AIMC Topic: Adenocarcinoma of Lung

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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 ...
Humans Prognosis Tomography, X-Ray Computed Deep Learning Lung Neoplasms Diagnosis, Differential Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Adenocarcinoma of Lung Female Reproducibility of Results Male Middle Aged Lung
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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...
Cadherins Datasets as Topic Lung Neoplasms Tumor Suppressor Proteins Adenosine Triphosphatases Computational Biology Membrane Transport Proteins Terminology as Topic Gene Expression Regulation, Neoplastic Monte Carlo Method Humans Transcription Factors Serpins Machine Learning Diagnosis, Differential 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 ...
Lymphatic Metastasis Adenocarcinoma of Lung Humans Tomography, X-Ray Computed Deep Learning Lung Neoplasms Lymph Nodes
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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...
Female Carcinoma, Squamous Cell Survival Rate Male Middle Aged Humans Artificial Intelligence Prognosis Retrospective Studies Clinical Decision-Making Follow-Up Studies Patient Selection Practice Guidelines as Topic Evidence-Based Medicine Adenocarcinoma of Lung China Decision Support Systems, Clinical Lung Neoplasms United States Carcinoma, Non-Small-Cell Lung
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CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.

European radiology Feb 28, 2020
PURPOSE: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography ...
Adenocarcinoma of Lung Image Processing, Computer-Assisted Lung Neoplasms Logistic Models Neoplasm Invasiveness Neoplasm Recurrence, Local Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Risk Factors Sensitivity and Specificity Male Middle Aged ROC Curve
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CT-based deep learning model to differentiate invasive pulmonary adenocarcinomas appearing as subsolid nodules among surgical candidates: comparison of the diagnostic performance with a size-based logistic model and radiologists.

European radiology Feb 13, 2020
OBJECTIVES: To evaluate the deep learning models for differentiating invasive pulmonary adenocarcinomas (IACs) among subsolid nodules (SSNs) considered for resection in a retrospective diagnostic cohort in comparison with a size-based logistic model ...
Aged Male Middle Aged Deep Learning ROC Curve Lung Neoplasms Logistic Models Radiologists Radiography, Thoracic Adenocarcinoma of Lung Humans Retrospective Studies Tomography, X-Ray Computed Female
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A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.

European radiology Dec 6, 2019
OBJECTIVE: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme wi...
Retrospective Studies Tomography, X-Ray Computed Female Aged Aged, 80 and over Young Adult Adult Male Middle Aged Disease Progression Adenocarcinoma in Situ Humans Neural Networks, Computer Artificial Intelligence Deep Learning Solitary Pulmonary Nodule Adolescent Neoplasm Invasiveness ROC Curve Radiologists Image Processing, Computer-Assisted Feasibility Studies Lung Neoplasms Adenocarcinoma of Lung
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ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network.

EBioMedicine Nov 22, 2019
BACKGROUND: The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". Ho...
Web Browser Adenocarcinoma of Lung Image Processing, Computer-Assisted Lung Neoplasms Female Humans Reproducibility of Results Tumor Microenvironment Neural Networks, Computer Male Software Deep Learning Neoplasm Staging Algorithms Workflow Histocytochemistry
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A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.

BMC cancer Sep 5, 2019
BACKGROUND: Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD.
Disease-Free Survival Humans Proportional Hazards Models Adenocarcinoma of Lung Machine Learning Survival Rate Transcriptome Multivariate Analysis Prognosis Biomarkers, Tumor Kaplan-Meier Estimate Lung Neoplasms Female Male Genes, Neoplasm Gene Expression Regulation, Neoplastic Aged Middle Aged Follow-Up Studies Disease Progression Databases, Genetic
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Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes.

Thoracic cancer Aug 19, 2019
BACKGROUND: The aim of this study was to investigate the influence of convolution kernel and iterative reconstruction on the diagnostic performance of radiomics and deep learning (DL) in lung adenocarcinomas.
Area Under Curve Humans Neoplasm Staging Tomography, X-Ray Computed Neoplasm Grading Deep Learning Medical Informatics Computing ROC Curve Adenocarcinoma of Lung Image Processing, Computer-Assisted
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