Comparison of the effects of model-based iterative reconstruction and filtered back projection algorithms on software measurements in pulmonary subsolid nodules.

OBJECTIVES: To evaluate the differences between filtered back projection (FBP) and model-based iterative reconstruction (MBIR) algorithms on semi-automatic measurements in subsolid nodules (SSNs). METHODS: Unenhanced CT scans of 73 SSNs obtained using the same protocol and reconstructed with both FBP and MBIR algorithms were evaluated by two radiologists. Diameter, mean attenuation, mass and volume of whole nodules and their solid components were measured. Intra- and interobserver variability and differences between FBP and MBIR were then evaluated using Bland-Altman method and Wilcoxon tests. RESULTS: Longest diameter, volume and mass of nodules and those of their solid components were significantly higher using MBIR (p < 0.05) with mean differences of 1.1% (limits of agreement, -6.4 to 8.5%), 3.2% (-20.9 to 27.3%) and 2.9% (-16.9 to 22.7%) and 3.2% (-20.5 to 27%), 6.3% (-51.9 to 64.6%), 6.6% (-50.1 to 63.3%), respectively. The limits of agreement between FBP and MBIR were within the range of intra- and interobserver variability for both algorithms with respect to the diameter, volume and mass of nodules and their solid components. There were no significant differences in intra- or interobserver variability between FBP and MBIR (p > 0.05). CONCLUSION: Semi-automatic measurements of SSNs significantly differed between FBP and MBIR; however, the differences were within the range of measurement variability. KEY POINTS: • Intra- and interobserver reproducibility of measurements did not differ between FBP and MBIR. • Differences in SSNs' semi-automatic measurement induced by reconstruction algorithms were not clinically significant. • Semi-automatic measurement may be conducted regardless of reconstruction algorithm. • SSNs' semi-automated classification agreement (pure vs. part-solid) did not significantly differ between algorithms.

Software performance in segmenting ground-glass and solid components of subsolid nodules in pulmonary adenocarcinomas.

OBJECTIVE: To evaluate the performance of software in segmenting ground-glass and solid components of subsolid nodules in pulmonary adenocarcinomas. METHOD: Seventy-three pulmonary adenocarcinomas manifesting as subsolid nodules were included. Two radiologists measured the maximal axial diameter of the ground-glass components on lung windows and that of the solid components on lung and mediastinal windows. Nodules were segmented using software by applying five (-850 HU to -650 HU) and nine (-130 HU to -500 HU) attenuation thresholds. We compared the manual and software measurements of ground-glass and solid components with pathology measurements of tumour and invasive components. RESULTS: Segmentation of ground-glass components at a threshold of -750 HU yielded mean differences of +0.06 mm (p = 0.83, 95 % limits of agreement, 4.51 to 4.67) and -2.32 mm (p < 0.001, -8.27 to 3.63) when compared with pathology and manual measurements, respectively. For solid components, mean differences between the software (at -350 HU) and pathology measurements and between the manual (lung and mediastinal windows) and pathology measurements were -0.12 mm (p = 0.74, -5.73 to 5.55]), 0.15 mm (p = 0.73, -6.92 to 7.22), and -1.14 mm (p < 0.001, -7.93 to 5.64), respectively. CONCLUSION: Software segmentation of ground-glass and solid components in subsolid nodules showed no significant difference with pathology. KEY POINTS: • Software can effectively segment ground-glass and solid components in subsolid nodules. • Software measurements show no significant difference with pathology measurements. • Manual measurements are more accurate on lung windows than on mediastinal windows.

Serum potassium concentration predicts brain hypoxia on CT after avalanche-induced cardiac arrest.

BACKGROUND: Brain anoxia after complete avalanche burial and cardiac arrest (CA) may occur despite adequate on-site triage. PURPOSE: To investigate clinical and biological parameters associated with brain hypoxia in a cohort of avalanche victims with whole body computed tomographic (CT) scan. METHODS: Retrospective study of patients with CA and whole body CT scan following complete avalanche burial admitted in a level-I trauma center. MAIN FINDINGS: Out of 19 buried patients with whole body CT scan, eight patients had refractory CA and 11 patients had pre-hospital return of spontaneous circulation. Six patients survived at hospital discharge and only two had good neurologic outcome. Twelve patients had signs of brain hypoxia on initial CT scan, defined as brain edema, loss of gray/white matter differentiation and/or hypodensity of basal ganglia. No clinical pre-hospital parameter was associated with brain anoxia. Serum potassium concentration at admission was higher in patients with brain anoxia as compared to patients with normal CT scan: 5.5 (4.1-7.2) mmol/L versus 3.3 (3.0-4.2) mmol/L, respectively (P<.01). A threshold of 4.35 mmol/L serum potassium had 100% specificity to predict brain anoxia on brain CT scan. CONCLUSIONS: Serum potassium concentration had good predictive value for brain anoxia after complete avalanche burial. This finding further supports the use of serum potassium concentration for extracorporeal life support insertion at hospital admission in this context.

Nasal High Flow at 25 L/min or Expiratory Resistive Load Do Not Improve Regional Lung Function in Patients With COPD: A Functional CT Imaging Study.

BACKGROUND: Nasal high flow (NHF) is a non-invasive breathing therapy that is based on the delivery via a large-caliber nasal cannula of heated and humidified air at flow rates that exceed peak inspiratory flow. It is thought that positive airway pressure generated by NHF can help reduce gas trapping and improve regional lung ventilation. There are no data to confirm this hypothesis at flow rates applicable in stable chronic obstructive pulmonary disease (COPD) patients. METHODS: In this study, we used non-rigid registration of computed tomography (CT) images acquired at maximal expiration and inspiration to compute regional lung attenuation changes (ΔHU), and lung displacement (LD), indices of regional lung ventilation. Parametric response maps (Galban et al., 2012) were also computed in each experimental condition. Eight COPD patients were assessed at baseline (BL) and after 5 min of NHF and expiratory resistive loading (ERL). RESULTS: ΔHU was: BL (median, IQR): 85 (67.2, 102.8); NHF: 90.7 (57.4, 97.6); ERL: 74.6 (46.4, 89.6) HU (p = 0.531); and LD: 27.8 (22.3, 39.3); 17.6 (15.4, 27.9); and 20.4 (16.6, 23.6) mm (p = 0.120) in the 3 conditions, respectively. No significant difference in trapping was observed. Respiratory rate significantly decreased with both treatments [BL: 17.3 (16.4, 18.9); NHF: 13.7; ERL: 11.4 (9.6, 13.2) bpm; and p < 0.001]. CONCLUSION: Neither NHF at 25 L/min nor ERL significantly improved the regional lung ventilation of stable COPD patients with gas trapping, based on functional lung CT imaging. Further study including more subjects is needed to assess the potential effect of NHF on regional lung function at higher flow rates. CLINICAL TRIAL REGISTRATION: www.clinicaltrials.gov/under, identifier NCT03821311.

Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs.

Importance: Interpretation of chest radiographs is a challenging task prone to errors, requiring expert readers. An automated system that can accurately classify chest radiographs may help streamline the clinical workflow. Objectives: To develop a deep learning-based algorithm that can classify normal and abnormal results from chest radiographs with major thoracic diseases including pulmonary malignant neoplasm, active tuberculosis, pneumonia, and pneumothorax and to validate the algorithm's performance using independent data sets. Design, Setting, and Participants: This diagnostic study developed a deep learning-based algorithm using single-center data collected between November 1, 2016, and January 31, 2017. The algorithm was externally validated with multicenter data collected between May 1 and July 31, 2018. A total of 54 221 chest radiographs with normal findings from 47 917 individuals (21 556 men and 26 361 women; mean [SD] age, 51 [16] years) and 35 613 chest radiographs with abnormal findings from 14 102 individuals (8373 men and 5729 women; mean [SD] age, 62 [15] years) were used to develop the algorithm. A total of 486 chest radiographs with normal results and 529 with abnormal results (1 from each participant; 628 men and 387 women; mean [SD] age, 53 [18] years) from 5 institutions were used for external validation. Fifteen physicians, including nonradiology physicians, board-certified radiologists, and thoracic radiologists, participated in observer performance testing. Data were analyzed in August 2018. Exposures: Deep learning-based algorithm. Main Outcomes and Measures: Image-wise classification performances measured by area under the receiver operating characteristic curve; lesion-wise localization performances measured by area under the alternative free-response receiver operating characteristic curve. Results: The algorithm demonstrated a median (range) area under the curve of 0.979 (0.973-1.000) for image-wise classification and 0.972 (0.923-0.985) for lesion-wise localization; the algorithm demonstrated significantly higher performance than all 3 physician groups in both image-wise classification (0.983 vs 0.814-0.932; all P < .005) and lesion-wise localization (0.985 vs 0.781-0.907; all P < .001). Significant improvements in both image-wise classification (0.814-0.932 to 0.904-0.958; all P < .005) and lesion-wise localization (0.781-0.907 to 0.873-0.938; all P < .001) were observed in all 3 physician groups with assistance of the algorithm. Conclusions and Relevance: The algorithm consistently outperformed physicians, including thoracic radiologists, in the discrimination of chest radiographs with major thoracic diseases, demonstrating its potential to improve the quality and efficiency of clinical practice.

The effect of late-phase contrast enhancement on semi-automatic software measurements of CT attenuation and volume of part-solid nodules in lung adenocarcinomas.

OBJECTIVES: To evaluate the differences in semi-automatic measurements of CT attenuation and volume of part-solid nodules (PSNs) between unenhanced and enhanced CT scans. MATERIALS AND METHODS: CT scans including unenhanced and enhanced phases (slice thickness 0.625 and 1.25mm, respectively) for 53 adenocarcinomas presenting as PSNs in 50 patients were retrospectively evaluated. For each nodule, semi-automatic segmentation provided the diameter, mean attenuation, mass, and volume of a whole nodule and its solid component. Interscan variability and statistical significance of the differences in those measures according to the adenocarcinoma category were evaluated by one reader. RESULTS: All parameters except for the mean attenuation of the solid components, were significantly increased on enhanced CT (p<0.05). For the whole nodule, the mean relative differences were as follows: the longest diameter, 1.4% (limits of agreement, -6.2-9.1); volume, 2.4% (-26.7-31.4); mass, 7.0% (-11.3-25.2); mean attenuation, 2.7% (-5.6-11). For the nodule's solid component, those differences were as follow: the longest diameter, 6.9% (-34.4-48.2); volume, 17.9% (-77.8-113.7); mass, 18.8% (-77.8-115.4). The differences of measures between the unenhanced and enhanced CT were not significantly different between two groups of adenocarcinoma in situ/minimally invasive adenocarcinomas and invasive adenocarcinomas (p>0.05). CONCLUSIONS: As most volumetric and attenuation measurements changed significantly after contrast enhancement, care should be taken in comparing unenhanced and enhanced CT in the evaluation of PSNs.

Differentiating pre- and minimally invasive from invasive adenocarcinoma using CT-features in persistent pulmonary part-solid nodules in Caucasian patients.

OBJECTIVE: To retrospectively investigate the diagnostic value of pre-operative CT-features between pre/minimally invasive and invasive lesions in part-solid persistent pulmonary ground glass nodules in a Caucasian population. MATERIALS AND METHODS: Retrospective review of two pre-operative CTs for 31 nodules in 30 patients. There were 10 adenocarcinomas in situ, 1 minimally invasive adenocarcinoma, 20 invasive adenocarcinomas. We analyzed the correlation between histopathology and the following CT-features: maximal axial diameter, maximal orthogonal axial diameter, height, density, size of solid component, air bronchogram, pleural retraction, nodule mass, disappearance rate and their evolution during follow-up. RESULTS: In univariate analysis, invasive adenocarcinomas had a higher maximal height, density, solid component size, mass, a lower disappearance rate and presented more often with pleural retraction (p<0.05). After logistic regression performed with the uncorrelated parameters using a method of selection of variables, only the size of solid component remained significant, with 100% sensitivity for invasive adenocarcinoma when larger than 5mm. CONCLUSION: Preoperative CT-features can help differentiating in situ and minimally invasive adenocarcinomas from invasive adenocarcinomas in Caucasian patients. A solid component larger than 5mm in diameter had 100% sensitivity for the diagnosis of invasive adenocarcinoma.