AIMC Topic: Lung Neoplasms
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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.
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 ...
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...
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...
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.
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...
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...
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...
Leveraging Artificial Intelligence as a Safety Net for Incidentally Identified Lung Nodules at a Tertiary Center.
Journal of the American College of Surgeons
Mar 17, 2025
BACKGROUND: Artificial intelligence (AI)-powered platforms may be used to ensure that clinically significant lung nodules receive appropriate management. We studied the impact of a commercially available AI natural language processing tool on the det...
DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT.
IEEE transactions on medical imaging
Mar 17, 2025
4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projection data will cause severe streak artifacts in 4D CBCT images. Existing deep learning (DL) methods ...
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