AIMC Topic: Lung Neoplasms

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Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT.

Radiology May 26, 2020
Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT. Purpose To determine the efficacy of a deep learning method for improving CADv for measuring the solid and ground-gla...
Multiple Pulmonary Nodules Tomography, X-Ray Computed Female Aged Humans Neural Networks, Computer Retrospective Studies Aged, 80 and over Male Adult Middle Aged Sensitivity and Specificity Lung Lung Neoplasms
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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.
Humans Tomography, X-Ray Computed Deep Learning Lung Neoplasms Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted
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Development of a machine learning-based multimode diagnosis system for lung cancer.

Aging May 23, 2020
As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, ar...
Humans Neural Networks, Computer Adult Male Support Vector Machine Machine Learning Middle Aged Early Detection of Cancer Area Under Curve Diagnosis, Computer-Assisted Female Lung Neoplasms
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Performance improvement of mediastinal lymph node severity detection using GAN and Inception network.

Computer methods and programs in biomedicine May 22, 2020
BACKGROUND AND OBJECTIVE: In lung cancer, the determination of mediastinal lymph node (MLN) status as benign or malignant influence treatment planning and survival rate. Invasive pathological tests for the classification of MLNs into benign and malig...
Lymph Nodes Tomography, X-Ray Computed Computer Systems Lung Neoplasms Humans Neural Networks, Computer
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3D-MCN: A 3D Multi-scale Capsule Network for Lung Nodule Malignancy Prediction.

Scientific reports May 14, 2020
Despite the advances in automatic lung cancer malignancy prediction, achieving high accuracy remains challenging. Existing solutions are mostly based on Convolutional Neural Networks (CNNs), which require a large amount of training data. Most of the ...
Humans Neural Networks, Computer Lung Neoplasms Computational Biology
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Reply from authors: Identifying lessons that could be generalized across different disease burdens.

The Journal of thoracic and cardiovascular surgery May 13, 2020
Robotic Surgical Procedures Humans Propensity Score Lung Neoplasms Mastectomy, Segmental Robotics
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Deep Learning and Lung Cancer: AI to Extract Information Hidden in Routine CT Scans.

Radiology May 12, 2020
Lung Neoplasms Tomography, X-Ray Computed Humans Deep Learning
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Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to whole-slide images.

Cancer medicine May 12, 2020
BACKGROUND: It is unclear whether clinical factors and immune microenvironment (IME) factors are associated with tumor mutation burden (TMB) in patients with nonsmall cell lung cancer (NSCLC).
Carcinoma, Non-Small-Cell Lung Lymphocytes, Tumor-Infiltrating Regression Analysis B7-H1 Antigen Antigens, Neoplasm Adenocarcinoma Smokers Exome Sequencing Keratin-19 Ex-Smokers Mutation Forkhead Transcription Factors Carcinoembryonic Antigen Aged, 80 and over Adult Tumor Microenvironment Male Middle Aged Lung Neoplasms Female Carcinoma, Squamous Cell Aged Humans Machine Learning Non-Smokers
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Radiomics and deep learning in lung cancer.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] May 4, 2020
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomograp...
False Positive Reactions Radiosurgery Radiotherapy Neoplasm Staging Neoplasm Recurrence, Local Tomography, X-Ray Computed Radiotherapy Planning, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Computational Biology Radiation Injuries Lung Neoplasms Treatment Outcome Phenotype Humans Imaging Genomics Genotype Databases, Factual Forecasting Prognosis
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Investigation of pulmonary nodule classification using multi-scale residual network enhanced with 3DGAN-synthesized volumes.

Radiological physics and technology May 1, 2020
It is often difficult to distinguish between benign and malignant pulmonary nodules using only image diagnosis. A biopsy is performed when malignancy is suspected based on CT examination. However, biopsies are highly invasive, and patients with benig...
Neural Networks, Computer Tomography, X-Ray Computed Imaging, Three-Dimensional Lung Neoplasms Humans
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