Lung Cancer

Displaced minds: ecologies of depression.

This issue of the Biomedical Journal explores emerging perspectives across neuropsychiatry, metabolism, aging, regenerative medicine, artificial intelligence, and chronic inflammatory disease. Featured contributions examine epigenetic and neuroendocrine mechanisms in depression, including the antidepressant-like effects of magnolol in chronic stress-induced models and the potential relevance of sh...

Age-Dependent z Scores and eGFR-Adjusted Reference Ranges for Neurofilament Light: A Practical Approach for Clinical Laboratories.

BACKGROUND: Neurofilament light (NfL) has emerged as a sensitive biomarker of neuroaxonal damage across a wide spectrum of neurological conditions, including acute brain injury as well as chronic inflammatory and neurodegenerative diseases. However, its clinical utility is compromised by physiological confounders, particularly age and renal function. The aim of the study was to establish age-depen...

Super-resolution deep learning reconstruction achieves low-dose high-resolution imaging in pediatric cardiac computed tomography.

BACKGROUND: Cardiac computed tomography (CT) is widely utilized in pediatric cardiology, but minimizing radiation exposure is essential. Recently, sup...

Interpretable machine learning model for predicting hemorrhage following ultrasound-guided percutaneous renal biopsy: a multicenter study.

OBJECTIVE: This study aims to develop and validate an interpretable machine learning (ML) model for predicting post-procedural hemorrhage (PH) after u...

Predicting late radiation-associated neurocognitive and endocrine toxicity in patients with brain tumors.

PURPOSE: Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, imp...

RAS pathway activity subtypes identified by machine learning define prognostic and immune microenvironment characteristics in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD), the predominant histological subtype of non-small cell lung cancer, remains a leading cause of cancer-related ...

Elucidating the molecular mechanisms linking bisphenol A to breast cancer: an integrated study of bioinformatics, machine learning, and molecular docking.

Bisphenol A (BPA) is a prevalent environmental endocrine disruptor linked to breast cancer. However, the precise molecular mechanisms and core therape...

Machine learning-based prediction of a high-risk kidney function trajectory class after acute kidney injury.

OBJECTIVES: Monitoring kidney function after acute kidney injury (AKI) hospitalisation is essential for identifying patients at risk of rapid progress...

Recent Updates on Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration and Its Clinical Implications.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is the standard minimally invasive modality for mediastinal staging in no...

Integrating single-cell analysis and digital pathology for risk stratification in pancreatic cancer.

Early liver metastasis is a major factor contributing to the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). Single-cell RNA sequencing (sc...

Integrative analysis and experimental validation of dioxin-interacting genes reveal diagnostic and prognostic biomarkers in lung adenocarcinoma.

Dioxins are persistent environmental pollutants and key components of the human exposome with established carcinogenic potential. As airborne toxicant...

Two-Step Ensemble Convolutional Neural Networks for Colonoscopic Biopsy Classification Resembling Pathologists' Process.

BACKGROUND: Colorectal cancer (CRC) is a leading cause of mortality worldwide, and early examination via colonoscopy is increasingly used to prevent C...

Evaluating artificial intelligence-generated synthesized mammography as a standalone alternative to digital mammography with or without tomosynthesis.

PURPOSE: To evaluate whether standalone synthesized mammography (SM) can maintain or improve diagnostic accuracy while reducing reading time and radia...

Whole-body reduced-dose dual-energy CT with deep learning image reconstruction for detection of osteolytic lesions in multiple myeloma.

INTRODUCTION: Deep learning image reconstruction (DLIR) has been incorporated into dual-energy CT (DECT) to improve image quality. However, its applic...

Development of a model for design radiation shielding composite aprons using machine learning.

This study presents the development and evaluation of a novel lead-free composite for radiation shielding, designed using an artificial neural network...

Transcriptional states define dependencies and therapeutic vulnerabilities in head and neck cancer.

Molecular heterogeneity in head and neck squamous cell carcinoma (HNSCC) is well recognized, yet existing subtype frameworks remain largely descriptiv...

Predicting fire consequences with the transformer model based on multimodal feature fusion.

Accurate prediction of fire consequences is fundamental to process safety management and quantitative risk assessment in the chemical process industri...

Machine Learning-Predicted Risk Trajectories for Incident Chronic Kidney Disease and Associations with Post-CKD Outcomes.

Chronic kidney disease (CKD) is a progressive condition where risk accumulates before clinical onset. However, strategies for evaluating CKD risk rema...

From data-rich to evidence-ready: A narrative review of generative artificial intelligence as a statistical scaffold in medical radiation sciences research.

BACKGROUND: Medical radiation science (MRS) research faces a growing asymmetry between a small body of high-rigour, statistically robust studies and a...

An automatic detection model for spread through air spaces in postoperative pathological sections based on deep learning in NSCLC.

Spread through air spaces (STAS) is recognized as an aggressive pattern of invasion in lung cancer and has been associated with poorer survival outcom...