AIMC Topic: Lung Neoplasms

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Exploring non-invasive biomarkers for pulmonary nodule detection based on salivary microbiomics and machine learning algorithms.

Scientific reports Mar 29, 2025
Microorganisms are one of the most promising biomarkers for cancer, and the relationship between microorganisms and lung cancer occurrence and development provides significant potential for pulmonary nodule (PN) diagnosis from a microbiological persp...
Saliva Microbiota Humans Male Middle Aged Case-Control Studies Lung Neoplasms RNA, Ribosomal, 16S Female Solitary Pulmonary Nodule Aged Bayes Theorem Adult Machine Learning Biomarkers, Tumor Algorithms Biomarkers
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The Role of Eosinophils, Eosinophil-Related Cytokines and AI in Predicting Immunotherapy Efficacy in NSCLC Cancer.

Biomolecules Mar 27, 2025
Immunotherapy and chemoimmunotherapy are standard treatments for non-oncogene-addicted advanced non-small cell lung cancer (NSCLC). Currently, a limited number of biomarkers, including programmed death-ligand 1 (PD-L1) expression, microsatellite inst...
Eosinophils Immune Checkpoint Inhibitors Artificial Intelligence Humans Lung Neoplasms Cytokines Immunotherapy Biomarkers, Tumor Carcinoma, Non-Small-Cell Lung
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Preoperative Prediction of STAS Risk in Primary Lung Adenocarcinoma Using Machine Learning: An Interpretable Model with SHAP Analysis.

Academic radiology Mar 25, 2025
BACKGROUND: Accurate preoperative prediction of spread through air spaces (STAS) in primary lung adenocarcinoma (LUAD) is critical for optimizing surgical strategies and improving patient outcomes.
Machine Learning Retrospective Studies Tomography, X-Ray Computed Middle Aged Aged Humans Female Risk Assessment Sensitivity and Specificity Male Lung Neoplasms Adenocarcinoma of Lung
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Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment.

Medical oncology (Northwood, London, England) Mar 25, 2025
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to challenges in early detection, suboptimal therapeutic efficacy, and severe adverse effects associated with conventional treatments. The convergence ...
Nanomedicine Artificial Intelligence Precision Medicine Drug Delivery Systems Lung Neoplasms Humans
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A tumor-infiltrating B lymphocytes -related index based on machine-learning predicts prognosis and immunotherapy response in lung adenocarcinoma.

Frontiers in immunology Mar 24, 2025
INTRODUCTION: Tumor-infiltrating B lymphocytes (TILBs) play a pivotal role in shaping the immune microenvironment of tumors (TIME) and in the progression of lung adenocarcinoma (LUAD). However, there remains a scarcity of research that has thoroughly...
Adenocarcinoma of Lung Male Middle Aged Lung Neoplasms Female Humans Immunotherapy Aged Treatment Outcome Tumor Microenvironment Machine Learning Lymphocytes, Tumor-Infiltrating Biomarkers, Tumor Prognosis B-Lymphocytes
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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 Mar 23, 2025
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...
Case-Control Studies Spectroscopy, Near-Infrared Lung Neoplasms Early Detection of Cancer Hemoglobins Wavelet Analysis Female Aged Male Middle Aged Humans Machine Learning
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Ontology-driven identification of inconsistencies in clinical data: A case study in lung cancer phenotyping.

Journal of biomedical informatics Mar 21, 2025
OBJECTIVE: To illustrate the use of an ontology in evaluating data quality in the medical field, focusing on phenotyping lung cancers.
Anaplastic Lymphoma Kinase ErbB Receptors Protein Kinase Inhibitors Data Warehousing Humans Phenotype Lung Neoplasms Mutation Data Accuracy Biological Ontologies
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Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy.

Scientific reports Mar 21, 2025
New bronchoscopy techniques like radial probe endobronchial ultrasound have been developed for real-time sampling characterization, but their use is still limited. This study aims to use classification algorithms with minimally invasive electrical im...
Humans Machine Learning Algorithms ROC Curve Lung Neoplasms Support Vector Machine Bronchoscopy Female Male Middle Aged Biopsy Decision Trees Dielectric Spectroscopy Bayes Theorem Discriminant Analysis
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Development and validation of a machine learning-based nomogram for predicting prognosis in lung cancer patients with malignant pleural effusion.

Scientific reports Mar 21, 2025
Malignant pleural effusion (MPE) is a common complication in patients with advanced lung cancer, significantly impacting their survival rates and quality of life. Effective tools for assessing the prognosis of these patients are urgently needed to en...
Nomograms Pleural Effusion, Malignant Prognosis Retrospective Studies Female Humans Aged Machine Learning Male Middle Aged Lung Neoplasms
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Ensemble-based multiclass lung cancer classification using hybrid CNN-SVD feature extraction and selection method.

PloS one Mar 19, 2025
Lung cancer (LC) is a leading cause of cancer-related fatalities worldwide, underscoring the urgency of early detection for improved patient outcomes. The main objective of this research is to harness the noble strategies of artificial intelligence f...
Algorithms Tomography, X-Ray Computed Lung Neoplasms Humans Neural Networks, Computer
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