AIMC Topic: Lung Neoplasms

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AI Applications for Thoracic Imaging: Considerations for Best Practice.

Radiology Feb 1, 2025
Artificial intelligence (AI) technology is rapidly being introduced into thoracic radiology practice. Current representative use cases for AI in thoracic imaging show cumulative evidence of effectiveness. These include AI assistance for reading chest...
Tomography, X-Ray Computed Lung Neoplasms Radiography, Thoracic Humans Artificial Intelligence
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Deep Learning Model for Predicting Immunotherapy Response in Advanced Non-Small Cell Lung Cancer.

JAMA oncology Feb 1, 2025
IMPORTANCE: Only a small fraction of patients with advanced non-small cell lung cancer (NSCLC) respond to immune checkpoint inhibitor (ICI) treatment. For optimal personalized NSCLC care, it is imperative to identify patients who are most likely to b...
Biomarkers, Tumor Male Middle Aged Deep Learning Humans Female Aged Carcinoma, Non-Small-Cell Lung Lymphocytes, Tumor-Infiltrating B7-H1 Antigen Immunotherapy Treatment Outcome Cohort Studies Immune Checkpoint Inhibitors Lung Neoplasms
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A novel machine learning-based cancer-specific cardiovascular disease risk score among patients with breast, colorectal, or lung cancer.

JNCI cancer spectrum Jan 3, 2025
BACKGROUND: Cancer patients have up to a 3-fold higher risk for cardiovascular disease (CVD) than the general population. Traditional CVD risk scores may be less accurate for them. We aimed to develop cancer-specific CVD risk scores and compare them ...
Area Under Curve Risk Assessment Lung Neoplasms Logistic Models Machine Learning Algorithms Female Breast Neoplasms Humans Male Aged Cardiovascular Diseases Adult Middle Aged Colorectal Neoplasms
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XLLC-Net: A lightweight and explainable CNN for accurate lung cancer classification using histopathological images.

PloS one Jan 1, 2025
Lung cancer imaging plays a crucial role in early diagnosis and treatment, where machine learning and deep learning have significantly advanced the accuracy and efficiency of disease classification. This study introduces the Explainable and Lightweig...
Humans Lung Neoplasms Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted
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Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma.

PeerJ Jan 1, 2025
BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer mortality. Considering the critical role of tumor infiltrating lymphocytes in effective immunotherapy, this study was designed to screen molecular markers related to tumor infiltrating...
Female Biomarkers, Tumor Male Humans Prognosis Immunotherapy Gene Expression Profiling Gene Expression Regulation, Neoplastic Transcriptome Lymphocytes, Tumor-Infiltrating Lung Neoplasms Adenocarcinoma of Lung
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Automated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT.

Radiology Jan 1, 2025
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician ...
Tomography, X-Ray Computed Lung Deep Learning Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Humans Retrospective Studies
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Immunoinfiltration Analysis of Mitochondrial Damage-Related Genes in Lung Adenocarcinoma and Construction of a Classification and Prognostic Model Integrated With WGCNA and Machine Learning Algorithms.

Cancer medicine Jan 1, 2025
BACKGROUND: Lung adenocarcinoma (LUAD) exhibits molecular heterogeneity, with mitochondrial damage affecting progression. The relationship between mitochondrial damage and immune infiltration, and Weighted Gene Co-expression Network Analysis (WGCNA)-...
Humans Computational Biology Gene Expression Profiling Machine Learning Gene Expression Regulation, Neoplastic Gene Regulatory Networks Mitochondria Prognosis Databases, Genetic Lung Neoplasms Adenocarcinoma of Lung Biomarkers, Tumor
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SVMVGGNet-16: A Novel Machine and Deep Learning Based Approaches for Lung Cancer Detection Using Combined SVM and VGGNet-16.

Current medical imaging Jan 1, 2025
BACKGROUND AND OBJECTIVE: Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating early and accurate detection methods. Our study aims to enhance lung cancer detection by integrating VGGNet-16 form of Convolutional Ne...
Convolutional Neural Networks Lung Neoplasms Support Vector Machine Tomography, X-Ray Computed Reproducibility of Results Deep Learning Humans Neural Networks, Computer
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Lightweight Lung-nodule Detection Model Combined with Multidimensional Attention Convolution.

Current medical imaging Jan 1, 2025
BACKGROUND: Early and timely detection of pulmonary nodules and initiation treatment can substantially improve the survival rate of lung carcinoma. However, current detection methods based on convolutional neural networks (CNNs) cannot easily detect ...
Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Tomography, X-Ray Computed Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule
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Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT-Based Radiomic Features in Non-Small Cell Lung Cancer.

Cancer medicine Dec 1, 2024
BACKGROUND: Non-small-cell lung cancer (NSCLC) remains a global health challenge, driving morbidity and mortality. The emerging field of radiogenomics utilizes statistical methods to correlate radiographic tumor features with genomic characteristics ...
Radiomics Gene Expression Regulation, Neoplastic Carcinoma, Non-Small-Cell Lung Tomography, X-Ray Computed Middle Aged Biomarkers, Tumor Gene Expression Profiling Male Humans Female Lung Neoplasms Aged Machine Learning Retrospective Studies
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