AIMC Topic: Lung Neoplasms

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Performance of a Deep Learning Algorithm Compared with Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population.

Radiology Sep 22, 2020
Background The performance of a deep learning algorithm for lung cancer detection on chest radiographs in a health screening population is unknown. Purpose To validate a commercially available deep learning algorithm for lung cancer detection on ches...
Sensitivity and Specificity Male Middle Aged Retrospective Studies Republic of Korea Female Radiography, Thoracic Deep Learning Lung Neoplasms Mass Screening Humans
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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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Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade.

Cancer discovery Sep 21, 2020
Real-world evidence (RWE), conclusions derived from analysis of patients not treated in clinical trials, is increasingly recognized as an opportunity for discovery, to reduce disparities, and to contribute to regulatory approval. Maximal value of RWE...
Humans Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Programmed Cell Death 1 Receptor Response Evaluation Criteria in Solid Tumors
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Artificial Intelligence and Data Mining to Assess Lung Cancer Risk: Challenges and Opportunities.

Annals of internal medicine Sep 1, 2020
Lung Neoplasms Early Detection of Cancer Data Mining Smokers Humans Artificial Intelligence Deep Learning
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Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model.

Annals of internal medicine Sep 1, 2020
BACKGROUND: Lung cancer screening with chest computed tomography (CT) reduces lung cancer death. Centers for Medicare & Medicaid Services (CMS) eligibility criteria for lung cancer screening with CT require detailed smoking information and miss many ...
Aged Sensitivity and Specificity Male Tomography, X-Ray Computed Female Risk Factors Deep Learning Lung Neoplasms Middle Aged Early Detection of Cancer Humans Risk Assessment Smoking Decision Support Techniques Lung
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Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs.

JAMA network open Sep 1, 2020
IMPORTANCE: The improvement of pulmonary nodule detection, which is a challenging task when using chest radiographs, may help to elevate the role of chest radiographs for the diagnosis of lung cancer.
Tumor Burden Radiography, Thoracic Radiographic Image Enhancement Humans Artificial Intelligence Algorithms Lung Deep Learning Female Predictive Value of Tests Solitary Pulmonary Nodule Sensitivity and Specificity Lung Neoplasms Early Detection of Cancer Reproducibility of Results Radiologists Male Aged Middle Aged
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Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations.

Biomolecules Aug 28, 2020
Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater than the number of samples, forcing the model to be prone to overfitting the training dataset. Second...
Humans Diagnosis, Differential Diagnosis, Computer-Assisted Mutation Lung Neoplasms Neural Networks, Computer Adenocarcinoma Carcinoma, Squamous Cell Reproducibility of Results
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A novel machine learning strategy for model selections - Stepwise Support Vector Machine (StepSVM).

PloS one Aug 27, 2020
An essential aspect of medical research is the prediction for a health outcome and the scientific identification of important factors. As a result, numerous methods were developed for model selections in recent years. In the era of big data, machine ...
Lung Neoplasms Humans Logistic Models Machine Learning Support Vector Machine Algorithms
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Detection of pulmonary nodules based on a multiscale feature 3D U-Net convolutional neural network of transfer learning.

PloS one Aug 26, 2020
A new computer-aided detection scheme is proposed, the 3D U-Net convolutional neural network, based on multiscale features of transfer learning to automatically detect pulmonary nodules from the thoracic region containing background and noise. The te...
Early Detection of Cancer Humans Algorithms Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Imaging, Three-Dimensional Lung Neural Networks, Computer Lung Neoplasms Solitary Pulmonary Nodule Machine Learning
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Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets.

Academic radiology Aug 21, 2020
RATIONALE AND OBJECTIVES: A more accurate lung nodule detection algorithm is needed. We developed a modified three-dimensional (3D) U-net deep-learning model for the automated detection of lung nodules on chest CT images. The purpose of this study wa...
Deep Learning Lung Neoplasms Japan Retrospective Studies Tomography, X-Ray Computed Lung Humans
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