AIMC Topic: Lung Neoplasms

Clear Filters Showing 361 to 370 of 1778 articles

CNN-O-ELMNet: Optimized Lightweight and Generalized Model for Lung Disease Classification and Severity Assessment.

IEEE transactions on medical imaging Dec 2, 2024
The high burden of lung diseases on healthcare necessitates effective detection methods. Current Computer-aided design (CAD) systems are limited by their focus on specific diseases and computationally demanding deep learning models. To overcome these...
Image Interpretation, Computer-Assisted Severity of Illness Index Deep Learning Lung Neoplasms Lung Diseases Neural Networks, Computer Humans Algorithms Lung
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Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.

IEEE transactions on neural networks and learning systems Dec 2, 2024
Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeli...
Humans Neural Networks, Computer COVID-19 Algorithms Image Processing, Computer-Assisted Lung Neoplasms Solitary Pulmonary Nodule Databases, Factual Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Supervised Machine Learning SARS-CoV-2 Lung
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External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan.

Japanese journal of radiology Nov 30, 2024
PURPOSE: Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the co...
Humans Early Detection of Cancer Radiographic Image Interpretation, Computer-Assisted Japan Radiation Dosage Aged, 80 and over Deep Learning Sensitivity and Specificity Lung Neoplasms Male Multiple Pulmonary Nodules Middle Aged Solitary Pulmonary Nodule Lung Retrospective Studies Tomography, X-Ray Computed Female Aged
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Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.

Journal of applied clinical medical physics Nov 29, 2024
BACKGROUND: Lung cancer poses a significant global health challenge. Adaptive radiotherapy (ART) addresses uncertainties due to lung tumor dynamics. We aimed to investigate a comprehensively and systematically validated offline ART regimen with high ...
Middle Aged Radiotherapy, Intensity-Modulated Deep Learning Cone-Beam Computed Tomography Image Processing, Computer-Assisted Organs at Risk Humans Radiotherapy Planning, Computer-Assisted Lung Neoplasms Female Radiotherapy Dosage Feasibility Studies Aged Male
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Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma.

IUBMB life Nov 29, 2024
Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), highlighting the need for alternative therapies. This study explores the genetic basis of GR to improve prediction, prevention, and treatment strategie...
Humans Machine Learning Prognosis Tumor Microenvironment Gene Expression Profiling Transcriptome Lung Neoplasms Biomarkers, Tumor Antineoplastic Agents Adenocarcinoma of Lung Multiomics Gene Expression Regulation, Neoplastic Drug Resistance, Neoplasm Gefitinib
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Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.

Frontiers in immunology Nov 28, 2024
BACKGROUND: Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.
Humans Gene Expression Regulation, Neoplastic Machine Learning Mutation Male Female Precision Medicine Adenocarcinoma of Lung Gene Expression Profiling Prognosis Biomarkers, Tumor Lung Neoplasms Genomics Computational Biology Immunotherapy Multiomics
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Predicting Survival and Recurrence of Lung Ablation Patients Using Deep Learning-Based Automatic Segmentation and Radiomics Analysis.

Cardiovascular and interventional radiology Nov 27, 2024
PURPOSE: To predict survival and tumor recurrence following image-guided thermal ablation (IGTA) of lung tumors segmented using a deep learning approach.
Middle Aged Lung Deep Learning Lung Neoplasms Ablation Techniques Surgery, Computer-Assisted Neoplasm Recurrence, Local Female Aged Radiomics Male Humans Retrospective Studies Tomography, X-Ray Computed
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A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.

The Thoracic and cardiovascular surgeon Nov 26, 2024
BACKGROUND:  Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrat...
Humans Artificial Intelligence Decision Support Techniques Retrospective Studies Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Neoplasm Invasiveness Aged Tomography, X-Ray Computed Predictive Value of Tests Biomarkers, Tumor Female Male Reproducibility of Results Middle Aged Risk Factors
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Application of NotebookLM, a large language model with retrieval-augmented generation, for lung cancer staging.

Japanese journal of radiology Nov 25, 2024
PURPOSE: In radiology, large language models (LLMs), including ChatGPT, have recently gained attention, and their utility is being rapidly evaluated. However, concerns have emerged regarding their reliability in clinical applications due to limitatio...
Lung Neoplasms Humans Tomography, X-Ray Computed Neoplasm Staging Reproducibility of Results Japan Lung Large Language Models
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Effect of Artificial Intelligence as a Second Reader on the Lung Nodule Detection and Localization Accuracy of Radiologists and Non-radiology Physicians in Chest Radiographs: A Multicenter Reader Study.

Academic radiology Nov 25, 2024
RATIONALE AND OBJECTIVES: Missed nodules in chest radiographs (CXRs) are common occurrences. We assessed the effect of artificial intelligence (AI) as a second reader on the accuracy of radiologists and non-radiology physicians in lung nodule detecti...
Aged Sensitivity and Specificity Male Middle Aged Lung Neoplasms Retrospective Studies Tomography, X-Ray Computed Female Reproducibility of Results Radiography, Thoracic Solitary Pulmonary Nodule Radiologists Radiographic Image Interpretation, Computer-Assisted Observer Variation Humans Artificial Intelligence
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