Pulmonology

Can intraoperative improvement of radial endobronchial ultrasound imaging enhance the diagnostic yield in peripheral pulmonary lesions?

BACKGROUND: Data regarding the diagnostic efficacy of radial endobronchial ultrasound (R-EBUS) findings obtained via transbronchial needle aspiration (TBNA)/biopsy (TBB) with endobronchial ultrasonography with a guide sheath (EBUS-GS) for peripheral pulmonary lesions (PPLs) are lacking. We evaluated whether intraoperative probe repositioning improves R-EBUS imaging and affects diagnostic yield and...

Development and validation of a machine learning model for real-time prediction of invasive mechanical ventilation weaning readiness.

PURPOSE: To develop and validate a bedside machine learning (ML) decision support tool for prediction of invasive mechanical ventilation (IMV) weaning readiness.

Artificial Intelligence for Risk Stratification of Acute Pulmonary Embolism: Perspectives on Clinical Needs, Expanding Toolkit, and Pathways Forward.

Despite a significant number of innovations for management of acute pulmonary embolism (PE) over the past decade, PE-related mortality has not decreas...

Gender- and Age-Associated Variations in the Prevalence of Atelectasis, Effusion, and Nodules on Chest Radiographs: A Large-Scale Analysis Using the NIH ChestX-Ray8.

Chest radiography remains a cornerstone of thoracic imaging; however, the influence of patient demographics and technical factors on radiologic findi...

An integrative and comprehensive analysis of blood transcriptomes combined with machine learning models reveals key signatures for tuberculosis diagnosis and risk stratification.

Tuberculosis (TB) remains a major global health challenge, contributing substantially to morbidity and mortality worldwide. The progression from (Mtb...

Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing.

Tuberculosis (TB) kills more people annually than any other pathogen. Resistance is an ever-increasing global problem, not least because diagnostics r...

Evaluation of locoregional invasiveness of early lung adenocarcinoma manifesting as ground-glass nodules via [Ga]Ga-FAPI-46 PET/CT imaging.

PURPOSE: Accurate differentiation of the histologic invasiveness of early-stage lung adenocarcinoma is crucial for determining surgical strategies. Th...

Deep learning reconstruction combined with contrast-enhancement boost in dual-low dose CT pulmonary angiography: a two-center prospective trial.

PURPOSE: To investigate whether the deep learning reconstruction (DLR) combined with contrast-enhancement-boost (CE-boost) technique can improve the d...

Big data-driven machine learning: transforming multi-omics lung cancer research.

BACKGROUND: Lung cancer remains a major global health threat, with its biological complexity and patient heterogeneity posing significant challenges. ...

Explainable machine learning and feature interpretation to predict survival outcomes in the treatment of lung cancer.

The treatment outcomes of lung cancer are highly variable, and machine learning (ML) models provide valuable insights into how clinical and biochemica...

The predictive value of F-FDG PET/CT radiomics for pleural invasion in non-small cell lung cancer.

OBJECTIVE: This study aims to develop and validate a PET/CT radiomics fusion model for preoperative predicting pleural invasion (PI) in non-small cell...

Predictive machine learning model for 30-day hospital readmissions in a tertiary healthcare setting.

MOTIVATION: Hospital readmissions represent a major challenge for healthcare systems due to their impact on patient outcomes and associated costs. As ...

CDKN1A and EGR1 are key genes for endoplasmic reticulum stress-induced ferroptosis in MASH.

Metabolic dysfunction-associated steatohepatitis (MASH) is a complex liver disease whose pathogenesis involving endoplasmic reticulum (ER) stress and ...

COVID-19CT+: A public dataset of CT images for COVID-19 retrospective analysis.

Background and objectiveCOVID-19 is considered as the biggest global health disaster in the 21st century, and it has a huge impact on the world.Method...

Self-supervised feature learning for cardiac Cine MR image reconstruction.

We propose a self-supervised feature learning assisted reconstruction (SSFL-Recon) framework for MRI reconstruction to address the limitation of exist...

Single-cell multimodal analysis reveals tumor microenvironment predictive of treatment response in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) constitutes over 80% of lung cancer cases and remains a leading cause of cancer-related mortality worldwide. Despit...

Integrative machine learning model for subtype identification and prognostic prediction in lung squamous cell carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) is a leading cause of cancer-related mortality, and tumor heterogeneity could result in diverse progno...

Artificial intelligence in the management of patient-ventilator asynchronies: A scoping review.

BACKGROUND: Patient-ventilator asynchronies (PVAs) are frequent complications in mechanically ventilated patients, contributing to adverse outcomes su...

Interpretable machine learning model integrating contrast-enhanced CT environmental radiomics and clinicopathological features for predicting postoperative recurrence in lung adenocarcinoma: a retrospective pilot study.

PURPOSE: This study aims to develop an interpretable predictive model combining contrast-enhanced CT (CECT) radiomics features with clinicopathologica...

Immune-related gene risk model establishment and role of key gene FUCA1 in malignant pleural mesothelioma.

BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare type of tumor closely associated with asbestos exposure. Increasing evidence shows that hig...