AIMC Topic: Lung Neoplasms

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Artificial intelligence for early detection of lung cancer in GPs' clinical notes: a retrospective observational cohort study.

The British journal of general practice : the journal of the Royal College of General Practitioners May 2, 2025
BACKGROUND: The journey of >80% of patients diagnosed with lung cancer starts in general practice. About 75% of patients are diagnosed when it is at an advanced stage (3 or 4), leading to >80% mortality within 1 year at present. The long-term data in...
Early Detection of Cancer Natural Language Processing Netherlands General Practice Retrospective Studies Electronic Health Records Female Aged Middle Aged Risk Assessment Lung Neoplasms Sensitivity and Specificity Male Humans Artificial Intelligence Machine Learning Algorithms
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Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.

BMC cancer May 1, 2025
BACKGROUND: Epidermal growth factor receptor (EGFR) mutations are present in 10-60% of all non-small cell lung cancer (NSCLC) patients and are associated with dismal prognosis. Lung cancer brain metastases (LCBM) are a common complication of lung can...
Lung Neoplasms Carcinoma, Non-Small-Cell Lung Mutation ErbB Receptors Machine Learning Humans Prognosis Brain Neoplasms
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Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.

Journal of thoracic imaging May 1, 2025
PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor ( EGFR ) and TP53 mutations and to assess the models' capacities t...
Humans Retrospective Studies Tomography, X-Ray Computed Female Radiomics Male Middle Aged Lung Deep Learning Reproducibility of Results Aged Adult Sensitivity and Specificity Adenocarcinoma of Lung Lung Neoplasms Mutation Tumor Suppressor Protein p53 ErbB Receptors
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Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.

Journal of thoracic imaging May 1, 2025
PURPOSE: To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).
Solitary Pulmonary Nodule Tomography, X-Ray Computed Female Radiographic Image Interpretation, Computer-Assisted Lung Aged Radiation Dosage Deep Learning Radiography, Thoracic Aged, 80 and over Lung Neoplasms Multiple Pulmonary Nodules Cross-Sectional Studies Adult Humans Male Algorithms Sensitivity and Specificity Middle Aged Prospective Studies
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Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.

Journal of translational medicine Apr 29, 2025
BACKGROUND: Ttyrosine kinase inhibitors (TKIs) represent the standard first-line treatment for patients with epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma. However, not all patients with EGFR mutations respond to TKIs. This study...
Humans Adenocarcinoma of Lung Mutation Retrospective Studies Tomography, X-Ray Computed Molecular Targeted Therapy Middle Aged Aged Female ErbB Receptors Deep Learning Male Protein Kinase Inhibitors Lung Neoplasms
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Development of an artificial intelligence powered software for automated analysis of skeletal muscle ultrasonography.

Scientific reports Apr 29, 2025
Muscle ultrasound has high utility in clinical practice and research; however, the main challenges are the training and time required for manual analysis to achieve objective quantification of muscle size and quality. We aimed to develop and validate...
Muscle, Skeletal Quadriceps Muscle Humans Artificial Intelligence Software Middle Aged Deep Learning Female Ultrasonography Aged Image Processing, Computer-Assisted Adult Lung Neoplasms Male
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F-FDG PET/CT-based deep learning models and a clinical-metabolic nomogram for predicting high-grade patterns in lung adenocarcinoma.

BMC medical imaging Apr 28, 2025
BACKGROUND: To develop and validate deep learning (DL) and traditional clinical-metabolic (CM) models based on 18 F-FDG PET/CT images for noninvasively predicting high-grade patterns (HGPs) of invasive lung adenocarcinoma (LUAD).
Deep Learning Lung Neoplasms Nomograms Positron Emission Tomography Computed Tomography Fluorodeoxyglucose F18 Adenocarcinoma of Lung Neoplasm Grading Radiopharmaceuticals Female Aged Humans Male Retrospective Studies Middle Aged
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Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction.

Frontiers in public health Apr 25, 2025
INTRODUCTION: Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and time factors. Machine learning offers powerful tools...
Incidence Humans Machine Learning Lung Neoplasms Forecasting United States Public Health Support Vector Machine
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Analyzing factors influencing hospitalization costs for five common cancers in China using neural network models.

Journal of medical economics Apr 24, 2025
BACKGROUND: Malignant tumors are a major global health crisis, causing 25% of deaths in China, with lung, liver, thyroid, breast, and colon cancers being the most common. Understanding the factors influencing hospitalization costs for these cancers i...
Humans China Thyroid Neoplasms ROC Curve Lung Neoplasms Adult Neural Networks, Computer Male Breast Neoplasms Colonic Neoplasms Female Middle Aged Neoplasms Hospitalization Aged
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Unsupervised non-small cell lung cancer tumor segmentation using cycled generative adversarial network with similarity-based discriminator.

Journal of applied clinical medical physics Apr 23, 2025
BACKGROUND: Tumor segmentation is crucial for lung disease diagnosis and treatment. Most existing deep learning-based automatic segmentation methods rely on manually annotated data for network training.
Humans Image Processing, Computer-Assisted Lung Neoplasms Carcinoma, Non-Small-Cell Lung Unsupervised Machine Learning Generative Adversarial Networks Neural Networks, Computer Algorithms Tomography, X-Ray Computed Deep Learning
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