AIMC Topic: Lung Neoplasms

Clear Filters Showing 1051 to 1060 of 1629 articles

External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules.

Thorax Mar 5, 2020
BACKGROUND: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI...
Retrospective Studies Female Aged Adult Artificial Intelligence Algorithms Databases, Factual Prognosis Cohort Studies ROC Curve Lung Neoplasms Multiple Pulmonary Nodules Early Detection of Cancer Male Middle Aged Risk Assessment Incidence Humans Neural Networks, Computer Cell Transformation, Neoplastic Neoplasm Invasiveness Predictive Value of Tests Area Under Curve Neoplasm Staging
View on PubMed DOI

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks.

PloS one Mar 5, 2020
Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional neural network (DCNN) to automatically classify cytological images as images with benign or malignant ...
Humans Lung Neoplasms Male Data Accuracy Lung Image Interpretation, Computer-Assisted Deep Learning Image Processing, Computer-Assisted
View on PubMed DOI

Achievability to Extract Specific Date Information for Cancer Research.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Accurate identification of temporal information such as date is crucial for advancing cancer research which often requires precise date information associated with related cancer events. However, there is a gap for existing natural language processin...
Multiple Myeloma Lung Neoplasms Information Storage and Retrieval Humans Natural Language Processing Electronic Health Records Time Data Collection Biomedical Research Medical Oncology Neoplasms
View on PubMed

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.

European radiology Feb 28, 2020
PURPOSE: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography ...
Neoplasm Recurrence, Local Adenocarcinoma of Lung Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Humans Risk Factors ROC Curve Lung Neoplasms Sensitivity and Specificity Logistic Models Image Processing, Computer-Assisted Male Neoplasm Invasiveness Middle Aged
View on PubMed DOI

Real-time markerless tumour tracking with patient-specific deep learning using a personalised data generation strategy: proof of concept by phantom study.

The British journal of radiology Feb 28, 2020
OBJECTIVE: For real-time markerless tumour tracking in stereotactic lung radiotherapy, we propose a different approach which uses patient-specific deep learning (DL) using a personalised data generation strategy, avoiding the need for collection of a...
Phantoms, Imaging Movement Four-Dimensional Computed Tomography Image Processing, Computer-Assisted Lung Neoplasms Fluoroscopy Feasibility Studies Humans Deep Learning
View on PubMed DOI

Radiomics: from qualitative to quantitative imaging.

The British journal of radiology Feb 26, 2020
Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and...
Humans Breast Neoplasms Brain Neoplasms Male Forecasting Female Diagnostic Imaging Deep Learning Workflow Image Processing, Computer-Assisted Technology, Radiologic Lung Neoplasms Radiography
View on PubMed DOI

Feature-shared adaptive-boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images.

Medical physics Feb 26, 2020
PURPOSE: In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules. These nodules can be classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ ...
Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Lung Neoplasms Neoplasm Invasiveness Humans
View on PubMed DOI

Automatic Lung Nodule Detection Combined With Gaze Information Improves Radiologists' Screening Performance.

IEEE journal of biomedical and health informatics Feb 24, 2020
Early diagnosis of lung cancer via computed tomography can significantly reduce the morbidity and mortality rates associated with the pathology. However, searching lung nodules is a high complexity task, which affects the success of screening program...
Tomography, X-Ray Computed Eye-Tracking Technology Deep Learning Lung Neoplasms Humans Radiologists Radiographic Image Interpretation, Computer-Assisted Fixation, Ocular
View on PubMed DOI

Machine learning helps identifying volume-confounding effects in radiomics.

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 20, 2020
Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding...
Cluster Analysis Regression Analysis Principal Component Analysis Radiometry Laryngeal Neoplasms Oropharyngeal Neoplasms Decision Support Systems, Clinical Lung Neoplasms Squamous Cell Carcinoma of Head and Neck Carcinoma, Non-Small-Cell Lung Aged, 80 and over Adult Male Middle Aged Machine Learning Humans Software Algorithms Female Databases, Factual Retrospective Studies Carcinoma, Squamous Cell Tomography, X-Ray Computed Aged
View on PubMed DOI

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening.

European radiology Feb 19, 2020
OBJECTIVE: Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In this study, a deep learning-based system was developed to automatically measure bone mineral density (BMD) for opportunistic osteoporosis screening usin...
Tomography, X-Ray Computed Humans Deep Learning Early Detection of Cancer Male Bone Density Lung Neoplasms Female Spine Middle Aged Osteoporosis Mass Screening Aged Bone Diseases, Metabolic Aged, 80 and over
View on PubMed DOI
Popular Topics
Recent Journals