Pulmonology

Distinct Tumor-Immune Ecologies in Patients with Lung Cancer Predict Progression and Define a Clinical Biomarker of Therapy Response.

UNLABELLED: Multiplexed imaging of tissues is an approach that holds promise for improving early detection, diagnosis, and treatment of cancer. In this study, we investigated multiplexed histologic images of paired pretreatment and on-treatment samples from nine patients with immunotherapy-refractory non-small cell lung cancer (NSCLC) treated with an oral histone deacetylase inhibitor (vorinostat)...

Deep-learning-based spectral motion artifact correction on photon-counting cardiac CT images.

Objective.While photon-counting computed tomography (PCCT) improves image quality and reduces radiation dose, artifacts induced by cardiac and respiratory motion is still a challenge. The purpose of this work is to evaluate the potential of an image-domain motion-artifact-correction method based on a deep-learning model that incorporates spectral information (material basis images).Approach.We sim...

MycoPermeNet-v2: Improved Prediction of Mycomembrane Permeation Using Fusion Noisy Student Self-Distillation.

Machine learning (ML) techniques offer a promising path for accelerating antibiotic discovery by computationally predicting and optimizing desirable p...

ARIMA-based forecasting of cerebral physiologic signals in acute traumatic brain injury: a CAnadian high-resolution TBI (CAHR-TBI) cohort study.

BACKGROUND: Traumatic brain injury (TBI) remains a major global health issue, with limited progress in reducing morbidity and mortality for TBI patien...

Facile Critical Evaluation of Extensive Lithium-Oxygen Battery Literature Using In-House Data and the Structured Query Learning-Retrieval-Augmented Generation Method.

Lithium-oxygen batteries (LOBs) offer combustion fuel-like energy densities but remain constrained by low efficiency, limited cycle life, and coupled ...

PSA-1DCNN: Multimodal Biomarker and Text Integration for Lung Cancer Diagnosis.

Integrating multimodal data, such as unstructured clinical narratives and quantitative blood biomarkers, remains a major challenge in modern healthcar...

Multimodal AI-based 28-day mortality prediction of pneumonia patients at ED discharge: a multicenter study.

Interpretable machine learning model for predicting the risk of dysphagia in patients after cardiac surgery.

BACKGROUND: Dysphagia is recognized as one of the most common severe complications following cardiac surgery, with the potential to result in adverse ...

Feasibility of Implicit Neural Representation Learned Motion Compensation for 3D Stack-of-Spirals Free-Breathing Cardiac Quantitative Susceptibility Mapping.

PURPOSE: Differential blood oxygenation between the right and left heart (ΔSO2) is an indicator of cardiovascular function currently assessed in clini...

Developing a Metabolic-Associated Prognostic Index for Risk Stratification and Therapeutic Guidance in Stage I Lung Adenocarcinoma via Multiomics Analysis.

PURPOSE: Early-stage lung adenocarcinoma (LUAD) exhibits substantial clinical heterogeneity that is not fully explained by TNM staging, highlighting t...

Sustainable Strategies in Tuberculosis Management: Bridging Ethnobotanical Pharmacology, Advanced Drug Delivery, and AI-Driven Innovation.

Tuberculosis (TB) persists as a leading infectious disease, with progress in global control complicated by emerging drug resistance, limited access to...

Emerging Trends and Innovations in Radiologic Diagnosis of Thoracic Diseases.

Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This revi...

Lung-DDPM: Semantic Layout-Guided Diffusion Models for Thoracic CT Image Synthesis.

With the rapid development of artificial intelligence (AI), AI-assisted medical imaging analysis demonstrates remarkable performance in early lung can...

Quantitative Chest Computed Tomography and Machine Learning for Subphenotyping Small Airways Disease in Long COVID.

PURPOSE: To investigate imaging phenotypes in posthospitalized COVID-19 patients by integrating quantitative CT (QCT) and machine learning (ML), with ...

Leveraging Artificial Intelligence to Transform Thoracic Radiology for Lung Nodules and Lung Cancer: Applications, Challenges, and Future Directions.

This review traces the historical path of artificial intelligence (AI) methods that have been applied to medical image interpretation. Early AI approa...

Quantitative CT and Artificial Intelligence in Chronic Lung Disease.

Computed tomography (CT) is routinely used in diagnosing and managing patients with chronic lung diseases such as chronic obstructive pulmonary diseas...

Retrieval Augmented Generation (RAG) for Evaluating Regulatory Compliance of Drug Information and Clinical Trial Protocols.

The purpose was to evaluate retrieval-augmented generative (RAG) artificial intelligence (AI) methods for assessing the regulatory compliance of drug ...

A Self-Supervised Foundation Model Based on Three-Dimensional Chest CT Scans for Lung Cancer Diagnosis and Prognosis Prediction.

Purpose To develop a self-supervised chest CT foundation model and evaluate its performance in lung cancer clinical tasks. Materials and Methods In th...

Simultaneous synthesis of perfusion and ventilation images from CT using a dual-decoder residual attention network for lung disease diagnosis.

BACKGROUND: Deep learning algorithms can synthesize pulmonary functional images from CT images. However, previous studies have only been able to predi...

Generalizability of deep learning-based dose conversion model in proton beam therapy.

BACKGROUND: In proton beam therapy (PBT), the analytical pencil beam (PB) algorithm involves dose uncertainties in inhomogeneous regions, making accur...