Lung Cancer

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 used data of multiple environmental chemical exposures from the 2011-2012 cycle of the National Health and Nutrition Examination Survey (NHANES) to develop an interpretable machine learning model for hyperuricemia risk prediction. The least absolute shrinkage and selection operator (LASSO) regression...

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 need for accurate and early diagnostic solutions. This study introduces a novel multimodal artificial intelligence (AI) framework that integrates Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) to improve lung cancer classification and severity assessment. The CNN model, t...

From classical approaches to artificial intelligence, old and new tools for PDAC risk stratification and prediction.

Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most lethal malignancies, characterized by late-stage diagnosis and limited therap...

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 fully capture the biological heterogeneity of the dise...

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 its ability to process large datasets. In nuclear ra...

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, extracellular matrix (ECM) architectures, known as tumor-ass...

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 breakthrough in lung structure imaging, providing image re...

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-ray imaging serving as the standard diagnostic mod...

[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 medical fields. However, repeated CBCT scans expose ...

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 oral cavity representations, and Cone-Beam Comput...

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 (TME) of lung adenocarcinoma (LUAD), exerting signi...

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 Traditional Chinese Medicine (TCM) syndromes like...

Taming Stable Diffusion for Computed Tomography Blind Super-Resolution

High-resolution computed tomography (CT) imaging is essential for medical diagnosis but requires increased radiation exposure, creating a critical t...

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 of cancer, patients often experience toxicity dur...

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

Cardiac substructures are essential in thoracic radiation therapy planning to minimize risk of radiation-induced heart disease. Deep learning (DL) o...

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

Cancer subtype classification is crucial for personalized treatment and prognostic assessment. However, effectively integrating multi-omic data rema...

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 (WSIs). However, adapting these pretrained PFMs f...

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 objects, including periodically varying structure...

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 clinical applications. This review highlights how phot...