AIMC Topic: Lung Neoplasms

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The predictive power of artificial intelligence on mediastinal lymphnode metastasis.

General thoracic and cardiovascular surgery Jun 16, 2021
OBJECTIVE: The aim of this study was to create the preoperative predictive model on mediastinal lymph-node metastasis based on artificial intelligence in surgically resected lung adenocarcinoma.
Neoplasm Staging Positron-Emission Tomography Fluorodeoxyglucose F18 Humans Artificial Intelligence Retrospective Studies Lung Neoplasms Lymph Nodes Lymphatic Metastasis
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Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning.

Nature biomedical engineering Jun 15, 2021
The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning...
Early Detection of Cancer High-Throughput Nucleotide Sequencing Circulating Tumor DNA Biomarkers, Tumor Male Middle Aged Case-Control Studies Lung Neoplasms Sequence Analysis, DNA DNA Methylation Humans Machine Learning Female Adult
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A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification.

The British journal of radiology Jun 11, 2021
OBJECTIVES: To compare the diagnostic performance of a newly developed artificial intelligence (AI) algorithm derived from the fusion of convolution neural networks (CNN) versus human observers in the estimation of malignancy risk in pulmonary nodule...
Humans Tomography, X-Ray Computed Female Aged Middle Aged Deep Learning Imaging, Three-Dimensional Lung Neoplasms Aged, 80 and over Adult Male Observation Contrast Media Radiographic Image Interpretation, Computer-Assisted Iohexol
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AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset.

European radiology Jun 4, 2021
OBJECTIVE: Assess if deep learning-based artificial intelligence (AI) algorithm improves reader performance for lung cancer detection on chest X-rays (CXRs).
Humans Lung Neoplasms Radiography Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Artificial Intelligence Algorithms Sensitivity and Specificity Lung
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Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports.

Chest Jun 4, 2021
BACKGROUND: There is an urgent need for population-based studies on managing patients with pulmonary nodules.
Documentation Radiography, Thoracic Dimensional Measurement Accuracy Radiology Tumor Burden Quality Improvement Sensitivity and Specificity Lung Multiple Pulmonary Nodules Humans Lung Neoplasms Solitary Pulmonary Nodule Algorithms Calcinosis Tomography, X-Ray Computed Natural Language Processing
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Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society May 28, 2021
During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, which is difficult because of respiratory motion and the latency of linear accelerator (LINAC) systems. ...
Humans Lung Lung Neoplasms Neural Networks, Computer Motion Algorithms
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Automatic detect lung node with deep learning in segmentation and imbalance data labeling.

Scientific reports May 27, 2021
In this study, a novel method with the U-Net-based network architecture, 2D U-Net, is employed to segment the position of lung nodules, which are an early symptom of lung cancer and have a high probability of becoming a carcinoma, especially when a l...
Radiography, Thoracic Tomography, X-Ray Computed Female Aged Male Humans Middle Aged Deep Learning Lung Neoplasms
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Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

Journal of magnetic resonance imaging : JMRI May 25, 2021
BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis.
Brain Neoplasms Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Magnetic Resonance Imaging Humans Retrospective Studies
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Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation.

Medical physics May 25, 2021
PURPOSE: Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reli...
Lung Neoplasms Magnetic Resonance Imaging Humans Deep Learning Cone-Beam Computed Tomography Image Processing, Computer-Assisted
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Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography.

Nature communications May 20, 2021
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Ou...
Deep Learning ROC Curve Image Processing, Computer-Assisted Lung Neoplasms Electrocardiography Mass Screening Follow-Up Studies Cardiovascular Diseases Middle Aged Risk Assessment Lung Aged, 80 and over Adult Male Datasets as Topic Humans Tomography, X-Ray Computed Coronary Vessels Retrospective Studies Female Clinical Trials as Topic Risk Factors Aged
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