AIMC Topic: Solitary Pulmonary Nodule

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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 (...
Humans Adult Male Middle Aged Diagnosis, Computer-Assisted Deep Learning Algorithms Retrospective Studies Tomography, X-Ray Computed Female Granuloma Tuberculosis Sex Factors Diagnostic Tests, Routine Observer Variation Pattern Recognition, Automated Calibration Adenocarcinoma of Lung ROC Curve Image Processing, Computer-Assisted Nomograms Lung Neoplasms Age Factors Logistic Models Solitary Pulmonary Nodule Regression Analysis Diagnosis, Differential
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Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography.

Japanese journal of radiology Jun 26, 2020
PURPOSE: To evaluate the performance of a deep learning-based computer-aided diagnosis (CAD) system at detecting pulmonary nodules on CT by comparing radiologists' readings with and without CAD.
Aged Aged, 80 and over Young Adult Adult Sensitivity and Specificity Retrospective Studies Tomography, X-Ray Computed Female Reproducibility of Results Deep Learning Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Male Humans Middle Aged Lung Diagnosis, Computer-Assisted
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Deep learning-based pulmonary nodule detection: Effect of slab thickness in maximum intensity projections at the nodule candidate detection stage.

Computer methods and programs in biomedicine Jun 20, 2020
BACKGROUND AND OBJECTIVE: To investigate the effect of the slab thickness in maximum intensity projections (MIPs) on the candidate detection performance of a deep learning-based computer-aided detection (DL-CAD) system for pulmonary nodule detection ...
Deep Learning Lung Neoplasms Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Humans Sensitivity and Specificity
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Identification of benign and malignant pulmonary nodules on chest CT using improved 3D U-Net deep learning framework.

European journal of radiology May 23, 2020
PURPOSE: To accurately distinguish benign from malignant pulmonary nodules with CT based on partial structures of 3D U-Net integrated with Capsule Networks (CapNets) and provide a reference for the early diagnosis of lung cancer.
Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Humans Tomography, X-Ray Computed Deep Learning Lung Neoplasms
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Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning.

European radiology Apr 29, 2020
OBJECTIVES: To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs.
Solitary Pulmonary Nodule Radiography Radiographic Image Interpretation, Computer-Assisted Precancerous Conditions Radiography, Thoracic Lung Diagnosis, Computer-Assisted Deep Learning Lung Neoplasms Female Aged Male Middle Aged Humans Neural Networks, Computer Retrospective Studies
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Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping.

Medical image analysis Dec 12, 2019
A number of studies on lung nodule classification lack clinical/biological interpretations of the features extracted by convolutional neural network (CNN). The methods like class activation mapping (CAM) and gradient-based CAM (Grad-CAM) are tailored...
Lung Neoplasms Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Tomography, X-Ray Computed Sensitivity and Specificity
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Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance.

European radiology Dec 10, 2019
OBJECTIVES: To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reco...
Humans Artificial Intelligence Solitary Pulmonary Nodule Aged, 80 and over Positron Emission Tomography Computed Tomography Deep Learning Adult Radiopharmaceuticals Image Processing, Computer-Assisted Sensitivity and Specificity Fluorodeoxyglucose F18 Algorithms Lung Neoplasms Male Retrospective Studies Middle Aged Multiple Pulmonary Nodules Female Aged
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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 Tomography, X-Ray Computed Female Aged Aged, 80 and over Young Adult Solitary Pulmonary Nodule Adenocarcinoma of Lung Deep Learning Neoplasm Invasiveness Adenocarcinoma in Situ Adolescent Radiologists Adult ROC Curve Feasibility Studies Male Image Processing, Computer-Assisted Middle Aged Lung Neoplasms Disease Progression
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Intraoperative localization of small pulmonary nodules to assist surgical resection: A novel approach using a surgical navigation puncture robot system.

Thoracic cancer Nov 26, 2019
BACKGROUND: Localization and resection of nonvisible, nonpalpable pulmonary nodules during video-assisted thoracoscopic surgery is challenging. In this study we developed a surgical navigation puncture robot system in order to locate small pulmonary ...
Animals Swine Multiple Pulmonary Nodules Solitary Pulmonary Nodule Robotics Surgery, Computer-Assisted Thoracic Surgery, Video-Assisted Punctures Intraoperative Care Tomography, X-Ray Computed
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Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study.

Academic radiology Nov 25, 2019
RATIONALE AND OBJECTIVES: We aimed to present a deep learning-based malignancy prediction model (CT-lungNET) that is simpler and faster to use in the diagnosis of small (≤2 cm) pulmonary nodules on nonenhanced chest CT and to preliminarily evaluate i...
Multiple Pulmonary Nodules Humans Tomography, X-Ray Computed Solitary Pulmonary Nodule Deep Learning Radiographic Image Interpretation, Computer-Assisted Lung Neoplasms
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