AIMC Topic: Adenocarcinoma of Lung

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A vision transformer-based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma.

Medical physics Sep 28, 2024
BACKGROUND: Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; however, the ability of chest computed tomography (CT) imaging to detect LNM status is limited.
Image Processing, Computer-Assisted Lung Neoplasms Nomograms Lymphatic Metastasis Adenocarcinoma of Lung Aged Male Middle Aged Deep Learning Humans Tomography, X-Ray Computed Female
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Development and validation of machine learning models for diagnosis and prognosis of lung adenocarcinoma, and immune infiltration analysis.

Scientific reports Sep 27, 2024
The aim of our study was to develop robust diagnostic and prognostic models for lung adenocarcinoma (LUAD) using machine learning (ML) techniques, focusing on early immune infiltration. Feature selection was performed on The Cancer Genome Atlas (TCGA...
Lung Neoplasms Gene Expression Regulation, Neoplastic Support Vector Machine Lymphocytes, Tumor-Infiltrating Prognosis Biomarkers, Tumor Gene Expression Profiling ROC Curve Adenocarcinoma of Lung Humans Machine Learning Algorithms
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Integrating machine learning and multi-omics analysis to develop an asparagine metabolism immunity index for improving clinical outcome and drug sensitivity in lung adenocarcinoma.

Immunologic research Sep 25, 2024
Lung adenocarcinoma (LUAD) is a malignancy affecting the respiratory system. Most patients are diagnosed with advanced or metastatic lung cancer due to the fact that most of their clinical symptoms are insidious, resulting in a bleak prognosis. Given...
Female Aged Biomarkers, Tumor Male Humans Machine Learning Prognosis Middle Aged Gene Expression Regulation, Neoplastic Antineoplastic Agents Drug Resistance, Neoplasm Lung Neoplasms Asparagine Multiomics Adenocarcinoma of Lung
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Radiomics-Based Support Vector Machine Distinguishes Molecular Events Driving the Progression of Lung Adenocarcinoma.

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer Sep 19, 2024
INTRODUCTION: An increasing number of early-stage lung adenocarcinomas (LUAD) are detected as lung nodules. The radiological features related to LUAD progression warrant further investigation. Exploration is required to bridge the gap between radiomi...
Disease Progression Lung Neoplasms Support Vector Machine Adenocarcinoma of Lung Humans Prognosis Aged Tomography, X-Ray Computed Radiomics Female Male Middle Aged
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Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.

BMC medical imaging Sep 13, 2024
BACKGROUND: To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma.
Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Male Middle Aged Lung Neoplasms Female Neoplasm Invasiveness Aged Adenocarcinoma of Lung Sensitivity and Specificity Radiomics
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Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort.

Frontiers in immunology Sep 13, 2024
INTRODUCTION: The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies...
Multiomics Gene Expression Regulation, Neoplastic Apoptosis Adenocarcinoma of Lung Biomarkers, Tumor Humans Male Machine Learning Lung Neoplasms Prognosis Computational Biology Female
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Integrated machine learning survival framework to decipher diverse cell death patterns for predicting prognosis in lung adenocarcinoma.

Genes and immunity Aug 31, 2024
Various forms of programmed cell death (PCD) collectively regulate the occurrence, development and metastasis of tumors. Nevertheless, a comprehensive analysis of the diverse types of PCD in lung adenocarcinoma (LUAD) is currently lacking. The study ...
Humans Machine Learning Prognosis Cell Death Female Adenocarcinoma of Lung Male Lung Neoplasms
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Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning.

International journal of molecular sciences Aug 31, 2024
Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristics and high recurrence rate. The high recurrence rate of LUAD is closely associated with cellular dormancy, which enhances resistance to chemotherapy a...
Glycosylation Humans Adenocarcinoma of Lung Biomarkers, Tumor Machine Learning Gene Regulatory Networks Prognosis Lung Neoplasms Tumor Microenvironment Gene Expression Regulation, Neoplastic
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Machine Learning Based on Clinical Information and Integrated CT Radiomics to Predict Local Recurrence of Stage Ia Lung Adenocarcinoma after Microwave Ablation.

Journal of vascular and interventional radiology : JVIR Aug 28, 2024
PURPOSE: To develop and compare 3 different machine learning-based models of clinical information and integrated radiomics features predicting the local recurrence of Stage Ia lung adenocarcinoma after microwave ablation (MWA) for assisting clinical ...
Male Middle Aged Risk Assessment Time Factors Lung Neoplasms Treatment Outcome Predictive Value of Tests Neoplasm Staging Female Humans Risk Factors Decision Support Techniques Machine Learning Aged Adenocarcinoma of Lung Retrospective Studies Radiomics Radiographic Image Interpretation, Computer-Assisted Ablation Techniques Neoplasm Recurrence, Local Tomography, X-Ray Computed Microwaves Clinical Decision-Making Margins of Excision
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An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.

Nature communications Aug 20, 2024
Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present ...
Female Adenocarcinoma of Lung Reproducibility of Results Male Middle Aged Mass Spectrometry Deep Learning Humans Metabolomics Lung Neoplasms Metabolome Data Analysis
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