radiologists, and existing deep learning models rarely integrate mul...

Integrative Bioinformatics, Experimental Validation, and Interpretable Machine Learning Reveal Oxyresveratrol-Mediated Protection Against Cadmium-Induced Lung Adenocarcinoma-Related Transcriptional Dysregulation.

Cadmium (Cd) is a toxic heavy metal strongly implicated in lung adenocarcinoma (LUAD) through mechanisms involving oxidative stress, epigenetic dysregulation, and chronic inflammation. This study aimed to identify Cd-responsive genes associated with LUAD and to evaluate the protective effects of oxyresveratrol (O-RES) against Cd-induced molecular alterations. Using an integrated bioinformatics app...

Deep Learning Framework for Early Detection of Pancreatic Cancer Using Multi-modal Medical Imaging Analysis.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal forms of cancer, with a five-year survival rate below 10% primarily due to late...

Robot-assisted lumbar facet joint infiltration improves accuracy and reduces radiation exposure compared to the manual technique in a comparative phantom study.

Compact robotic systems offer new opportunities for spinal procedures outside the operating room, but their potential for small-scale interventions su...

Characterization of telomere-related gene subtypes in lung adenocarcinoma and their implications for prognosis and treatment.

BACKGROUND: Telomeres, located at chromosome ends, regulate cell division and maintain genomic stability. Telomere-related genes (TRGs) play essential...

Artificial neural network analysis for oxytactic microbes in hybrid nanofluid with chemical reaction and thermal radiation.

The purpose of this investigation is to assess the outcome of Oxytactic microorganism in chemical reactive flow of TiO2 + GO/water based hybrid nanofl...

Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence.

Risk stratification is an important tool in clinical decision-making, yet current approaches often fail to translate sophisticated survival analysis i...

Single-cell and bulk omics uncover fibroblast heterogeneity and HSPH1 as a key driver in Barrett's esophagus to esophageal adenocarcinoma progression.

BACKGROUND: Esophageal adenocarcinoma (EAC) is a highly aggressive malignancy with poor prognosis, often evolving from Barrett's esophagus (BE). Under...

Precision theranostics in oncology: integrating antibody-drug conjugates, radioimmunotherapy, and immuno‑PET for adaptive cancer care.

The century-old vision of a "magic bullet" in oncology is being realized through the paradigm of precision theranostics, which formally integrates tar...

Feasibility of retrieval-augmented generation for large language models with Japanese input in radiotherapy.

Large language models (LLMs) have recently gained attention for their potential. However, concerns remain regarding their reliability due to limitatio...

Artificial intelligence, omics, and biomarkers: Redefining lung cancer early detection.

Lung cancer, the leading cause of death worldwide, claims millions of lives yearly, largely due to limited early interventions. Currently used lung ca...

Reconstruction algorithms and arm positioning effects on abdominal CT image quality and radiation dose: a phantom study.

OBJECTIVE: To evaluate the effects of arm positioning and reconstruction algorithms on radiation dose and image quality of abdominal CT. MATERIALS AND...

Pediatric lung ground glass nodules: a real-world, large-scale CT cohort analysis.

BACKGROUND: Increasing detection of pediatric ground-glass nodules (GGNs) presents a clinical dilemma lacking robust evidence and guidelines. We aimed...

Deep Learning Morphometric Analysis on Protocol Biopsies Predicts Future Graft Function.

INTRODUCTION: The predictive value of Banff classification in protocol transplant biopsies without specific lesions is limited. Morphometry provides p...

Single-cell and spatial transcriptomic profiling of POU5F1 in Lung Adenocarcinoma: Dynamics, spatial niche, and prognosis via multi-algorithm ML.

BACKGROUND: POU5F1 (OCT4), a core regulator of pluripotency, plays an important role in tumor stemness and immune microenvironment remodeling, yet its...

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...

National Institute of Allergy and Infectious Diseases (NIAID) - Bundeswehr Institute of Radiobiology (BIR) Joint Programmatic Workshop on a Global Approach to Medical Countermeasure Development in Radiological Public Health Preparedness.

This invited commentary grew out of a presentation made at the 2025 ConRad Meeting in Munich, Germany, and summarizes talks made by researchers suppor...

Positron emission tomography imaging of inflammation and infection in children: an update.

Positron emission tomography (PET) has been used in pediatric oncology since the modality gained traction 20 years ago but has been used more sparingl...

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 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...