AIMC Topic: Lung Neoplasms
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Disparities in the Treatment and Outcome of Stage I Non-Small-Cell Lung Cancer in the 21st Century.
Clinical lung cancer
Nov 20, 2018
BACKGROUND: African American (AA) individuals are less likely to receive treatment and more likely to die from cancer compared with Caucasian (C) individuals. Recent advancements in surgery and radiation have improved outcomes in early stage non-smal...
Measurement and immunophenotyping of pleural fluid EpCAM-positive cells and clusters for the management of non-small cell lung cancer patients.
Lung cancer (Amsterdam, Netherlands)
Nov 19, 2018
OBJECTIVES: A malignant pleural effusion (MPE) is a common complication in non-small cell lung cancer (NSCLC) with important staging and prognostic information. Patients with MPEs are often candidates for advanced therapies, however, the current gold...
SVM-RFE: selection and visualization of the most relevant features through non-linear kernels.
BMC bioinformatics
Nov 19, 2018
BACKGROUND: Support vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited be...
3-D Convolutional Neural Networks for Automatic Detection of Pulmonary Nodules in Chest CT.
IEEE journal of biomedical and health informatics
Nov 9, 2018
Deep two-dimensional (2-D) convolutional neural networks (CNNs) have been remarkably successful in producing record-breaking results in a variety of computer vision tasks. It is possible to extend CNNs to three dimensions using 3-D kernels to make th...
Multiple Human-Behaviour Indicators for Predicting Lung Cancer Mortality with Support Vector Machine.
Scientific reports
Nov 9, 2018
Lung cancer is still one of the most common causes of death around the world, while there is overwhelming evidence that the environment and lifestyle factors are predominant causes of most sporadic cancers. However, when applying human-behaviour indi...
A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings.
Medical physics
Nov 8, 2018
PURPOSE: Bronchoscopy is useful in lung cancer detection, but cannot be used to differentiate cancer types. A computer-aided diagnosis (CAD) system was proposed to distinguish malignant cancer types to achieve objective diagnoses.
A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation.
IEEE journal of biomedical and health informatics
Nov 6, 2018
The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's structural information from CT scans in a computer-aided system is a challenging task. Unlike previous mod...
Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques.
Acta oncologica (Stockholm, Sweden)
Nov 6, 2018
BACKGROUND: In this study, a deep convolutional neural network (CNN)-based automatic segmentation technique was applied to multiple organs at risk (OARs) depicted in computed tomography (CT) images of lung cancer patients, and the results were compar...
Anatomy, Artistic
Radiography, Thoracic
Radiography, Abdominal
Image Processing, Computer-Assisted
Humans
Organs at Risk
Atlases as Topic
Lung Neoplasms
Neural Networks, Computer
Radiotherapy Planning, Computer-Assisted
Esophagus
Tomography, X-Ray Computed
Tumor Burden
Spine
Pattern Recognition, Automated
Machine learning to predict lung nodule biopsy method using CT image features: A pilot study.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Nov 3, 2018
Computed tomography (CT)-based screening on lung cancer mortality is poised to make lung nodule management a growing public health problem. Biopsy and pathologic analysis of suspicious nodules is necessary to ensure accurate diagnosis and appropriate...
Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks - Initial results.
Lung cancer (Amsterdam, Netherlands)
Nov 3, 2018
OBJECTIVES: We evaluated whether machine learning may be helpful for the detection of lung cancer in FDG-PET imaging in the setting of ultralow dose PET scans.
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