Lung Cancer

Development of a clinical prediction model using digital device data for interstitial lung disease in patients with unresectable stage III non-small cell lung cancer treated with durvalumab.

BACKGROUND: Early detection of interstitial lung disease including radiation pneumonitis (ILD/RP) is crucial in consolidative durvalumab therapy after chemoradiotherapy (CRT) in unresectable stage III non-small cell lung cancer. We conducted a multicenter, noninterventional pilot study to develop clinical prediction models for grade ≥2 ILD/RP following durvalumab treatment by using machine learnin...

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 CRC mortality. Recently, studies have attempted utilizing artificial intelligence for the classification of CRC. However, datasets were limited in these studies, and the limited number of findings resulting from these studies are not specific to cance...

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...

Deep learning for visceral pleural invasion in non-small cell lung cancer.

BACKGROUND: To evaluate and compare the diagnostic performance of several commonly used deep learning (DL) models and a conventional clinical-radiolog...

Unraveling the molecular landscape of chronic radiation injury: From oxidative stress signaling to translational modeling (Review).

The expanding footprint of human radiation exposure, driven by advances in interventional diagnostics, the resurgence of the nuclear industry and the ...

Integration of single-cell regulon atlas and bulk RNA-seq for individualized prognostic prediction in stomach adenocarcinoma.

Transcriptional regulators reflect cellular heterogeneity and are key for prognostic modeling. Given the poor prognosis of stomach adenocarcinoma (STA...

Deciphering the multi-target mechanism of Magnolia officinalis in irritable bowel syndrome with diarrhea: A combined network pharmacology, machine learning, and in vivo validation study.

BACKGROUND: The bark of Magnolia officinalis Rehder & E. Wilson, used in TCM for abdominal distension, pain, and diarrhea, aligns with IBS-D symptoms....

Longitudinal Plasma Proteomics Reveals an Immuno-thrombotic Signature that Predicts Radiation Pneumonitis in Lung Cancer.

PURPOSE: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy, often poorly predicted by static clinical and dosimetric ...

Uric Acid to HDL-C Ratio as a Novel Biomarker for Sarcopenia: A National Study with Machine Learning Insights.

BACKGROUND: Sarcopenia is a major age-related health burden. Although the uric acid to high-density lipoprotein cholesterol ratio (UHR) has been linke...

Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single-cell and Mendelian randomization analyses.

The progression of lung adenocarcinoma (LUAD) is influenced by polyamine metabolism, which modulates antitumor immunity, although the underlying mecha...

Predicting 2-Year Overall Survival in NSCLC from CT Scans Using 2D CNNs and Soft Attention.

Accurate overall survival (OS) prediction in non-small cell lung cancer (NSCLC) is crucial but challenging due to high-dimensional 3D computed tomogra...

Machine Learning Models for Predicting Mortality Risk and Survival Time in Lung Cancer Patients Treated with EGFR-TKIs.

This study develops machine learning models to predict patient mortality and estimate survival time using electronic health record (EHR) data from thr...

Generating synthetic CT from MR images for radiation-free computer-assisted quantification of hip morphology.

OBJECTIVES: To compare MR image-based synthetic CT (sCT) with conventional CT for computer-assisted quantification of hip morphology by evaluating oss...