AIMC Topic: Lung Neoplasms

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Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.

Frontiers in immunology Jun 28, 2024
INTRODUCTION: Deep learning (DL) models predicting biomarker expression in images of hematoxylin and eosin (H&E)-stained tissues can improve access to multi-marker immunophenotyping, crucial for therapeutic monitoring, biomarker discovery, and person...
T-Lymphocytes Humans Lung Neoplasms Male Female Staining and Labeling Biomarkers, Tumor Deep Learning Immunophenotyping
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Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.

Functional & integrative genomics Jun 27, 2024
Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 1...
Prognosis Tumor Microenvironment Biomarkers, Tumor Humans Machine Learning Multiomics Lung Neoplasms Gene Expression Regulation, Neoplastic Adenocarcinoma of Lung Genomics Immunotherapy
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Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis.

Scientific reports Jun 26, 2024
Detecting aberrant cell-free DNA (cfDNA) methylation is a promising strategy for lung cancer diagnosis. In this study, our aim is to identify methylation markers to distinguish patients with lung cancer from healthy individuals. Additionally, we soug...
Deep Learning ROC Curve Lung Neoplasms Biomarkers, Tumor DNA Methylation Humans Male Cell-Free Nucleic Acids Female Middle Aged Aged
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Development of a machine learning-based risk model for postoperative complications of lung cancer surgery.

Surgery today Jun 19, 2024
PURPOSE: To develop a comorbidity risk score specifically for lung resection surgeries.
Pneumonectomy Risk Comorbidity ROC Curve Lung Neoplasms Risk Factors Aged Male Middle Aged Risk Assessment Postoperative Complications Humans Machine Learning Female
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Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy.

Computer methods and programs in biomedicine Jun 19, 2024
BACKGROUND AND OBJECTIVE: To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve radiother...
Humans Lung Neoplasms Multiomics Radiotherapy Dosage Male Radiotherapy, Intensity-Modulated Tomography, X-Ray Computed Middle Aged Radiation Pneumonitis Female Deep Learning Aged
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A Multiscale Connected UNet for the Segmentation of Lung Cancer Cells in Pathology Sections Stained Using Rapid On-Site Cytopathological Evaluation.

The American journal of pathology Jun 17, 2024
Lung cancer is an increasingly serious health problem worldwide, and early detection and diagnosis are crucial for successful treatment. With the development of artificial intelligence and the growth of data volume, machine learning techniques can pl...
Deep Learning Lung Neoplasms Endoscopic Ultrasound-Guided Fine Needle Aspiration Humans Algorithms
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Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.

Nature medicine Jun 14, 2024
In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (T...
Humans Neoplasms Lung Neoplasms Polymorphism, Single Nucleotide DNA Copy Number Variations Machine Learning Colorectal Neoplasms Biomarkers, Tumor Circulating Tumor DNA Whole Genome Sequencing Tumor Burden Neoplasm, Residual
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Decoding temporal heterogeneity in NSCLC through machine learning and prognostic model construction.

World journal of surgical oncology Jun 13, 2024
BACKGROUND: Non-small cell lung cancer (NSCLC) is a prevalent and heterogeneous disease with significant genomic variations between the early and advanced stages. The identification of key genes and pathways driving NSCLC tumor progression is critica...
Gene Expression Regulation, Neoplastic Carcinoma, Non-Small-Cell Lung Transcriptome Single-Cell Analysis Lung Neoplasms Prognosis Humans Machine Learning Female Male Biomarkers, Tumor Disease Progression Gene Expression Profiling
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The efficacy of machine learning models in lung cancer risk prediction with explainability.

PloS one Jun 13, 2024
Among many types of cancers, to date, lung cancer remains one of the deadliest cancers around the world. Many researchers, scientists, doctors, and people from other fields continuously contribute to this subject regarding early prediction and diagno...
Lung Neoplasms Humans Machine Learning Risk Assessment
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Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks.

Journal of imaging informatics in medicine Jun 11, 2024
Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadliest malignant tumors worldwide. Given the increased emphasis on the precise management of lung cancer, identifying various subtypes of NSCLC has becom...
Humans Deep Learning Neural Networks, Computer Imaging, Three-Dimensional Tomography, X-Ray Computed Lung Neoplasms Radiomics Carcinoma, Non-Small-Cell Lung
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