Search

Accurate assessment of thoracic aortic aneurysm (TAA) growth is important for appropriate clinical management. Maximal aortic diameter is the primary metric that is used to assess growth, but it suffers from substantial measurement variability. A recently proposed technique, termed vascular deformation mapping (VDM), is able to quantify three-dimensional aortic growth using clinical computed tomography angiography (CTA) data using an approach based on deformable image registration (DIR). However, the accuracy and robustness of VDM remains undefined given the lack of ground truth from clinical CTA data, and, furthermore, the performance of VDM relative to standard manual diameter measurements is unknown.To evaluate the performance of the VDM pipeline for quantifying aortic growth, we developed a novel and systematic evaluation process to generate 76 unique synthetic CTA growth phantoms (based on 10 unique cases) with variable degrees and locations of aortic wall deformation. Aortic deformation was quantified using two metrics: area ratio (AR), defined as the ratio of surface area in triangular mesh elements and the magnitude of deformation in the normal direction (DiN) relative to the aortic surface. Using these phantoms, we further investigated the effects on VDM's measurement accuracy resulting from factors that influence the quality of clinical CTA data such as respiratory translations, slice thickness, and image noise. Lastly, we compare the measurement error of VDM TAA growth assessments against two expert raters performing standard diameter measurements of synthetic phantom images.Across our population of 76 synthetic growth phantoms, the median absolute error was 0.063 (IQR: 0.073-0.054) for AR and 0.181 mm (interquartile range [IQR]: 0.214-0.143 mm) for DiN. Median relative error was 1.4% for AR andVDM yields an accurate, three-dimensional assessment of aortic growth in TAA patients and is robust to factors such as image noise, respiration-induced translations, and differences in patient position. Further, VDM significantly outperformed two expert manual raters in assessing the magnitude and location of aortic growth despite optimized experimental measurement conditions. These results support validation of the VDM technique for accurate quantification of aortic growth in patients and highlight several important advantages over diameter measurements.

Background Aortic diameter measurements in patients with a thoracic aortic aneurysm (TAA) show wide variation. There is no technique to quantify aortic growth in a three-dimensional (3D) manner. Purpose To validate a CT-based technique for quantification of 3D growth based on deformable registration in patients with TAA. Materials and Methods Patients with ascending and descending TAA with two or more CT angiography studies between 2006 and 2020 were retrospectively identified. The 3D aortic growth was quantified using vascular deformation mapping (VDM), a technique that uses deformable registration to warp a mesh constructed from baseline aortic anatomy. Growth assessments between VDM and clinical CT diameter measurements were compared. Aortic growth was quantified as the ratio of change in surface area at each mesh element (area ratio). Manual segmentations were performed by independent raters to assess interrater reproducibility. Registration error was assessed using manually placed landmarks. Agreement between VDM and clinical diameter measurements was assessed using Pearson correlation and Cohen κ coefficients. Results A total of 38 patients (68 surveillance intervals) were evaluated (mean age, 69 years ± 9 [standard deviation]; 21 women), with TAA involving the ascending aorta (n = 26), descending aorta (n = 10), or both (n = 2). VDM was technically successful in 35 of 38 (92%) patients and 58 of 68 intervals (85%). Median registration error was 0.77 mm (interquartile range, 0.54-1.10 mm). Interrater agreement was high for aortic segmentation (Dice similarity coefficient = 0.97 ± 0.02) and VDM-derived area ratio (bias = 0.0, limits of agreement: -0.03 to 0.03). There was strong agreement (r = 0.85, P < .001) between peak area ratio values and diameter change. VDM detected growth in 14 of 58 (24%) intervals. VDM revealed growth outside the maximally dilated segment in six of 14 (36%) growth intervals, none of which were detected with diameter measurements. Conclusion Vascular deformation mapping provided reliable and comprehensive quantitative assessment of three-dimensional aortic growth and growth patterns in patients with thoracic aortic aneurysms undergoing CT surveillance. Published under a CC BY 4.0 license Online supplemental material is available for this article. See also the editorial by Wieben in this issue.

Background Lung cancer risk prediction models do not routinely incorporate imaging metrics available on low-dose CT (LDCT) imaging of the chest ordered for lung cancer screening. Research Question What is the association between quantitative emphysema measured on LDCT imaging and lung cancer incidence and mortality, all-cause mortality, and airflow obstruction in individuals who currently or formerly smoked and are undergoing lung cancer screening? Study Design and Methods In 7,262 participants in the CT arm of the National Lung Screening Trial, percent low attenuation area (%LAA) was defined as the percentage of lung volume with voxels less than –950 Hounsfield units on the baseline examination. Multivariable Cox proportional hazards models, adjusting for competing risks where appropriate, were built to test for association between %LAA and lung cancer incidence, lung cancer mortality, and all-cause mortality with censoring at 6 years. In addition, multivariable logistic regression models were built to test the cross-sectional association between %LAA and airflow obstruction on spirometry, which was available in 2,700 participants. Results The median %LAA was 0.8% (interquartile range, 0.2%-2.7%). Every 1% increase in %LAA was independently associated with higher hazards of lung cancer incidence (hazard ratio [HR], 1.02; 95% CI, 1.01-1.03; P = .004), lung cancer mortality (HR, 1.02; 95% CI, 1.00-1.05; P = .045), and all-cause mortality (HR, 1.01; 95% CI, 1.00-1.03; P = .042). Among participants with spirometry, 892 had airflow obstruction. The likelihood of airflow obstruction increased with every 1% increase in %LAA (odds ratio, 1.07; 95% CI, 1.06-1.09; P 65 years. Interpretation Quantitative emphysema measured on LDCT imaging of the chest can be leveraged to improve lung cancer risk prediction and help diagnose COPD in individuals who currently or formerly smoked and are undergoing lung cancer screening.

Background: Little is known about factors associated with emphysema progression in cigarette smokers. We evaluated factors associated with change in emphysema and forced expiratory volume in 1 second (FEV(1)) in participants with and without chronic obstructive pulmonary disease (COPD). Methods: This retrospective study included individuals participating in the COPD Genetic Epidemiology study who completed the 5-year follow-up, including inspiratory and expiratory computed tomography (CT) and spirometry. All paired CT scans were analyzed using micro-mapping, which classifies individual voxels as emphysema or functional small airway disease (fSAD). Presence and progression of emphysema and FEV(1) were determined based on comparison to nonsmoker values. Logistic regression analyses were used to identify clinical parameters associated with disease progression. Results: A total of 3088 participants were included with a mean ± SD age of 60.7±8.9 years, including 72 nonsmokers. In all Global initiative for chronic Obstructive Lung Disease (GOLD) stages, the presence of emphysema at baseline was associated with emphysema progression (odds ratio [OR]: GOLD 0: 4.32; preserved ratio-impaired spirometry [PRISm]; 5.73; GOLD 1: 5.16; GOLD 2: 5.69; GOLD 3/4: 5.55; all p ≤0.01). If there was no emphysema at baseline, the amount of fSAD at baseline was associated with emphysema progression (OR for 1% increase: GOLD 0: 1.06; PRISm: 1.20; GOLD 1: 1.7; GOLD 3/4: 1.08; all p ≤ 0.03).In 1735 participants without spirometric COPD, progression in emphysema occurred in 105 (6.1%) participants and only 21 (1.2%) had progression in both emphysema and FEV(1). Conclusions: The presence of emphysema is an important predictor of emphysema progression. In patients without emphysema, fSAD is associated with the development of emphysema. In participants without spirometric COPD, emphysema progression occurred independently of FEV(1) decline.

Parametric response mapping (PRM) is a novel computed tomography (CT) technology that has shown potential for assessment of bronchiolitis obliterans syndrome (BOS) after hematopoietic stem cell transplantation (HCT). The primary aim of this study was to evaluate whether variations in image acquisition under real-world conditions affect the PRM measurements of clinically diagnosed BOS. CT scans were obtained retrospectively from 72 HCT recipients with BOS and graft-versus-host disease from Fred Hutchinson Cancer Research Center, Karolinska Institute, and the University of Michigan. Whole lung volumetric scans were performed at inspiration and expiration using site-specific acquisition and reconstruction protocols. PRM and pulmonary function measurements were assessed. Patients with moderately severe BOS at diagnosis (median forced expiratory volume at 1 second [FEV1] 53.5% predicted) had similar characteristics between sites. Variations in site-specific CT acquisition protocols had a negligible effect on the PRM-derived small airways disease (SAD), that is, BOS measurements. PRM-derived SAD was found to correlate with FEV1% predicted and FEV1/ forced vital capacity (R = -0.236, P = .046; and R = -0.689, P < .0001, respectively), which suggests that elevated levels in the PRM measurements are primarily affected by BOS airflow obstruction and not CT scan acquisition parameters. Based on these results, PRM may be applied broadly for post-HCT diagnosis and monitoring of BOS.

In chronic obstructive pulmonary disease (COPD) functional small airways disease (fSAD) has been identified as a transitional state between healthy lung and irremediable emphysema. Topological analysis of the CT-based parametric response map (PRM) spatially quantifies distribution and arrangement of fSAD and emphysema. We present a cross sectional analysis of topological PRM (tPRM) in 8,972 participants from the COPDGene study, covering a complete spectrum of COPD severity. We aimed to evaluate tPRM dynamics with respect to GOLD and test association of tPRM with pulmonary function using Spearman correlation and stepwise linear regression. Baseline CT and clinical data were included, and tPRM was computed as whole lung averages summarizing disease volume density, surface area, curvature and perforation (measured by Ⲭ). Strong correlation (ρ = -0.74, p < 0.001) was determined between degree of perforation in healthy lung (ⲬNorm) and fSAD regions (ⲬfSAD), transposing from predominantly healthy lung perforated with fSAD (GOLD 2, ⲬNorm = -0.84, ⲬfSAD = 0.47) to fSAD perforated with healthy lung (GOLD 4, ⲬNorm = 0.39, ⲬfSAD = -0.36). tPRM significantly (p < 0.05) contributed to regression modelling of FEV1/FVC (R2 = 0.71), where surface area of emphysema had the most notable effect (β = -0.49, p < 0.01). Thus, tPRM provided insight into topology of fSAD and emphysema in COPD and associated statistically with clinical lung function measures.

Chronic rejection of lung allografts has two major subtypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), which present radiologically either as air trapping with small airways disease or with persistent pleuroparenchymal opacities. Parametric response mapping (PRM), a computed tomography (CT) methodology, has been demonstrated as an objective readout of BOS and RAS and bears prognostic importance, but has yet to be correlated to biological measures. Using a topological technique, we evaluate the distribution and arrangement of PRM-derived classifications of pulmonary abnormalities from lung transplant recipients undergoing redo-transplantation for end-stage BOS (N = 6) or RAS (N = 6). Topological metrics were determined from each PRM classification and compared to structural and biological markers determined from microCT and histopathology of lung core samples. Whole-lung measurements of PRM-defined functional small airways disease (fSAD), which serves as a readout of BOS, were significantly elevated in BOS versus RAS patients (p = 0.01). At the core-level, PRM-defined parenchymal disease, a potential readout of RAS, was found to correlate to neutrophil and collagen I levels (p < 0.05). We demonstrate the relationship of structural and biological markers to the CT-based distribution and arrangement of PRM-derived readouts of BOS and RAS. ispartof: CELLS vol:11 issue:4 ispartof: location:Switzerland status: published

Rationale: Evidence suggests damage to small airways is a key pathologic lesion in chronic obstructive pulmonary disease (COPD). Computed tomography densitometry has been demonstrated to identify e...

Despite good performance for medical image segmentation, deep convolutional neural networks (CNNs) have not been widely accepted in clinical practice as they are complex and tend to fail silently. Additionally, uncertainty in their predictions are not well understood, making them obscure and challenging to interpret. Automatically detecting possible failures in network predictions is important, as we can refer such cases for manual inspection or correction by human observers. In this paper, we analyse the uncertainty for deep CNN-based lung lobe segmentation in computed tomography (CT) scans by proposing a test-time augmentation-based aleatoric uncertainty measure. Through this analysis, we produce spatial uncertainty maps, from which a clinician can observe where and why a system thinks it is failing, and quantify the image-level prediction of failure. Our results show that such an uncertainty measure is highly correlated to segmentation accuracy and therefore presents an inherent measure of segmentation quality.

Abstract Impaired single breath carbon monoxide diffusing capacity (DLCO) is associated with emphysema. Small airways disease (SAD) may be a precursor lesion to emphysema, but the relationship between SAD and DLCO is undescribed. We hypothesized that in mild COPD, functional SAD (fSAD) defined by computed tomography (CT) and Parametric Response Mapping methodology would correlate with impaired DLCO. Using data from ever-smokers in the COPDGene cohort, we established that fSAD correlated significantly with lower DLCO among both non-obstructed and GOLD 1–2 subjects. The relationship between DLCO with CT-defined emphysema was present in all GOLD stages, but most prominent in severe disease. Trial registration NCT00608764. Registry: COPDGene. Registered 06 February 2008, retrospectively registered.

PURPOSE: Multiple commercial, open-source, and academic software tools exist for objective quantification of lung density in computed tomography (CT) images. The purpose of this study was to evaluate the intersoftware reproducibility of CT lung density measurements. METHODS: Computed tomography images from 50 participants from the COPDGene™ cohort study were randomly selected for analysis; n = 10 participants across each global initiative for chronic obstructive lung disease (GOLD) grade (GOLD 0–IV). Academic-based groups (n = 4) and commercial vendors (n = 4) participated anonymously to generate CT lung density measurements using their software tools. Computed tomography total lung volume (TLV), percentage of the low attenuation areas in the lung with Hounsfield unit (HU) values below −950HU (LAA(950)), and the HU value corresponding to the 15th percentile on the parenchymal density histogram (Perc15) were included in the analysis. The intersoftware bias and reproducibility coefficient (RDC) was generated with and without quality assurance (QA) for manual correction of the lung segmentation; intrasoftware bias and RDC was also generated by repeated measurements on the same images. RESULTS: Intersoftware mean bias was within ±0.22 mL, ±0.46%, and ±0.97 HU for TLV, LAA(950) and Perc15, respectively. The RDC was 0.35 L, 1.2% and 1.8 HU for TLV, LAA(950) and Perc15, respectively. Intersoftware RDC remained unchanged following QA: 0.35 L, 1.2% and 1.8 HU for TLV, LAA(950) and Perc15, respectively. All software investigated had an intrasoftware RDC of 0. The RDC was comparable for TLV, LAA(950) and Perc15 measurements, respectively, for academic-based groups/commercial vendor-based software tools: 0.39 L/0.32 L, 1.2%/1.2%, and 1.7 HU/1.6 HU. Multivariable regression analysis showed that academic-based software tools had greater within-subject standard deviation of TLV than commercial vendors, but no significant differences between academic and commercial groups were found for LAA(950) or Perc15 measurements. CONCLUSIONS: Computed tomography total lung volume and lung density measurement bias and reproducibility was reported across eight different software tools. Bias was negligible across vendors, reproducibility was comparable for software tools generated by academic-based groups and commercial vendors, and segmentation QA had negligible impact on measurement variability between software tools. In summary, results from this study report the amount of additional measurement variability that should be accounted for when using different software tools to measure lung density longitudinally with well-standardized image acquisition protocols. However, intrasoftware reproducibility was deterministic for all cases so use of the same software tool to reduce variability for serial studies is highly recommended.

In this work, a convolutional neural network (CNN) based method for automated lung boundary estimation from computed tomography (CT) scans is presented and validated. The CNN model was trained to regress the locations of the superior and inferior borders of the lungs from multiple tissue-specific 2D projections of thoracic CT images. The model utilized a DenseNet architecture and was trained and evaluated on CT images from the COPDGene study. The median (95th percentile) localization error was 2.51 (11.18) for the inferior border and 1.52 (7.21) for the superior border of the lungs.

Rationale and Objectives Chronic obstructive pulmonary disease is a heterogeneous disease characterized by small airway abnormality and emphysema. We hypothesized that a voxel-wise computed tomography analytic approach would identify patterns of disease progression in smokers. Materials and Methods We analyzed 725 smokers in spirometric GOLD stages 0-4 with two chest CTs 5 years apart. Baseline inspiration, follow-up inspiration and follow-up expiration images were spatially registered to baseline expiration so that each voxel had correspondences across all time points and respiratory phases. Voxel-wise Parametric Response Mapping (PRM) was then generated for the baseline and follow-up scans. PRM classifies lung as normal, functional small airway disease (PRMfSAD), and emphysema (PRMEMPH). Results Subjects with low baseline PRMfSAD and PRMEMPH predominantly had an increase in PRMfSAD on follow-up; those with higher baseline PRMfSAD and PRMEMPH mostly had increases in PRMEMPH. For GOLD 0 participants (n = 419), mean 5-year increases in PRMfSAD and PRMEMPH were 0.3% for both; for GOLD 1–4 participants (n = 306), they were 0.6% and 1.6%, respectively. Eighty GOLD 0 subjects (19.1%) had overall radiologic progression (30.0% to PRMfSAD, 52.5% to PRMEMPH, and 17.5% to both); 153 GOLD 1–4 subjects (50.0%) experienced progression (17.6% to PRMfSAD, 48.4% to PRMEMPH, and 34.0% to both). In a multivariable model, both baseline PRMfSAD and PRMEMPH were associated with development of PRMEMPH on follow-up, although this relationship was diminished at higher levels of baseline PRMEMPH. Conclusion A voxel-wise longitudinal PRM analytic approach can identify patterns of disease progression in smokers with and without chronic obstructive pulmonary disease.

PURPOSE: To evaluate the reproducibility and predicted clinical outcomes of CT-based quantitative lung density measurements using standard fixed-dose (FD) and reduced-dose (RD) scans. MATERIALS AND METHODS: In this retrospective analysis of prospectively acquired data, 1205 participants (mean age, 65 years ± 9 [standard deviation]; 618 men) enrolled in the COPDGene study who underwent FD and RD CT image acquisition protocols between November 2014 and July 2017 were included. Of these, the RD scans of 640 participants were also reconstructed using iterative reconstruction (IR). Median filtering was applied to the RD scans (RD-MF) to investigate an alternative noise reduction strategy. CT attenuation at the 15th percentile of the lung CT histogram (Perc15) was computed for all image types (FD, RD, RD-MF, and RD-IR). Reproducibility coefficients were calculated to quantify the measurement differences between FD and RD scans. The ability of Perc15 to predict chronic obstructive pulmonary disease (COPD) diagnosis and exacerbation frequency was investigated using receiver operating characteristic analysis. RESULTS: The Perc15 reproducibility coefficients with and without volume adjustment were as follows: RD, 29.43 HU ± 0.62 versus 32.81 HU ± 1.70; RD-MF, 7.42 HU ± 0.42 versus 19.40 HU ± 2.65; and RD-IR, 7.10 HU ± 0.52 versus 22.46 HU ± 3.91. Receiver operating characteristic curve analysis indicated that Perc15 on volume-adjusted FD and RD scans were both predictive for COPD diagnosis (area under the receiver operating characteristic curve [AUC]: FD, 0.724 ± 0.045; RD, 0.739 ± 0.045) and for having one or more exacerbation per year (AUCs: FD, 0.593 ± 0.068; RD, 0.589 ± 0.066). Similar trends were observed when volume adjustment was not applied. CONCLUSION: A combination of volume adjustment and noise reduction filtering improved the reproducibility of lung density measurements computed using serial FD and RD CT scans. Supplemental material is available for this article. © RSNA, 2021

Background Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression. Objective To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. Materials and methods Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model. Results QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach. Conclusion The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

Purpose: Lung cancer has been the leading cause of cancer-related deaths worldwide. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMMs) have become integral in identifying and evaluating unique pathways that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer aided detection tools, for the accurate and efficient analysis of these histopathology images. Our work demonstrates a simple machine learning approach called sparse principal component analysis (PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Methods: Our method comprises four steps: 1) cascaded sparse PCA; 2) graph-based PCA hashing; 3) block-wise histograms; and 4) support vector machine (SVM) classification. In our proposed architecture, sparse PCA is employed to learn the filter banks of the multiple stages. This is followed by a graph-based PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this sparse PCA are then fed to an SVM classifier. We tested the proposed sparse PCA network on H&E slides obtained from an inducible KrasG12D lung cancer mouse model. Our dataset consists of N = 21 whole slide histopathology lung images with 9 non-tumor bearing control mice and 12 mice with visible lung tumors. Tumor lesions from 12 lung images with visible tumors were visually identified by three trained individuals, which served as ground truth. The size of each image in our dataset is 2048 × 2048 pixels. Each image was divided into non-overlapping image patches of size 20 × 20 pixels consisting of a total of 12,361 cancer lesion patches and 207,839 non-cancer patches. We used 50% of the data for training and 50% of the data for testing our proposed sparse PCA network. We evaluated our algorithm using conventional metrics that have been used for evaluation of classification algorithms, namely precision (P), recall (R), and coverage measure (F-score). Results: The automatic cancer lesion detection results were compared with manually annotated ground truth. The proposed method achieves a cancer lesion detection accuracy of 97.98% with P = 0.8624, R = 0.9062 and F-score = 0.8790. The proposed method was found to take on average 17 minutes to train and learn a good representation for accurate and efficient classification of cancerous lesions within the images. Conclusion: We demonstrated a simple machine learning methodology for detection of cancerous lesions within histopathological lung images. Experimental results show that the proposed method is able to classify the regions of interest both efficiently and accurately. Future work will focus on feature extraction of individual tumors and tumor location within lungs. Citation Format: Sundaresh Ram, Wenfei Tang, Alexander J. Bell, Cara Spencer, Alexander Buschhuas, Charles R. Hatt, Marina P. di Magliano, Stefanie Galban, Craig J. Galban. Detection of cancer lesions in histopathological lung images using a sparse PCA network [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-086.