Lung Cancer

Development and Validation of an XGBoost-SHAP Model for Predicting Adverse Outcomes in Elderly Cardiovascular Patients With Polypharmacy: A Retrospective Cohort Study.

This study aims to develop and validate an interpretable machine learning model using Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) analysis to predict adverse outcomes in elderly cardiovascular patients with polypharmacy. This retrospective cohort study included 1200 patients aged ≥ 65 years with cardiovascular disease and polypharmacy (≥ 5 medications) from The Fi...

A modality-agnostic coronary artery habitat model for cardiac sparing in radiotherapy.

BACKGROUND: Emerging evidence suggests that the risk of cardiotoxicity increases with increased radiation dose to coronary arteries (CAs). However, robust tools to evaluate this increased burden for cancer patients are not available due to current limitations in imaging for radiotherapy treatment planning. PURPOSE: We have developed a novel, statistical methodology to define coronary artery "habit...

Estimation of pulmonary function from time-resolved dynamic chest radiography using machine learning in patients with respiratory disease.

BACKGROUND: Pulmonary function tests (PFTs), particularly spirometry, are the reference standard for assessing airflow limitation in respiratory disea...

Local Recurrence Prediction After Carbon-ion Radiotherapy for Early-stage Non-small Cell Lung Cancer Using Machine Learning.

BACKGROUND/AIM: Predicting local recurrence remains challenging in carbon-ion radiotherapy (CIRT) for non-small cell lung cancer (NSCLC). In this stud...

Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration.

Quantitative systems pharmacology (QSP) models require calibration data from literature, yet manual curation is inconsistently documented and large la...

Multimodal Management of Pain in Oral Squamous Cell Carcinoma: A Mechanism-Based Approach.

PURPOSE OF REVIEW: Oral squamous cell carcinoma (OSCC) is frequently associated with severe nociceptive and neuropathic pain that adversely affects pa...

Unraveling the roles of DKK1 and S100P in pancreatic ductal adenocarcinoma: Insights from machine learning, single-cell/spatial transcriptomics, and functional validation.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a 5-year survival rate of only 13%. Despite recent advances in diagnosi...

AI-driven discovery and validation of a GIPC1 PDZ domain inhibitor for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) remains highly aggressive, with a five-year survival rate under 13.3%, due to late diagnosis, rapid progressio...

Dynamic evolution of chaperone-mediated autophagy is associated with tumor microenvironment remodeling and prognostic stratification in lung adenocarcinoma: insights from single-cell transcriptomics, ensemble machine learning, and experimental validation.

BACKGROUND: Lung adenocarcinoma (LUAD) shows prognostic heterogeneity, and tumor-node-metastasis (TNM) staging is limited for individualized managemen...

Beyond Radiation Exposure: Occupational Health Challenges Across the Modern Radiology Workforce.

Radiology occupational safety has historically centred on radiation protection. Although radiation safety remains essential, the digital transformatio...

Artificial intelligence-assisted proteomic signatures for discriminating malignant from benign pulmonary nodules.

Lung cancer remains a major global health burden. Although low-dose CT (LDCT) is effective for early detection, its clinical application is limited by...

Miniaturizing and modifying natural proteins with Raygun.

Proteins have evolved over billions of years through coordinated substitutions, insertions and deletions, yet computational protein design cannot full...

Deep learning-based multi-modality image conversion for evaluating dose calculation and position correction accuracy in image-guided radiation therapy.

Cone-beam CT (CBCT) is widely used in image-guided radiation therapy (IGRT). However, CBCT has limited image quality, which can reduce dose calculatio...

Identification of Methionine Metabolism-Driven Heterogeneous Subtypes in Colorectal Cancer and Their Associated Immune Microenvironment.

AIM: Tumor heterogeneity, driven by metabolic reprogramming, challenges colorectal cancer (CRC) treatment. Methionine metabolism is crucial for tumor ...

Computed tomography body composition-based prediction of postoperative quality of life in non-small cell lung cancer patients.

OBJECTIVES: This paper aims to design an explainable machine learning model capable of predicting postoperative quality of life in elderly NSCLC patie...

High FDG Uptake in the Non-cancerous Lung Predicts Immune-Related Adverse Events and Poor Prognosis in Patients with Lung Cancer Treated with Immune Checkpoint Inhibitors.

RATIONALE AND OBJECTIVES: Immune checkpoint inhibitors (ICIs) have improved survival in non-small cell lung cancer (NSCLC); however, predicting immune...

Tumour cell density quantified by artificial intelligence is associated with differential benefit from irinotecan-based chemo-radiotherapy in locally advanced rectal cancer: a post-hoc study of the phase 3 ARISTOTLE trial.

BACKGROUND: Tumour cells and tumour-associated stroma are key components of the tumour microenvironment, and their interaction impacts disease progres...

PET/CT-derived whole-body composition and survival in resectable NSCLC: double machine learning-based adjusted association analysis of intermuscular adiposity burden and sex-specific metabolic phenotypes.

PURPOSE: To evaluate whether whole-body PET/CT-derived body composition features are associated with survival in patients with resectable non-small ce...

Artificial Intelligence for radiation protection in medical imaging and radiotherapy: A perspective from the AI Working Party of ICRP Committee 3.

PURPOSE: The increasing integration of Artificial Intelligence (AI) into clinical workflows for medical imaging and radiotherapy presents new opportun...

Radiation dose reduction in computed tomography perfusion of acute ischemic stroke patients using a denoising autoencoder.

BACKGROUND: Ischemic stroke results from the occlusion of a cerebral artery and is a leading cause of mortality and disability worldwide. Multimodal c...