Detection of artificial pulmonary lung nodules in ultralow-dose CT using an ex vivo lung phantom.
Ontology highlight
ABSTRACT:
SUBMITTER: Burgard CA
PROVIDER: S-EPMC5752031 | biostudies-other | 2018
REPOSITORIES: biostudies-other
Publications
Detection of artificial pulmonary lung nodules in ultralow-dose CT using an ex vivo lung phantom.
Burgard Caroline Alexandra CA Gaass Thomas T Bonert Madeleine M Bondesson David D Thaens Natalie N Reiser Maximilian Ferdinand MF Dinkel Julien J
PloS one 20180103 1
<h4>Objectives</h4>To assess the image quality of 3 different ultralow-dose CT protocols on pulmonary nodule depiction in a ventilated ex vivo-system.<h4>Materials and methods</h4>Four porcine lungs were inflated inside a dedicated chest phantom and prepared with n = 195 artificial nodules (0.5-1 mL). The artificial chest wall was filled with water to simulate the absorption of a human chest. Images were acquired with a 2x192-row detector CT using low-dose (reference protocol with a tube voltage ...[more]
Similar Datasets
Project description:PurposeTo develop a radiomics prediction model to improve pulmonary nodule (PN) classification in low-dose CT. To compare the model with the American College of Radiology (ACR) Lung CT Screening Reporting and Data System (Lung-RADS) for early detection of lung cancer.MethodsWe examined a set of 72 PNs (31 benign and 41 malignant) from the Lung Image Database Consortium image collection (LIDC-IDRI). One hundred three CT radiomic features were extracted from each PN. Before the model building process, distinctive features were identified using a hierarchical clustering method. We then constructed a prediction model by using a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO). A tenfold cross-validation (CV) was repeated ten times (10 × 10-fold CV) to evaluate the accuracy of the SVM-LASSO model. Finally, the best model from the 10 × 10-fold CV was further evaluated using 20 × 5- and 50 × 2-fold CVs.ResultsThe best SVM-LASSO model consisted of only two features: the bounding box anterior-posterior dimension (BB_AP) and the standard deviation of inverse difference moment (SD_IDM). The BB_AP measured the extension of a PN in the anterior-posterior direction and was highly correlated (r = 0.94) with the PN size. The SD_IDM was a texture feature that measured the directional variation of the local homogeneity feature IDM. Univariate analysis showed that both features were statistically significant and discriminative (P = 0.00013 and 0.000038, respectively). PNs with larger BB_AP or smaller SD_IDM were more likely malignant. The 10 × 10-fold CV of the best SVM model using the two features achieved an accuracy of 84.6% and 0.89 AUC. By comparison, Lung-RADS achieved an accuracy of 72.2% and 0.77 AUC using four features (size, type, calcification, and spiculation). The prediction improvement of SVM-LASSO comparing to Lung-RADS was statistically significant (McNemar's test P = 0.026). Lung-RADS misclassified 19 cases because it was mainly based on PN size, whereas the SVM-LASSO model correctly classified 10 of these cases by combining a size (BB_AP) feature and a texture (SD_IDM) feature. The performance of the SVM-LASSO model was stable when leaving more patients out with five- and twofold CVs (accuracy 84.1% and 81.6%, respectively).ConclusionWe developed an SVM-LASSO model to predict malignancy of PNs with two CT radiomic features. We demonstrated that the model achieved an accuracy of 84.6%, which was 12.4% higher than Lung-RADS.
| S-EPMC5903960 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT.MethodsA total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen's kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis.ResultsAgreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen's kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942).ConclusionWe conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.
| S-EPMC7931546 | biostudies-literature
Project description:PurposeTo investigate image quality and bronchial wall quantification in low- and ultralow-dose third-generation dual-source computed tomography (CT).MethodsA lung specimen from a formerly healthy male was scanned using third-generation dual-source CT at standard-dose (51 mAs/120 kV, CTDIvol 3.41 mGy), low-dose (1/4th and 1/10th of standard dose), and ultralow-dose setting (1/20th). Low kV (70, 80, 90, and Sn100 kV) scanning was applied in each low/ultralow-dose setting, combined with adaptive mAs to keep a constant dose. Images were reconstructed at advanced modeled iterative reconstruction (ADMIRE) levels 1, 3, and 5 for each scan. Bronchial wall were semi-automatically measured from the lobar level to subsegmental level. Spearman correlation analysis was performed between bronchial wall quantification (wall thickness and wall area percentage) and protocol settings (dose, kV, and ADMIRE). ANOVA with a post hoc pairwise test was used to compare signal-to-noise ratio (SNR), noise and bronchial wall quantification values among standard- and low/ultralow-dose settings, and among ADMIRE levels.ResultsBronchial wall quantification had no correlation with dose level, kV, or ADMIRE level (|correlation coefficients| < 0.3). SNR and noise showed no statistically significant differences at different kV in the same ADMIRE level (1, 3, or 5) and in the same dose group (P > 0.05). Generally, there were no significant differences in bronchial wall quantification among the standard- and low/ultralow-dose settings, and among different ADMIRE levels (P > 0.05).ConclusionThe combined use of low/ultralow-dose scanning and ADMIRE does not influence bronchial wall quantification compared to standard-dose CT. This specimen study suggests the potential that an ultralow-dose scan can be used for bronchial wall quantification.
| S-EPMC7592972 | biostudies-literature
Project description:IntroductionLung cancer screening in individuals at risk has been recommended by various scientific institutions. One of the main concerns for CT screening is repeated radiation exposure, with the risk of inducing malignancies in healthy individuals. Therefore, lowering the radiation dose is one of the main objectives for radiologists. The aim of this study is to demonstrate that an ultra-low dose (ULD) chest CT protocol, using recently introduced hybrid iterative reconstruction (ASiR-V, GE medical Healthcare, Milwaukee, Wisconsin, USA), is as performant as a standard 'low dose' (LD) CT to detect non-calcified lung nodules ≥4 mm.Methods and analysisThe total number of patients to include is 150. Those are referred for non-enhanced chest CT for detection or follow-up of lung nodule and will undergo an additional unenhanced ULD CT acquisition, the dose of which is on average 10 times lower than the conventional LD acquisition. Total dose of the entire exam (LD+ULD) is lower than the French diagnostic reference level for a chest CT (6.65 millisievert). ULD CT images will be reconstructed with 50% and 100% ASiR-V and LD CT with 50%. The three sets of images will be read in random order by two pair of radiologists, in a blind test, where patient identification and study outcomes are concealed. Detection rate (sensitivity) is the primary outcome. Secondary outcomes will include concordance of nodule characteristics; interobserver reproducibility; influence of subjects' characteristics, nodule location and nodule size; and concordance of emphysema, coronary calcifications evaluated by visual scoring and bronchial alterations between LD and ULD CT. In case of discordance, a third radiologist will arbitrate.Ethics and disseminationThe study was approved by the relevant ethical committee. Each study participant will sign an informed consent form.Trial registration numberNCT03305978; Pre-results.
| S-EPMC6701577 | biostudies-literature
Project description:Background There is a wide variation in radiation dose levels that can be used with chest CT in order to detect indeterminate pulmonary nodules. Purpose To compare the performance of lower-radiation-dose chest CT with that of routine dose in the detection of indeterminate pulmonary nodules 5 mm or greater. Materials and Methods In this retrospective study, CT projection data from 83 routine-dose chest CT examinations performed in 83 patients (120 kV, 70 quality reference mAs [QRM]) were collected between November 2013 and April 2014. Reference indeterminate pulmonary nodules were identified by two nonreader thoracic radiologists. By using validated noise insertion, five lower-dose data sets were reconstructed with filtered back projection (FBP) or iterative reconstruction (IR; 30 QRM with FBP, 10 QRM with IR, 5 QRM with FBP, 5 QRM with IR, and 2.5 QRM with IR). Three thoracic radiologists circled pulmonary nodules, rating confidence that the nodule was a 5-mm-or-greater indeterminate pulmonary nodule, and graded image quality. Analysis was performed on a per-nodule basis by using jackknife alternative free-response receiver operating characteristic figure of merit (FOM) and noninferiority limit of -0.10. Results There were 66 indeterminate pulmonary nodules (mean size, 8.6 mm ± 3.4 [standard deviation]; 21 part-solid nodules) in 42 patients (mean age, 51 years ± 17; 21 men and 21 women). Compared with the FOM for routine-dose CT (size-specific dose estimate, 6.5 mGy ± 1.8; FOM, 0.86 [95% confidence interval: 0.80, 0.91]), FOM was noninferior for all lower-dose configurations except for 2.5 QRM with IR. The sensitivity for subsolid nodules at 70 QRM was 60% (range, 48%-72%) and was significantly worse at a dose of 5 QRM and lower, whether or not IR was used (P < .05). Diagnostic image quality decreased with decreasing dose (P < .001) and was better with IR at 5 QRM (P < .05). Conclusion CT images reconstructed at dose levels down to 10 quality reference mAs (size-specific dose estimate, 0.9 mGy) had noninferior performance compared with routine dose in depicting pulmonary nodules. Iterative reconstruction improved subjective image quality but not performance at low dose levels. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by White and Kazerooni in this issue.
| S-EPMC7706885 | biostudies-literature
Project description:Purpose18F-FDG PET/CT is widely used to evaluate indeterminate pulmonary nodules (IPNs). False positive results occur, especially from active granulomatous nodules. A PET-based imaging agent with superior specificity to 18F-FDG for IPNs, is badly needed, especially in areas of endemic granulomatous nodules. Somatostatin receptors (SSTR) are expressed in many malignant cells including small cell and non-small cell lung cancers (NSCLCs). 68Ga-DOTATATE, a positron emitter labeled somatostatin analog, combined with PET/CT imaging, may improve the diagnosis of IPNs over 18F-FDG by reducing false positives. Our study purpose was to test this hypothesis in our region with high endemic granulomatous IPNs.MethodsWe prospectively performed 68Ga-DOTATATE PET/CT and 18F-FDG PET/CT scans in the same 30 patients with newly diagnosed, treatment-naïve lung cancer (N = 14) or IPNs (N = 15) and one metastatic nodule. 68Ga-DOTATATE SUVmax levels at or above 1.5 were considered likely malignant. We analyzed the scan results, correlating with ultimate diagnosis via biopsy or 2-year chest CT follow-up. We also correlated 68Ga-DOTATATE uptake with immunohistochemical (IHC) staining for SSTR subtype 2A (SSTR2A) in pathological specimens.ResultsWe analyzed 31 lesions in 30 individuals, with 14 (45%) being non-neuroendocrine lung cancers and 1 (3%) being metastatic disease. McNemar's result comparing the two radiopharmaceuticals (p = 0.65) indicates that their accuracy of diagnosis in this indication are equivalent. 68Ga-DOTATATE was more specific (94% compared to 81%) and less sensitive 73% compared to 93%) than 18F-FDG. 68Ga-DOTATATE uptake correlated with SSTR2A expression in tumor stroma determined by immunohistochemical (IHC) staining in 5 of 9 (55%) NSCLCs.Conclusion68Ga-DOTATATE and 18F-FDG PET/CT had equivalent accuracy in the diagnosis of non-neuroendocrine lung cancer and 68Ga-DOTATATE was more specific than 18F-FDG for the diagnosis of IPNs. IHC staining for SSTR2A receptor expression correlated with tumor stroma but not tumor cells.
| S-EPMC5300187 | biostudies-literature
Project description:OBJECTIVES:To systematically evaluate the influence of acquisition settings in conjunction with raw-data based iterative image reconstruction (IR) on lung densitometry based on multi-row detector computed tomography (CT) in an anthropomorphic chest phantom. MATERIALS AND METHODS:Ten porcine heart-lung explants were mounted in an ex vivo chest phantom shell, six with highly and four with low attenuating chest wall. CT (Somatom Definition Flash, Siemens Healthineers) was performed at 120kVp and 80kVp, each combined with current-time products of 120, 60, 30, and 12mAs, and was reconstructed with filtered back projection (FBP) and IR (Safire, Siemens Healthineers). Mean lung density (LD), air density (AD) and noise were measured by semi-automated region-of interest (ROI) analysis, with 120kVp/120 mAs serving as the standard of reference. RESULTS:Using IR, noise in lung parenchyma was reduced by ~ 31% at high attenuating chest wall and by ~ 22% at low attenuating chest wall compared to FBP, respectively (p<0.05). IR induced changes in the order of ±1 HU to mean absolute LD and AD compared to corresponding FBP reconstructions which were statistically significant (p<0.05). CONCLUSIONS:Densitometry is influenced by acquisition parameters and reconstruction algorithms to a degree that may be clinically negligible. However, in longitudinal studies and clinical research identical protocols and potentially other measures for calibration may be required.
| S-EPMC7428081 | biostudies-literature
Project description:OBJECTIVES: The aim of this phantom study was to minimize the radiation dose by finding the best combination of low tube current and low voltage that would result in accurate volume measurements when compared to standard CT imaging without significantly decreasing the sensitivity of detecting lung nodules both with and without the assistance of CAD. METHODS: An anthropomorphic chest phantom containing artificial solid and ground glass nodules (GGNs, 5-12 mm) was examined with a 64-row multi-detector CT scanner with three tube currents of 100, 50 and 25 mAs in combination with three tube voltages of 120, 100 and 80 kVp. This resulted in eight different protocols that were then compared to standard CT sensitivity (100 mAs/120 kVp). For each protocol, at least 127 different nodules were scanned in 21-25 phantoms. The nodules were analyzed in two separate sessions by three independent, blinded radiologists and computer-aided detection (CAD) software. RESULTS: The mean sensitivity of the radiologists for identifying solid lung nodules on a standard CT was 89.7% ± 4.9%. The sensitivity was not significantly impaired when the tube and current voltage were lowered at the same time, except at the lowest exposure level of 25 mAs/80 kVp [80.6% ± 4.3% (p = 0.031)]. Compared to the standard CT, the sensitivity for detecting GGNs was significantly lower at all dose levels when the voltage was 80 kVp; this result was independent of the tube current. The CAD significantly increased the radiologists' sensitivity for detecting solid nodules at all dose levels (5-11%). No significant volume measurement errors (VMEs) were documented for the radiologists or the CAD software at any dose level. CONCLUSIONS: Our results suggest a CT protocol with 25 mAs and 100 kVp is optimal for detecting solid and ground glass nodules in lung cancer screening. The use of CAD software is highly recommended at all dose levels.
| S-EPMC3873253 | biostudies-literature
Project description:ObjectiveCT serves as gold standard for the evaluation of pulmonary nodules. However, CT exposes patients to ionizing radiation, a concern especially in screening scenarios with repeated examinations. Due to recent technological advances, MRI emerges as a potential alternative for lung imaging using 3D steady state free precession and ultra-short echo-time sequences. Therefore, in this study we assessed the performance of three state-of-the-art MRI sequences for the evaluation of pulmonary nodules.MethodsLesions of variable sizes were simulated in porcine lungs placed in a dedicated chest phantom mimicking a human thorax, followed by CT and MRI examinations. Two blinded readers evaluated the acquired MR-images locating and measuring every suspect lesion. Using the CT-images as reference, logistic regression was performed to investigate the sensitivity of the tested MRI-sequences for the detection of pulmonary nodules.ResultsFor nodules with a diameter of 6 mm, all three sequences achieved high sensitivity values above 0.91. However, the sensitivity dropped for smaller nodules, yielding an average of 0.83 for lesions with 4 mm in diameter and less than 0.69 for lesions with 2 mm in diameter. The positive predictive values ranged between 0.91 and 0.96, indicating a low amount of false positive findings. Furthermore, the size measurements done on the MR-images were subject to a bias ranging from 0.83 mm to -1.77 mm with standard deviations ranging from 1.40 mm to 2.11 mm. There was no statistically significant difference between the three tested sequences.ConclusionWhile showing promising sensitivity values for lesions larger than 4 mm, MRI appears to be not yet suited for lung cancer screening. Nonetheless, the three tested MRI sequences yielded high positive predictive values and accurate size measurements; therefore, MRI could potentially figure as imaging method of the chest in selected follow-up scenarios, e.g. of incidental findings subject to the Fleischner Criteria.
| S-EPMC7757901 | biostudies-literature
Project description:Purpose. The purpose of this study was to evaluate the technical and diagnostic performance of sub-milliSievert ultralow-dose (ULD) CT colonograpy (CTC) in the detection of colonic and extracolonic lesions. Materials and Methods. CTC with standard dose (SD) and ULD acquisitions of 64 matched patients, half of them with colonic findings, were reconstructed with filtered back projection (FBP), hybrid (HIR) and iterative model reconstruction techniques (IMR). Image noise in six colonic segments, in the left psoas muscle and aorta were measured. Image quality of the left adrenal gland and of the colon in the endoscopic and 2D view was rated on a five point Likert scale by two observers, who also completed the reading of CTC for colonic and extracolonic findings. Results. The mean radiation dose estimate was 4.1 ± 1.4 mSv for SD and 0.86 ± 0.17 mSv for ULD for both positions (p < 0.0001). In ULD-IMR, SD-IMR and SD-HIR, the endoluminal noise was decreased in all colonic segments compared to SD-FBP (p < 0.001). There were 27 small (6-9 mm) and 17 large (≥10 mm) colonic lesions that were classified as sessile polyps (n = 38), flat lesions (n = 3), or as a mass (n = 3). Per patient sensitivity and specificity were 0.82 and 0.93 for ULD-FBP, 0.97 and 0.97 for ULD-HIR, 0.97 and 1.0 for ULD-IMR. Per polyp sensitivity was 0.84 for ULD-FBP, 0.98 for ULD-HIR, 0.98 for ULD-IMR. Significantly less extracolonic findings were detected in ULD-FBP and ULD-HIR, but in the E4 category by C-RADS (potentially important findings), the detection was similar. Conclusion. Both HIR and IMR are suitable for sub-milliSievert ULD CTC without sacrificing diagnostic performance of the study.
| S-EPMC4824919 | biostudies-literature