AIMC Topic: Lung Neoplasms

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Decoding Dendritic Cell Subtypes via Integrated Radiogenomics: A Stacked Ensemble Model for Predicting Immunotherapy Response in NSCLC.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
We pioneer a multimodal framework integrating single-cell RNA sequencing (scRNA-seq), radiomics, and deep learning to decipher dendritic cell (DC)-mediated mechanisms underlying anti-PD-1 response in non-small cell lung cancer (NSCLC). Single-cell RN...

Current and future applications of artificial intelligence in lung cancer and mesothelioma.

Thorax
BACKGROUND: Considerable challenges exist in managing lung cancer and mesothelioma, including diagnostic complexity, treatment stratification, early detection and imaging quantification. Variable incidence in mesothelioma also makes equitable provisi...

Comprehensive Analysis of Epigenetic Signatures in Non-Small Cell Lung Cancer: Development and Validation of an Epigenetics-Based Prognostic Model for Drug Sensitivity Prediction.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Non-small cell lung cancer (NSCLC) exhibits complex epigenetic dysregulation that impacts treatment response and prognosis, yet comprehensive analysis linking epigenetic signatures to clinical outcomes remains limited. We integrated single-cell RNA s...

Sex-specific prognostic value of automated epicardial adipose tissue quantification on serial lung cancer screening chest computed tomography.

European heart journal. Cardiovascular Imaging
AIMS: Epicardial adipose tissue (EAT) is a metabolically active fat depot associated with coronary atherosclerosis and cardiovascular (CV) risk. While EAT is a known prognostic marker in lung cancer screening, its sex-specific prognostic value remain...

Towards automated and reliable lung cancer detection in histopathological images using DY-FSPAN: A feature-summarized pyramidal attention network for explainable AI.

Computational biology and chemistry
Medical image classification is critical for accurate disease diagnosis, necessitating models that balance performance and interpretability. This study presents Dilated Y-Block-based Feature Summarized Pyramidal Attention Network (DY-FSPAN), a deep l...

Deciphering the molecular fingerprint of haemoglobin in lung cancer: A new strategy for early diagnosis using two-trace two-dimensional correlation near infrared spectroscopy (2T2D-NIRS) and machine learning techniques.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Lung cancer remains one of the deadliest malignancies worldwide, highlighting the need for highly sensitive and minimally invasive early diagnostic methods. Near-infrared spectroscopy (NIRS) offers unique advantages in probing molecular vibrational i...

Metabolic Adaptation Study of Tumor Cells during Lung Cancer Bone Metastasis in Mice Based on Single-Cell Metabolome Analysis.

Journal of the American Society for Mass Spectrometry
Lung cancer metastasis, the leading cause of patient mortality, is driven by circulating tumor cells (CTCs), which act as direct mediators of metastatic spread. To elucidate the metabolic heterogeneity across lung cancer metastatic stages, a panorami...

Advances in the Application of Three-Dimensional Reconstruction in Thoracic Surgery: A Comprehensive Review.

Thoracic cancer
This review presents a comprehensive overview of recent advancements and clinical applications of three-dimensional (3D) reconstruction technology in thoracic surgery, with a focus on lung cancer surgery. The widespread adoption of chest computed tom...

Knowledge-based trade-off prediction for NSCLC treatment planning using multi-output regression.

Medical physics
BACKGROUND: Knowledge-based planning (KBP) is a data-driven approach that utilizes the knowledge from previous high-quality treatment plans to predict dose-volume histogram (DVH) parameters for organs-at-risk (OARs) in new cases. Research has demonst...

From BERT to generative AI - Comparing encoder-only vs. large language models in a cohort of lung cancer patients for named entity recognition in unstructured medical reports.

Computers in biology and medicine
BACKGROUND: Extracting clinical entities from unstructured medical documents is critical for improving clinical decision support and documentation workflows. This study examines the performance of various encoder and decoder models trained for Named ...