Lung Cancer

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 computed tomography (CT) findings, for identifying visceral pleural invasion (VPI) in small non-small cell lung cancer (NSCLC). This multi-center retrospective analysis included 2822 small NSCLCs. These were divided into four datasets (training, valid...

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 assessment of their expression remains challenging. This study aimed to determine whether radiomics features extracted from contrast-enhanced MRI could predict EGFR expression in high-grade gliomas (HGG) and to explore their associations with immune infiltr...

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...

[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...

ProteoBoostR: an interactive framework for supervised machine learning in clinical proteomics.

BACKGROUND: Mass spectrometry-based proteomics enables high-throughput quantification of thousands of proteins in clinical samples, fueling biomarker ...

Exploration Novel Therapeutic Targets for Periodontitis via Stress Granules Biomarkers.

BACKGROUND: Periodontitis (PD) is associated with stress granules (SGs), which are involved in cellular stress responses. Identifying biomarkers relat...

Lung scintigraphy for the diagnosis of acute pulmonary embolism.

Pulmonary embolism (PE) remains a major diagnostic challenge due to its potentially life-threatening nature and the clinical burden associated with an...

Amplifying image quality gain in x-ray phase contrast imaging of mastectomy samples with deep learning denoising.

Phase-contrast computed tomography (PCT) of the breast has previously been shown to produce higher-quality images at lower radiation doses without the...

Immune Checkpoint Activity and Prognostic Roles of TNFRSF8, CD160, and TNFSF9 in Primary and Brain Metastatic NSCLC.

Here, we utilized advanced bioinformatics approaches alongside experimental validation to identify key prognostic biomarkers and potential immune chec...

Numerical modeling and prediction of late estimated glomerular filtration rate in kidney transplant recipients based on machine learning models and the Monte Carlo simulation method.

OBJECTIVES: This paper presents an experimental numerical method for modeling and analyzing stochastic systems. For this purpose, various machine pred...

A screening strategy based on machine learning for diagnostic biomarkers in small cell lung cancer.

Small cell lung cancer (SCLC) is the most aggressive subtype with high mortality rates due to the lack of specific diagnostic biomarkers to delay the ...

An integrative multiomics random forest framework for robust biomarker discovery.

BACKGROUND: High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic ...

Semi-supervised learning for dose prediction in targeted radionuclide therapy: a synthetic data study.

Objective.Accurate and personalized radiation dose estimation is crucial for effective targeted radionuclide therapy (TRT). Deep learning (DL) holds p...