Lung Cancer

C-X-C Motif Chemokine Ligand 3 as a Potential Biomarker for Diagnosis and Prognosis of Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease globally, yet reliable biomarkers for its diagnosis and progression assessment are lacking. This study employed artificial intelligence techniques, including weighted gene co-expression network analysis (WGCNA) and machine learning, to identify crucial genes associated with DKD. Validation was conducted using online data...

Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer: A Scoping Review.

BACKGROUND: Artificial intelligence, particularly machine learning, has great potential to improve health outcomes, including predicting adverse conditions. In breast cancer, machine learning models can help personalize prevention strategies for radiation-induced cutaneous toxicity. METHODS: This scoping review aimed to explore machine learning models for predicting radiation dermatitis in women w...

Artificial intelligence-driven 3D surface-topography app for screening and monitoring adolescent scoliosis: early results from a single institution.

PURPOSE: Radiation-free tools, such as scoliometers, ultrasound, and Moiré topography, have been explored for monitoring Adolescent Idiopathic Scolios...

Identification of diagnostic and prognostic biomarkers in lung adenocarcinoma through integrated bioinformatics analysis and real time PCR validation.

Lung cancer is the third most common cancer in the US with a 5-year survival rate of 17%. Non-small cell lung cancer, especially adenocarcinoma, preva...

Feasibility of single-phase DECT as an alternative to triple-phase CT for imaging-defined risk factors assessment in neuroblastoma for dose reduction.

OBJECTIVE: To investigate the feasibility of evaluating Imaging-Defined Risk Factors (IDRFs) in Neuroblastoma (NB) patients using venous-phase (VP)-on...

Education as a Tool to Navigate Changing Technology in Radiation Oncology.

While technological innovation in radiation therapy (RT) continues to accelerate, safe and equitable adoption of emerging tools is reliant on the read...

Machine learning-driven identification of shared and disease-specific mitochondria-related genes in COPD, NSCLC, and NSCLC with COPD.

Chronic obstructive pulmonary disease (COPD) and non-small-cell lung cancer (NSCLC) often coexist; here, the shared mitochondrial drivers were investi...

Unraveling heterogeneity in LUAD via multi-omics integration: molecular classification and therapeutic implications.

Lung adenocarcinoma (LUAD) displays significant morphological and molecular heterogeneity both within and between tumors. This heterogeneity, coupled ...

Integrated predictive model for visceral pleural invasion in small NSCLC with high clinical utility.

This study aims to develop and validate a multi-feature integrated imaging fusion (MIIF) model, incorporating deep learning, radiomics features, and c...

Radiomics-based gradient boosting model on contrast-enhanced MRI for non-invasive prediction of epidermal growth factor receptor expression and therapeutic response to EGFR-targeted antibody-drug conjugates in high-grade glioma organoid models.

BACKGROUND: Epidermal growth factor (EGF) and its receptor EGF(EGFR) play crucial roles in glioblastoma (GBM) prognosis. However, non-invasive assessm...

Novel "No-Lo" imaging techniques to minimize intraoperative radiation exposure in vascular and endovascular surgery.

INTRODUCTION: The rapid expansion in endovascular techniques has placed vascular surgeons among those most exposed to occupational medical radiation. ...

A multi-algorithm machine learning framework identifies FGD5, LRRC36, C8B, and MYOC as novel diagnostic biomarkers in lung adenocarcinoma.

Artificial Intelligence Driven Virtual Screening and Molecular Docking Approaches Identified LIFR, BTG2, EPHX2, and PAK3 as Targets and BI-2536, AP-24534, and AZ-628 as Repurposed Drugs for PDAC.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal tumors worldwide, with limited effective treatments. Globally, the in...

Predicting and interpreting protein and phosphoprotein abundance from pan-cancer and single-cell transcriptomes.

Proteins that impact phenotype and disease are often approximated by RNA expression, which poorly infers protein abundance. We developed DeepGxP, a de...

Effect of frailty status and histopathological features on upstaging of non-muscle-invasive bladder cancer: a critical analysis based on machine learning.

AIM: This study aimed to develop a machine learning (ML)-assisted model to predict the risk of upstaging (subsequent higher stage on repeat pathology)...

[Artificial intelligence prediction of surgical difficulty in mid-low rectal cancer: a single-center cohort study].

Objective: This study processes and analyzes rectal MRI images of patients with mid-to-low rectal cancer using deep learning technology, and integrate...

MRI-based multilevel radiomics and transformer features for predicting radiation-induced carotid artery injury after nasopharyngeal carcinoma radiotherapy: A multicenter study.

PURPOSE: To develop and validate an MRI-based fusion model (Rad-SRad-SwinT) integrating conventional radiomics (Rad), subregional radiomics (SRad), an...

Anti-angiogenesis effect of Baiying Juhua Decoction on the non-small cell lung cancer: integrating pharmacology, multi-machine learning and experimental investigation.

Baiying Juhua Decoction (BYJHD) is a well-established traditional Chinese herbal formula primarily composed of Solanum lyratum and chrysanthemum, whic...

Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model.

BACKGROUND: Lung cancer is one of the major cancers worldwide, and rapid, accurate diagnosis is crucial for subsequent treatment and management. Curre...

Breast cancer frontiers: mapping molecular signals, intelligent diagnostics, and adaptive therapies.

Breast cancer continues to be a significant worldwide health concern, requiring ongoing improvements in early detection, therapeutic approaches, and c...