Lung Cancer

Revealing the limits of covalent docking and advancing affinity prediction with covalent-aware multi-task learning.

Targeted covalent inhibitors (TCIs) have become an important modality in modern drug discovery, but ...

CT Radiation Dose Reduction With Preserved Diagnostic Performance: How Far Have We Come Over 25 Years?

Concerns about the risk of radiation from CT have driven a spectrum of major advances in radiation d...

Rapid, label-free cancer detection in fresh pancreatic tissue using deep learning and multispectral Mueller matrix polarimetry.

BACKGROUND: Frozen section (FS) tissue assessment is essential for guiding intraoperative surgical d...

Enhancing diagnostic safety with low iodine, low radiation CTPA classification using deep learning.

Pulmonary embolism (PE) is a life-threatening condition for which computed tomography pulmonary angi...

Comparison of Image Quality Reconstructed Using Iterative Reconstruction and Deep Learning Algorithms Under Varying Dose Reductions in Dual-Energy Carotid CT Angiography.

Carotid CT angiography (CTA) is valuable for diagnosing carotid artery disease but involves radiatio...

Incremental prognostic value of immune cell densities beyond clinical parameters in non-small cell lung cancer.

BACKGROUND: Multiplex immunofluorescence imaging enables detailed characterization of the tumor immu...

Pilot clinical evaluation of artificial intelligence-driven guiding catheter simulation for optimizing percutaneous coronary intervention.

AIMS: In percutaneous coronary intervention (PCI), a suboptimal choice of guiding catheter may compr...

Foundations for AI-assisted Adverse Outcome Pathways (AOPs) in radiation research.

PURPOSE: Artificial Intelligence (AI) and Machine Learning (ML) are being explored to improve system...

TabPFN-driven ternary classification of stage IA lung adenocarcinoma subtypes using AI-derived histogram features a retrospective multicenter cohort study.

BACKGROUND: Preoperative differentiation of precursor glandular lesions (PGL), minimally invasive (M...

Metabolic characterization of tumor-immune interactions by multiplexed immunofluorescence reveals spatial mechanisms of immunotherapy response in non-small cell lung carcinoma (NSCLC).

Immune checkpoint inhibitors (ICI) have improved clinical outcomes for some patients with advanced N...

Machine learning framework for mRNA alternative splicing analysis identifies a signature of progression in colorectal adenocarcinoma.

Despite recent advances in genome-wide profiling and the discovery of novel therapeutic options for ...

Detection of Swallowing Abnormalities in Pediatric FEES Recordings Using Rule-Based and Model-Based Methods.

Pediatric swallowing dysfunction (SwD) poses serious health risks, including aspiration, malnutritio...

Interpretable machine learning based decision-making system for lung adenocarcinoma metastasis: a population-based study with exploration of multi-classification models.

AI-Assisted Chromosome Aberration Analysis for Rapid Biological Dose Estimation of Ionizing Radiation.

Radiation dose assessment in exposed individuals relies on the dicentric assay, the gold-standard cy...

Multi-omics integration in deciphering non-small cell lung cancer drug resistance: current status, challenges, and future prospects.

Lung cancer is the leading cause of cancer-related deaths globally. Non-small cell lung cancer (NSCL...

EXPRESS: Convolutional Autoencoder for Automated Pre-Processing of Tumor Cell and Tissue Raman Spectra.

Raman spectroscopy (RS) is a label-free, non-destructive optical modality that provides a detailed p...

Molecular classification of radiation-induced meningiomas.

PURPOSE: Radiation-induced meningiomas (RIMs) are an uncommon late complication of cranial irradiati...

Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and electronic population-based healthcare data.

INTRODUCTION: Within the UK there are 33 deaths every day from prostate cancer, second only to lung ...

Machine learning-based prognostic model for local control prediction in lung cancer patients undergoing stereotactic body radiation therapy: a preliminary study.

PURPOSE: Predicting local recurrence after stereotactic body radiation therapy (SBRT) for lung cance...

Multi-omics predicts radiotherapy response in small cell lung cancer patients receiving whole brain irradiation.

OBJECTIVE: Dosiomics and radiomics elaborate the low-and high-order features extracted from images t...

Evaluating the impact of deep learning-based image denoising on low-dose CT for lung cancer screening.

PURPOSE: Low-dose CT (LDCT) is increasingly being adopted as a preferred method for lung cancer scre...