AIMC Topic: Lung Neoplasms
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Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region.
European radiology
Jan 9, 2022
OBJECTIVES: To evaluate the clinical impact of a deep learning system (DLS) for automated detection of pulmonary nodules on computed tomography (CT) images as a second reader.
Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging.
Thoracic cancer
Jan 6, 2022
BACKGROUND: Early identification of the malignant propensity of pulmonary ground-glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning-based...
Application of Deep Learning in Lung Cancer Imaging Diagnosis.
Journal of healthcare engineering
Jan 3, 2022
Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pol...
Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning.
Computers in biology and medicine
Dec 29, 2021
Lymph node metastasis also called nodal metastasis (Nmet), is a clinically primary task for physicians. The survival and recurrence of lung cancer are related to the Nmet staging from Tumor-Node-Metastasis (TNM) reports. Furthermore, preoperative Nme...
Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network.
Genes
Dec 27, 2021
Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual sample...
Analysis of high-resolution reconstruction of medical images based on deep convolutional neural networks in lung cancer diagnostics.
Computer methods and programs in biomedicine
Dec 20, 2021
BACKGROUND AND OBJECTIVE: To study the diagnostic effect of 64-slice spiral CT and MRI high-resolution images based on deep convolutional neural networks(CNN) in lung cancer.
Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes.
Journal of cardiovascular computed tomography
Dec 17, 2021
BACKGROUND: Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment a...
Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA.
Scientific reports
Dec 16, 2021
Machine learning (ML) has demonstrated promise in predicting mortality; however, understanding spatial variation in risk factor contributions to mortality rate requires explainability. We applied explainable artificial intelligence (XAI) on a stack-e...
Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study.
The British journal of radiology
Dec 15, 2021
OBJECTIVES: The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric a...
Creating a training set for artificial intelligence from initial segmentations of airways.
European radiology experimental
Nov 29, 2021
Airways segmentation is important for research about pulmonary disease but require a large amount of time by trained specialists. We used an openly available software to improve airways segmentations obtained from an artificial intelligence (AI) tool...
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