AIMC Topic: Adenocarcinoma of 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 ...
Humans Retrospective Studies Tomography, X-Ray Computed Female Aged Lung Neoplasms Male Logistic Models Middle Aged Radiologists Deep Learning Radiography, Thoracic ROC Curve Adenocarcinoma of Lung
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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...
Humans Neural Networks, Computer Artificial Intelligence Retrospective Studies Adult Male Middle Aged Disease Progression Deep Learning Tomography, X-Ray Computed Female Aged Aged, 80 and over Young Adult Neoplasm Invasiveness Radiologists Feasibility Studies Adolescent Adenocarcinoma of Lung ROC Curve Adenocarcinoma in Situ Image Processing, Computer-Assisted Lung Neoplasms Solitary Pulmonary Nodule
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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...
Adenocarcinoma of Lung Lung Neoplasms Neoplasm Staging Workflow Histocytochemistry Web Browser Humans Neural Networks, Computer Software Algorithms Female Reproducibility of Results Tumor Microenvironment Male Deep Learning Image Processing, Computer-Assisted
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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.
Proportional Hazards Models Multivariate Analysis Kaplan-Meier Estimate Genes, Neoplasm Adenocarcinoma of Lung Lung Neoplasms Gene Expression Regulation, Neoplastic Follow-Up Studies Databases, Genetic Disease-Free Survival Machine Learning Biomarkers, Tumor Humans Prognosis Male Female Middle Aged Aged Disease Progression Survival Rate Transcriptome
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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.
Humans Neoplasm Staging Tomography, X-Ray Computed Neoplasm Grading Deep Learning Medical Informatics Computing ROC Curve Adenocarcinoma of Lung Image Processing, Computer-Assisted Area Under Curve
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Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning.

Cancer medicine May 10, 2019
To develop a deep learning system based on 3D convolutional neural networks (CNNs), and to automatically predict EGFR-mutant pulmonary adenocarcinoma in CT images. A dataset of 579 nodules with EGFR mutation status labels of mutant (Mut) or wild-type...
Deep Learning ROC Curve Image Processing, Computer-Assisted Computational Biology Neoplasm Staging Mutation ErbB Receptors Data Curation Adenocarcinoma of Lung Algorithms Tomography, X-Ray Computed Humans Female Neural Networks, Computer Reproducibility of Results Male
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Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.

The European respiratory journal Mar 28, 2019
Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification of EGFR genotype requires biopsy and sequence testing which is i...
Mutation ErbB Receptors Humans Adenocarcinoma of Lung Neural Networks, Computer Deep Learning Retrospective Studies ROC Curve Tomography, X-Ray Computed Female Image Processing, Computer-Assisted Reproducibility of Results Computational Biology Aged Neoplasm Staging Male Middle Aged
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LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features.

BMC cancer Mar 22, 2019
BACKGROUND: Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction...
Mutation Feasibility Studies Models, Biological Humans ROC Curve Survival Analysis Adenocarcinoma of Lung Precision Medicine Lung Neoplasms Prognosis Datasets as Topic Genomics Survival Rate Computational Biology Biomarkers, Tumor Support Vector Machine
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Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes.

IEEE/ACM transactions on computational biology and bioinformatics Mar 18, 2019
As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medicine provides an opportunity to improve the treatment of lung cancer. Subtyping lung cancer plays an ...
Adenocarcinoma of Lung Gene Expression Profiling Humans Transcriptome Neural Networks, Computer Lung Neoplasms Algorithms Cluster Analysis Biomarkers, Tumor Mutation
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