AIMC Topic: Solitary Pulmonary Nodule
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Applicability analysis of immunotherapy for lung cancer patients based on deep learning.
Methods (San Diego, Calif.)
Jul 6, 2022
According to global and Chinese cancer statistics, lung cancer is the second most common cancer globally with the highest mortality rate and a severe threat to human life and health. In recent years, immunotherapy has made significant breakthroughs i...
Radiologists with and without deep learning-based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses.
European radiology
Jun 25, 2022
OBJECTIVES: To compare the performance of radiologists in characterizing and diagnosing pulmonary nodules/masses with and without deep learning (DL)-based computer-aided diagnosis (CAD).
Deep Learning-Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study.
Academic radiology
Jun 9, 2022
RATIONALE AND OBJECTIVES: Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We develop...
Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population.
PloS one
May 5, 2022
OBJECTIVE: In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantificati...
Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario.
European radiology
Jan 21, 2022
OBJECTIVES: This study was conducted to evaluate the effect of dose reduction on the performance of a deep learning (DL)-based computer-aided diagnosis (CAD) system regarding pulmonary nodule detection in a virtual screening scenario.
Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method.
Scientific reports
Jan 14, 2022
We developed and validated a deep learning (DL)-based model using the segmentation method and assessed its ability to detect lung cancer on chest radiographs. Chest radiographs for use as a training dataset and a test dataset were collected separatel...
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...
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...
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