AIMC Topic: Adenocarcinoma of Lung

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Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.

The journal of pathology. Clinical research Nov 1, 2024
EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitati...
Humans Female Image Interpretation, Computer-Assisted DNA Mutational Analysis ErbB Receptors Adenocarcinoma of Lung Deep Learning Lung Neoplasms Mutation
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Establishment of artificial intelligence model for precise histological subtyping of lung adenocarcinoma and its application to quantitative and spatial analysis.

Japanese journal of clinical oncology Sep 4, 2024
BACKGROUND: The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify lung adenocarcinoma images into seven histological subtypes and adopted the model for whole-slide i...
Spatial Analysis Artificial Intelligence Adenocarcinoma of Lung Prognosis Female Middle Aged Lung Neoplasms Adult Adenocarcinoma Male Aged Humans Aged, 80 and over
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Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.

Journal of cellular and molecular medicine Jul 1, 2024
Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approaches, especially in the context of immunotherapy. In this study, we constructed an artificial intellig...
Neoplastic Stem Cells Cell Movement Adenocarcinoma of Lung Humans CDC2-CDC28 Kinases Biomarkers, Tumor Immunotherapy Gene Expression Regulation, Neoplastic Transcriptome Gene Expression Profiling Cell Proliferation Cell Line, Tumor Artificial Intelligence Lung Neoplasms Prognosis
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Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

Journal of cellular and molecular medicine Jul 1, 2024
The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cel...
Gene Expression Profiling Single-Cell Analysis Lung Neoplasms Immunotherapy Tumor Microenvironment Biomarkers, Tumor Disease Progression Gene Expression Regulation, Neoplastic Adenocarcinoma of Lung Machine Learning Prognosis Humans
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Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Journal of cellular and molecular medicine Jul 1, 2024
Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mR...
Machine Learning Prognosis Biomarkers, Tumor Gene Expression Profiling Humans Lung Neoplasms Gene Expression Regulation, Neoplastic Adenocarcinoma of Lung Immunotherapy MicroRNAs Multiomics
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[Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Apr 25, 2024
Computed tomography (CT) imaging is a vital tool for the diagnosis and assessment of lung adenocarcinoma, and using CT images to predict the recurrence-free survival (RFS) of lung adenocarcinoma patients post-surgery is of paramount importance in tai...
Humans Neural Networks, Computer Tomography, X-Ray Computed Disease-Free Survival Adenocarcinoma of Lung Lung Neoplasms Neoplasm Recurrence, Local
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Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Radiology Apr 1, 2024
Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improv...
Adenocarcinoma Adenocarcinoma of Lung Female Middle Aged Deep Learning Lung Neoplasms Humans Retrospective Studies Tomography, X-Ray Computed
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Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier.

Technology in cancer research & treatment Jan 1, 2024
INTRODUCTION: This study aimed to devise a diagnostic algorithm, termed the Refined Radiomics and Deep Learning Features-Guided CatBoost Classifier (RRDLC-Classifier), and evaluate its efficacy in predicting pathological high-grade patterns in patien...
Adenocarcinoma of Lung Aged Deep Learning Male Retrospective Studies Humans Adult Tomography, X-Ray Computed Lung Neoplasms Middle Aged Radiomics Female Neoplasm Staging Neoplasm Grading Algorithms Prognosis ROC Curve
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Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma.

Current medicinal chemistry Jan 1, 2024
BACKGROUND: Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with cellular damage, heralding new avenues for exploring tumor cell dynamics. We aimed to explore the impact of disulfide cell death on the tumor immune mi...
Humans Multiomics Adenocarcinoma of Lung Tumor Microenvironment Machine Learning Apoptosis Lung Neoplasms Prognosis Immunotherapy
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Supervised training models with or without manual lesion delineation outperform clinicians in distinguishing pulmonary cryptococcosis from lung adenocarcinoma on chest CT.

Mycoses Jan 1, 2024
BACKGROUND: The role of artificial intelligence (AI) in the discrimination between pulmonary cryptococcosis (PC) and lung adenocarcinoma (LA) warrants further research.
Deep Learning Lung Neoplasms Adenocarcinoma of Lung Artificial Intelligence Retrospective Studies Tomography, X-Ray Computed Humans
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