AIMC Topic: Lung Neoplasms

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A series of natural language processing for predicting tumor response evaluation and survival curve from electronic health records.

BMC medical informatics and decision making Feb 17, 2025
BACKGROUND: The clinical information housed within unstructured electronic health records (EHRs) has the potential to promote cancer research. The National Cancer Center Hospital (NCCH) is widely recognized as a leading institution for the treatment ...
Japan Survival Analysis Humans Electronic Health Records Antineoplastic Agents Lung Neoplasms Natural Language Processing
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Prior knowledge-based multi-task learning network for pulmonary nodule classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Feb 16, 2025
The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for classification of benign and malignant nodules. In clinical, radiologists usually conduct a comprehensive analysis of correlations between different ...
Radiographic Image Interpretation, Computer-Assisted Humans Tomography, X-Ray Computed Neural Networks, Computer Lung Neoplasms Machine Learning Solitary Pulmonary Nodule
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Unveiling the crucial role of glycosylation modification in lung adenocarcinoma metastasis through artificial neural network-based spatial multi-omics single-cell analysis and Mendelian randomization.

BMC cancer Feb 13, 2025
BACKGROUND: Investigations into the intricacies of glycosylation modifications, a prevalent post-translational alteration observed in neoplasms, especially remain elusive in the context of lung adenocarcinoma. Through the integration of multiple omic...
Mendelian Randomization Analysis Neural Networks, Computer Adenocarcinoma of Lung Gene Expression Profiling Gene Expression Regulation, Neoplastic Transcriptome Humans Glycosylation Single-Cell Analysis Protein Processing, Post-Translational Lung Neoplasms Multiomics
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Artificial intelligence-assisted point-of-care devices for lung cancer.

Clinica chimica acta; international journal of clinical chemistry Feb 11, 2025
Lung cancer is the leading cause of cancer-related deaths worldwide, primarily due to late-stage detection, which limits treatment options. Early detection and screening can increase survival rates, but traditional medical imaging methods are costly ...
Lung Neoplasms Biosensing Techniques Early Detection of Cancer Artificial Intelligence Biomarkers, Tumor Point-of-Care Systems Humans
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Deep learning paradigms in lung cancer diagnosis: A methodological review, open challenges, and future directions.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Feb 11, 2025
Lung cancer is the leading cause of global cancer-related deaths, which emphasizes the critical importance of early diagnosis in enhancing patient outcomes. Deep learning has demonstrated significant promise in lung cancer diagnosis, excelling in nod...
Humans Neural Networks, Computer Diagnosis, Computer-Assisted Deep Learning Lung Neoplasms
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Multi-modality medical image classification with ResoMergeNet for cataract, lung cancer, and breast cancer diagnosis.

Computers in biology and medicine Feb 11, 2025
The variability in image modalities presents significant challenges in medical image classification, as traditional deep learning models often struggle to adapt to different image types, leading to suboptimal performance across diverse datasets. This...
Humans Deep Learning Female Cataract Lung Neoplasms Breast Neoplasms Image Interpretation, Computer-Assisted Multimodal Imaging
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Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study.

Journal of cardiothoracic surgery Feb 11, 2025
BACKGROUND: Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + ...
Tomography, X-Ray Computed Lung Neoplasms Female Neoplasm Invasiveness Aged Male Adenocarcinoma of Lung Machine Learning Humans Radiomics Middle Aged Prognosis Cross-Sectional Studies Retrospective Studies
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Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.

Journal of medical systems Feb 11, 2025
This study aims to apply a multi-modal approach of the deep learning method for survival prediction in patients with non-small-cell lung cancer (NSCLC) using CT-based radiomics. We utilized two public data sets from the Cancer Imaging Archive (TCIA) ...
Aged Radiomics Male Neural Networks, Computer Tomography, X-Ray Computed Female Carcinoma, Non-Small-Cell Lung Proportional Hazards Models Middle Aged Deep Learning Lung Neoplasms Humans
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Differential gene expression analysis and machine learning identified structural, TFs, cytokine and glycoproteins, including SOX2, TOP2A, SPP1, COL1A1, and TIMP1 as potential drivers of lung cancer.

Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals Feb 10, 2025
BACKGROUND: Lung cancer is a primary global health concern, responsible for a considerable portion of cancer-related fatalities worldwide. Understanding its molecular complexities is crucial for identifying potential targets for treatment. The goal i...
Gene Regulatory Networks Lung Neoplasms Gene Expression Regulation, Neoplastic Biomarkers, Tumor Gene Expression Profiling Protein Interaction Maps Humans SOXB1 Transcription Factors Poly-ADP-Ribose Binding Proteins Machine Learning Collagen Type I, alpha 1 Chain Osteopontin DNA Topoisomerases, Type II Collagen Type I Tissue Inhibitor of Metalloproteinase-1
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Identifying invasiveness to aid lung adenocarcinoma diagnosis using deep learning and pathomics.

Scientific reports Feb 10, 2025
Most classification efforts for primary subtypes of lung adenocarcinoma (LUAD) have not yet been integrated into clinical practice. This study explores the feasibility of combining deep learning and pathomics to identify tumor invasiveness in LUAD pa...
Humans Retrospective Studies Lung Neoplasms Neoplasm Invasiveness Area Under Curve Adenocarcinoma of Lung Middle Aged Deep Learning ROC Curve Female Aged Male
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