AIMC Topic: Adenocarcinoma of Lung

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Predicting potential residues associated with lung cancer using deep neural network.

Mutation research Dec 11, 2020
Lung cancer is a prominent type of cancer, which leads to high mortality rate worldwide. The major lung cancers lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) occur mainly due to somatic driver mutations in proteins and screening of su...
Neoplasm Proteins Adenocarcinoma of Lung Humans Deep Learning Lung Neoplasms Models, Genetic
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Immune profile of the tumor microenvironment and the identification of a four-gene signature for lung adenocarcinoma.

Aging Dec 9, 2020
The composition and relative abundances of immune cells in the tumor microenvironment are key factors affecting the progression of lung adenocarcinomas (LUADs) and the efficacy of immunotherapy. Using the cancer gene expression dataset from The Cance...
Female Aged Gene Ontology Tumor Microenvironment Adenocarcinoma of Lung Humans Biomarkers, Tumor Receptors, CCR4 Prognosis Male Middle Aged Receptors, Purinergic P2 Receptors, Purinergic P2Y12 Lung Neoplasms Gene Expression Regulation, Neoplastic Receptors, CCR2
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Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data.

BMC genomics Sep 22, 2020
BACKGROUND: The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far ...
Lung Neoplasms Genomics Cluster Analysis Supervised Machine Learning Adenocarcinoma of Lung Humans Software Biomarkers, Tumor
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A Simple Method to Train the AI Diagnosis Model of Pulmonary Nodules.

Computational and mathematical methods in medicine Aug 1, 2020
BACKGROUND: The differential diagnosis of subcentimetre lung nodules with a diameter of less than 1 cm has always been one of the problems of imaging doctors and thoracic surgeons. We plan to create a deep learning model for the diagnosis of pulmonar...
Solitary Pulmonary Nodule Deep Learning Humans Expert Testimony Lung Neoplasms Artificial Intelligence Adenocarcinoma of Lung Carcinoma, Non-Small-Cell Lung Diagnosis, Differential Databases, Factual Multiple Pulmonary Nodules Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Expert Systems
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Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.

Nature communications Jul 16, 2020
Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address...
Humans Machine Learning Sensitivity and Specificity Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Adenocarcinoma of Lung Biomarkers, Tumor Lung Neoplasms Early Detection of Cancer Metabolomics
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Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.

European radiology Jun 27, 2020
OBJECTIVES: To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (...
Solitary Pulmonary Nodule Regression Analysis Diagnosis, Differential Granuloma Tuberculosis Lung Neoplasms Logistic Models Nomograms Age Factors Calibration Adenocarcinoma of Lung Sex Factors Diagnostic Tests, Routine Observer Variation Pattern Recognition, Automated Tomography, X-Ray Computed Female Adult Male Middle Aged Diagnosis, Computer-Assisted Deep Learning ROC Curve Image Processing, Computer-Assisted Humans Algorithms Retrospective Studies
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Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans.

European journal of radiology Jun 25, 2020
PURPOSE: Adenocarcinoma (ADC) is the most common histological subtype of lung cancers in non-small cell lung cancer (NSCLC) in which ground glass opacifications (GGOs) found on computed tomography (CT) scans are the most common lesions. However, the ...
Adenocarcinoma of Lung Lung Neoplasms Humans Diagnosis, Differential Prognosis Predictive Value of Tests Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Female Reproducibility of Results Male Middle Aged Lung Deep Learning
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Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms.

Biochimica et biophysica acta. Molecular basis of disease Apr 28, 2020
Lung cancer is one of the most common cancer types worldwide and causes more than one million deaths annually. Lung adenocarcinoma (AC) and lung squamous cell cancer (SCC) are two major lung cancer subtypes and have different characteristics in sever...
Diagnosis, Differential Cadherins Datasets as Topic Tumor Suppressor Proteins Adenosine Triphosphatases Terminology as Topic Membrane Transport Proteins Monte Carlo Method Serpins Lung Neoplasms Computational Biology Gene Expression Regulation, Neoplastic Humans Transcription Factors Machine Learning Adenocarcinoma of Lung Carcinoma, Squamous Cell Cystatin A Gene Expression Profiling Transcriptome
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A cross-modal 3D deep learning for accurate lymph node metastasis prediction in clinical stage T1 lung adenocarcinoma.

Lung cancer (Amsterdam, Netherlands) Apr 25, 2020
OBJECTIVES: The evaluation of lymph node (LN) status by radiologists based on preoperative computed tomography (CT) lacks high precision for early lung cancer patients; erroneous evaluations result in inappropriate therapeutic plans and increase the ...
Deep Learning Lung Neoplasms Lymph Nodes Lymphatic Metastasis Adenocarcinoma of Lung Humans Tomography, X-Ray Computed
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Real world study for the concordance between IBM Watson for Oncology and clinical practice in advanced non-small cell lung cancer patients at a lung cancer center in China.

Thoracic cancer Mar 19, 2020
BACKGROUND: IBM Watson for Oncology (WFO) provides physicians with evidence-based treatment options. This study was designed to explore the concordance of the suggested therapeutic regimen for advanced non-small cell lung (NSCLC) cancer patients betw...
Survival Rate Male Retrospective Studies Patient Selection Humans Lung Neoplasms Middle Aged Clinical Decision-Making Practice Guidelines as Topic Artificial Intelligence China United States Female Evidence-Based Medicine Prognosis Decision Support Systems, Clinical Carcinoma, Non-Small-Cell Lung Carcinoma, Squamous Cell Adenocarcinoma of Lung Follow-Up Studies
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