Dataset Information

Optimization of a chest computed tomography protocol for detecting pure ground glass opacity nodules: A feasibility study with a computer-assisted detection system and a lung cancer screening phantom.

ABSTRACT: OBJECTIVE:This study aimed to optimize computed tomography (CT) parameters for detecting ground glass opacity nodules (GGNs) using a computer-assisted detection (CAD) system and a lung cancer screening phantom. METHODS:A lung cancer screening phantom containing 15 artificial GGNs (-630 Hounsfield unit [HU], 2-10 mm) in the left lung was examined with a CT scanner. Three tube voltages of 80, 100, and 120 kVp were used in combination with five tube currents of 25, 50, 100, 200, and 400 mA; additionally, three slice thicknesses of 0.625, 1.25, and 2.5 mm and four reconstruction algorithms of adaptive statistical iterative reconstruction (ASIR-V) of 30, 60, and 90% were used. For each protocol, accuracy of the CAD system was evaluated for nine target GGNs of 6, 8, or 10 mm in size. The cut-off size was set to 5 mm to minimize false positives. RESULTS:Among the 180 combinations of tube voltage, tube current, slice thickness, and reconstruction algorithms, combination of 80 kVp, 200 mA, and 1.25-mm slice thickness with an ASIR-V of 90% had the best performance in the detection of GGNs with six true positives and no false positives. Other combinations had fewer than five true positives. In particular, any combinations with a 0.625-mm slice thickness had 0 true positive and at least one false positive result. CONCLUSION:Low-voltage chest CT with a thin slice thickness and a high iterative reconstruction algorithm improve the detection rate of GGNs with a CAD system in a phantom model, and may have potential in lung cancer screening.

SUBMITTER: Kang S

PROVIDER: S-EPMC7244125 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Optimization of a chest computed tomography protocol for detecting pure ground glass opacity nodules: A feasibility study with a computer-assisted detection system and a lung cancer screening phantom.

Kang Seongmin S Kim Tae Hoon TH Shin Jae Min JM Han Kyunghwa K Kim Ji Young JY Min Baeggi B Park Chul Hwan CH

PloS one 20200522 5

<h4>Objective</h4>This study aimed to optimize computed tomography (CT) parameters for detecting ground glass opacity nodules (GGNs) using a computer-assisted detection (CAD) system and a lung cancer screening phantom.<h4>Methods</h4>A lung cancer screening phantom containing 15 artificial GGNs (-630 Hounsfield unit [HU], 2-10 mm) in the left lung was examined with a CT scanner. Three tube voltages of 80, 100, and 120 kVp were used in combination with five tube currents of 25, 50, 100, 200, and ...[more]

PMID: 32442174

Similar Datasets

Project description:ObjectivesTo investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).MethodsThis study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic-radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed.ResultsFive radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic-radiomics model (AUC 0.77; 95% CI, 0.69-0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62-0.81) and Rad-score (AUC 0.72; 95% CI, 0.63-0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model.ConclusionsThe proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs.Key points• CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic-radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.

Dataset Information

Optimization of a chest computed tomography protocol for detecting pure ground glass opacity nodules: A feasibility study with a computer-assisted detection system and a lung cancer screening phantom.

Publications

Optimization of a chest computed tomography protocol for detecting pure ground glass opacity nodules: A feasibility study with a computer-assisted detection system and a lung cancer screening phantom.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets