Lung Cancer

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). Understanding the molecular mechanisms driving this progression is critical for identifying diagnostic biomarkers and therapeutic targets. METHODS: We integrated single-cell RNA sequencing and bulk transcriptomic datasets to investigate fibroblast heterog...

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 targeted delivery, cytotoxic action, and non-invasive imaging into a unified clinical feedback loop. This review synthesizes the cutting-edge convergence of three interdependent pillars: antibody-drug conjugates (ADCs), which act as Trojan horses delive...

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...

Low-dose CT reconstruction by self-supervised learning in the projection domain.

OBJECTIVE: To address the critical issue of compromised image quality and diagnostic accuracy in low-dose computed tomography (LDCT) due to increased ...

CARES-Net: a channel-attention residual network for multi-disease classification in small-sample 1H NMR metabolomics data.

BACKGROUND: NMR-based metabolomics is widely used in disease diagnosis due to its non-destructive and quantitative advantages. However, its analytical...

Non-small Cell Lung Cancer in Metastatic Renal Cell Carcinoma Patients.

INTRODUCTION: Renal cell carcinoma (RCC) most commonly metastasizes to the lungs and shares risk factors with lung cancer. However, primary lung cance...

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...

CTM-DETR-frequency-aware and statistically guided transformer for early infrared forest fire detection.

Early-stage infrared forest fire detection is severely hindered by strong background thermal interference and extremely weak fire radiation signals. E...

Comprehensive Multi-Omics Profiling of Tertiary Lymphoid Structures Reveals Immunogenetic Landscapes and Prognostic Subtypes in Lung Adenocarcinoma.

Tertiary lymphoid structures (TLSs) are key components of the tumor immune microenvironment and show prognostic relevance in many cancers. However, th...

Artificial intelligence: Catalyzing a new era in pancreatic cancer cure.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human cancers, mostly due to its insidious onset that consequently leads to dia...

Ultra-low-dose CT for malignant metastasis screening using a deep learning image reconstruction algorithm.

PURPOSE: To evaluate the feasibility and diagnostic performance of ultra-low-dose CT (ULD-CT) for screening malignant metastasis using super-resolutio...