Lung Cancer

A systematic review and meta-analysis of the utility of quantitative, imaging-based approaches to predict radiation-induced toxicity in lung cancer patients.

BACKGROUND AND PURPOSE: To conduct a systematic review and meta-analysis of the performance of radio...

Trustworthy AI for stage IV non-small cell lung cancer: Automatic segmentation and uncertainty quantification.

Accurate segmentation of lung tumors is essential for advancing personalized medicine in non-small c...

Development and validation of an interpretable machine learning model for predicting hyperuricemia risk: Based on environmental chemical exposure.

Hyperuricemia is a global health concern, with environmental chemicals as risk factors. This study u...

Multimodal AI framework for lung cancer diagnosis: Integrating CNN and ANN models for imaging and clinical data analysis.

Lung cancer remains a leading cause of cancer-related mortality worldwide, emphasizing the critical ...

Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to full...

RAPTOR-AI: An open-source AI powered radiation protection toolkit for radioisotopes.

Artificial intelligence (AI) has gained significant attention in various scientific fields due to it...

Computationally Enabled Polychromatic Polarized Imaging Enables Mapping of Matrix Architectures that Promote Pancreatic Ductal Adenocarcinoma Dissemination.

Pancreatic ductal adenocarcinoma (PDA) is a highly metastatic and lethal disease. In PDA, extracellu...

Artificial Intelligence-assisted Pixel-level Lung (APL) Scoring for Fast and Accurate Quantification in Ultra-short Echo-time MRI

Lung magnetic resonance imaging (MRI) with ultrashort echo-time (UTE) represents a recent breakthr...

BPD-Neo: An MRI Dataset for Lung-Trachea Segmentation with Clinical Data for Neonatal Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a common complication among preterm neonates, with portable X-...

[Advances in low-dose cone-beam computed tomography image reconstruction methods based on deep learning].

Cone-beam computed tomography (CBCT) is widely used in dentistry, surgery, radiotherapy and other me...

ViT-NeBLa: A Hybrid Vision Transformer and Neural Beer-Lambert Framework for Single-View 3D Reconstruction of Oral Anatomy from Panoramic Radiographs

Dental diagnosis relies on two primary imaging modalities: panoramic radiographs (PX) providing 2D...

Characterization of m6A-Related Genes in Tumor-Associated Macrophages for Prognosis, Immunotherapy, and Drug Prediction in Lung Adenocarcinomas Based on Machine Learning Algorithms.

Tumor-associated macrophages (TAMs) are a vital immune component within the tumor microenvironment (...

Network Pharmacology Reveals HSPA1A/BST2 as Potential Targets of Ci Bai Capsule's Active Compounds Intervening in Leukopenia

Background: Radiation-induced leukopenia caused by low-dose exposure is frequently associated with...

Taming Stable Diffusion for Computed Tomography Blind Super-Resolution

High-resolution computed tomography (CT) imaging is essential for medical diagnosis but requires i...

Validation and Derivation of miRNA-Based Germline Signatures Predicting Radiation Toxicity in Prostate Cancer.

PURPOSE: Although radiotherapy (RT) is one of the primary treatment modalities used in the treatment...

Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation

Cardiac substructures are essential in thoracic radiation therapy planning to minimize risk of rad...

MoXGATE: Modality-aware cross-attention for multi-omic gastrointestinal cancer sub-type classification

Cancer subtype classification is crucial for personalized treatment and prognostic assessment. How...

Single GPU Task Adaptation of Pathology Foundation Models for Whole Slide Image Analysis

Pathology foundation models (PFMs) have emerged as powerful tools for analyzing whole slide images...

Analytical Reconstruction of Periodically Deformed Objects in Time-resolved CT

Time-resolved CT is an advanced measurement technique that has been widely used to observe dynamic...

Deep learning model for predicting immunotherapy response in patients with advanced NSCLC: Study findings demonstrate a strong and independent deep learning-based feature associated with an immune checkpoint inhibitor response in patients with NSCLC across cohorts.

Future Applications of Cardiothoracic CT.

Radiologists are witnessing astonishing innovation and advancement of CT technologies and their clin...