AIMC Topic: 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.
Precancerous Conditions Radiography, Thoracic Lung Neoplasms Solitary Pulmonary Nodule Radiography Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Female Aged Male Middle Aged Lung Diagnosis, Computer-Assisted Humans Deep Learning Neural Networks, Computer
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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...
Neoplasm Metastasis Fluorodeoxyglucose F18 Aged, 80 and over Adult Lymphatic Metastasis Neural Networks, Computer Middle Aged Sensitivity and Specificity Predictive Value of Tests Retrospective Studies Humans Positron Emission Tomography Computed Tomography Male Imaging, Three-Dimensional Female Aged Lung Neoplasms Radiopharmaceuticals Carcinoma, Non-Small-Cell Lung
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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 Tumor Suppressor Proteins Transcription Factors Transcriptome Adenosine Triphosphatases Diagnosis, Differential Lung Neoplasms Terminology as Topic Cadherins Computational Biology Humans Membrane Transport Proteins Datasets as Topic Gene Expression Regulation, Neoplastic 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 ...
Humans Lymphatic Metastasis Adenocarcinoma of Lung Tomography, X-Ray Computed Deep Learning Lung Neoplasms Lymph Nodes
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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...
Artifacts Respiration Signal-To-Noise Ratio Image Processing, Computer-Assisted Lung Neoplasms Carcinoma, Non-Small-Cell Lung Magnetic Resonance Imaging Humans Neural Networks, Computer Retrospective Studies Time Factors
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A machine learning-based real-time tumor tracking system for fluoroscopic gating of lung radiotherapy.

Physics in medicine and biology Apr 23, 2020
To improve respiratory-gated radiotherapy accuracy, we developed a machine learning approach for markerless tumor tracking and evaluated it using lung cancer patient data. Digitally reconstructed radiography (DRR) datasets were generated using planni...
Humans Fluoroscopy Likelihood Functions Radiotherapy, Image-Guided Machine Learning Time Factors Image Processing, Computer-Assisted Lung Neoplasms
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Prognosis in pathology: Are we "prognosticating" or only establishing correlations between independent variables and survival? A study with various analytics cautions about the overinterpretation of statistical results.

Annals of diagnostic pathology Apr 21, 2020
Survival data from 225 patients with resected pulmonary typical carcinoids were analyzed with Kaplan-Meier statistics (K-M) and "deep learning" methods to illustrate the difference between establishing "correlations" and "prognostications". Cases wer...
Humans Neural Networks, Computer Prognosis Lung Neoplasms Kaplan-Meier Estimate Pathology, Clinical Carcinoid Tumor
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Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT.

The Lancet. Digital health Apr 21, 2020
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is underdiagnosed in the community. Thoracic CT scans are widely used for diagnostic and screening purposes for lung cancer. In this proof-of-concept study, we aimed to evaluate a software pipe...
Models, Biological Smoking Smokers Ex-Smokers Lung Neoplasms Tomography, X-Ray Computed Male Canada Humans Lung Female Middle Aged Mass Screening Neural Networks, Computer Cohort Studies Area Under Curve Risk Assessment Aged Artificial Intelligence ROC Curve Data Analysis Disease Progression Pulmonary Disease, Chronic Obstructive Image Processing, Computer-Assisted
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Early-Stage Lung Cancer Diagnosis by Deep Learning-Based Spectroscopic Analysis of Circulating Exosomes.

ACS nano Apr 17, 2020
Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosize...
Deep Learning Lung Neoplasms Spectrum Analysis, Raman Exosomes Humans Biomarkers, Tumor
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Machine learning identifies 10 feature miRNAs for lung squamous cell carcinoma.

Gene Apr 13, 2020
Lung squamous cell carcinoma (LUSC) is a common type of malignancy. The mechanism behind its tumor progression is not clear yet. The aim of this study is to use machine learning to identify the feature miRNAs, which can be reliably used as biomarkers...
Humans Machine Learning Middle Aged Female Lung Neoplasms Carcinoma, Squamous Cell RNA, Messenger Biomarkers, Tumor MicroRNAs Male
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