AIMC Topic: Lung Neoplasms

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Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers.

JAMA network open Apr 5, 2019
IMPORTANCE: A molecular diagnostic method that incorporates information about the transcriptional status of all genes across multiple tissue types can strengthen confidence in cancer diagnosis.
Biomarkers, Tumor Cross-Sectional Studies Male Machine Learning Female Adult Lung Neoplasms Mesothelioma Mesothelioma, Malignant Exome Sequencing Humans Middle Aged Neural Networks, Computer Neoplasms Transcriptome
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Pulmonary nodule detection in CT scans with equivariant CNNs.

Medical image analysis Mar 28, 2019
Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain for medical imaging problems. In this work we show that the sample complexity of CNNs can be significantly improved by usi...
Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Deep Learning Imaging, Three-Dimensional Humans Lung Neoplasms Neural Networks, Computer Tomography, X-Ray Computed
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Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Mar 22, 2019
Deep learning techniques have been extensively used in computerized pulmonary nodule analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in which convolutional neural networks (CNNs) are used only as one part ...
Neural Networks, Computer Databases, Factual Tomography, X-Ray Computed Female Male Diagnosis, Computer-Assisted Humans Deep Learning Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted
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LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features.

BMC cancer Mar 22, 2019
BACKGROUND: Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction...
Feasibility Studies Models, Biological Survival Analysis Datasets as Topic Adenocarcinoma of Lung Computational Biology Support Vector Machine Mutation Humans ROC Curve Lung Neoplasms Genomics Survival Rate Precision Medicine Biomarkers, Tumor Prognosis
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Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes.

IEEE/ACM transactions on computational biology and bioinformatics Mar 18, 2019
As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medicine provides an opportunity to improve the treatment of lung cancer. Subtyping lung cancer plays an ...
Gene Expression Profiling Transcriptome Lung Neoplasms Neural Networks, Computer Algorithms Biomarkers, Tumor Cluster Analysis Mutation Adenocarcinoma of Lung Humans
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Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection.

Neural networks : the official journal of the International Neural Network Society Mar 18, 2019
Lung cancer is a global and dangerous disease, and its early detection is crucial for reducing the risks of mortality. In this regard, it has been of great interest in developing a computer-aided system for pulmonary nodules detection as early as pos...
Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Tomography, X-Ray Computed
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Generation of virtual lung single-photon emission computed tomography/CT fusion images for functional avoidance radiotherapy planning using machine learning algorithms.

Journal of medical imaging and radiation oncology Mar 15, 2019
INTRODUCTION: Functional image-guided radiotherapy (RT) planning for normal lung avoidance has recently been introduced. Single-photon emission computed tomography (SPECT)/CT can help identify the functional areas of lungs, but it is associated with ...
Humans Machine Learning Lung Neoplasms Radiotherapy Planning, Computer-Assisted Tomography, Emission-Computed, Single-Photon Radiotherapy, Image-Guided
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Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks.

Scientific reports Mar 4, 2019
Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteri...
Pathologists Automation Histological Techniques Adenocarcinoma of Lung Neural Networks, Computer Deep Learning Humans Lung Neoplasms
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A Novel Hybrid Feature Extraction Model for Classification on Pulmonary Nodules.

Asian Pacific journal of cancer prevention : APJCP Feb 26, 2019
In this paper an improved Computer Aided Design system can offer a second opinion to radiologists on early diagnosis of pulmonary nodules on CT (Computer Tomography) images. A Deep Convolutional Neural Network (DCNN) method is used for feature extrac...
Diagnosis, Computer-Assisted Lung Neoplasms Humans Support Vector Machine Early Detection of Cancer Neural Networks, Computer Algorithms Decision Trees Machine Learning Prognosis Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed
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Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Feb 25, 2019
PURPOSE: To improve respiratory gating accuracy and treatment throughput, we developed a fluoroscopic markerless tumor tracking algorithm based on a deep neural network (DNN).
Lung Neoplasms Fluoroscopy Four-Dimensional Computed Tomography Humans Time Factors Neural Networks, Computer Liver Neoplasms Retrospective Studies Image Processing, Computer-Assisted
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