AIMC Topic: Lung Neoplasms

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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
Lung Neoplasms Robotics Robotic Surgical Procedures Propensity Score Mastectomy, Segmental Humans
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Deep Learning and Lung Cancer: AI to Extract Information Hidden in Routine CT Scans.

Radiology May 12, 2020
Lung Neoplasms Humans Tomography, X-Ray Computed 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, Squamous Cell Aged Aged, 80 and over Adult Humans Machine Learning Female B7-H1 Antigen Antigens, Neoplasm Adenocarcinoma Carcinoma, Non-Small-Cell Lung Lymphocytes, Tumor-Infiltrating Tumor Microenvironment Male Middle Aged Lung Neoplasms Non-Smokers Smokers Regression Analysis Exome Sequencing Mutation Keratin-19 Forkhead Transcription Factors Ex-Smokers Carcinoembryonic Antigen
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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...
Neoplasm Staging Neoplasm Recurrence, Local Radiotherapy Planning, Computer-Assisted False Positive Reactions Lung Neoplasms Genotype Computational Biology Treatment Outcome Radiosurgery Radiotherapy Radiation Injuries Imaging Genomics Humans Forecasting Phenotype Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Databases, Factual 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...
Humans Neural Networks, Computer Tomography, X-Ray Computed Imaging, Three-Dimensional 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.
Radiographic Image Interpretation, Computer-Assisted Precancerous Conditions Radiography, Thoracic Deep Learning Lung Neoplasms Solitary Pulmonary Nodule Radiography Humans Neural Networks, Computer Retrospective Studies Female Middle Aged Lung Aged Diagnosis, Computer-Assisted Male
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Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.

AJR. American journal of roentgenology Apr 29, 2020
The purpose of this study was to assess, by analyzing features of the primary tumor with F-FDG PET, the utility of deep machine learning with a convolutional neural network (CNN) in predicting the potential of newly diagnosed non-small cell lung can...
Humans Neoplasm Metastasis Neural Networks, Computer Fluorodeoxyglucose F18 Retrospective Studies Lymphatic Metastasis Female Male Aged Imaging, Three-Dimensional Predictive Value of Tests Middle Aged Positron Emission Tomography Computed Tomography Aged, 80 and over Lung Neoplasms Radiopharmaceuticals Adult Carcinoma, Non-Small-Cell Lung Sensitivity and Specificity
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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...
Monte Carlo Method Serpins Adenocarcinoma of Lung Cystatin A Transcription Factors Diagnosis, Differential Cadherins Datasets as Topic Tumor Suppressor Proteins Adenosine Triphosphatases Terminology as Topic Membrane Transport Proteins Transcriptome Lung Neoplasms Computational Biology Gene Expression Regulation, Neoplastic Humans Machine Learning Carcinoma, Squamous Cell Gene Expression Profiling
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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 ...
Lung Neoplasms Lymph Nodes Lymphatic Metastasis Adenocarcinoma of Lung Humans Tomography, X-Ray Computed Deep Learning
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Single patient convolutional neural networks for real-time MR reconstruction: coherent low-resolution versus incoherent undersampling.

Physics in medicine and biology Apr 23, 2020
Accelerated MRI involves undersampling k-space, creating unwanted artifacts when reconstructing the data. While the strategy of incoherent k-space acquisition is proven for techniques such as compressed sensing, it may not be optimal for all techniqu...
Retrospective Studies Time Factors Image Processing, Computer-Assisted Lung Neoplasms Carcinoma, Non-Small-Cell Lung Magnetic Resonance Imaging Artifacts Respiration Humans Neural Networks, Computer Signal-To-Noise Ratio
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