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Rationale and Objectives: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA)., Materials and Methods: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type., Results: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall., Conclusion: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria., (Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: Median survival of patients with idiopathic pulmonary fibrosis (IPF) is 2-5 years. Sensitive imaging metrics can play a role in detecting early changes in therapeutic development. The aim of the present study was to compare known computed tomography (CT) histogram kurtosis and a classifier-based quantitative score to assess baseline severity and change over time in patients with IPF., Materials and Methods: A total of 57 patients with at least baseline and paired follow-up scans were selected from an imaging database of standardized CT scans obtained from patients with IPF. CT histogram measurement of kurtosis and quantitative lung fibrosis (QLF) and quantitative interstitial lung disease (QILD) scores from a classification algorithm were calculated. Spearman rank correlations were used to assess associations between baseline severity and changes for all CT-derived measures compared to forced vital capacity (FVC) and carbon monoxide diffusion capacity (DLCO) (percent predicted)., Results: At baseline, mean (±SD) of kurtosis was 2.43 (±1.83). Mean (±SD) values of QLF and QILD scores were 20.7% (±13.4) and 43.3% (±20.0), respectively. All baseline histogram indices and QLF and QILD scores were correlated well with baseline FVC and DLCO. When assessing associations with changes in FVC and DLCO over time, only QLF score was statistically significant (ρ = -0.57; P < .0001 for FVC and ρ = -0.34; P = .025 for DLCO), whereas kurtosis was not., Conclusions: Classifier-model-derived scores (QLF and QILD), based on a set of texture features, are associated with baseline disease extent and are also a sensitive measure of change over time. A QLF score can be used for measuring the extent of disease severity and longitudinal changes., (Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: To estimate and statistically compare the bias and variance of radiologists measuring the size of spherical and complex synthetic nodules., Materials and Methods: This study did not require the institutional review board approval. Six radiologists estimated the size of 10 synthetic nodules embedded within an anthropomorphic thorax phantom from computed tomography scans at 0.8- and 5-mm slice thicknesses. The readers measured the nodule size using unidimensional (1D) longest in-slice dimension, bidimensional (2D) area from longest in-slice and longest perpendicular dimension, and three-dimensional (3D) semiautomated volume. Intercomparisons of bias (difference between average and true size) and variance among methods were performed after converting the 2D and 3D estimates to a compatible 1D scale., Results: The relative biases of radiologists with the 3D tool were -1.8%, -0.4%, -0.7%, -0.4%, and -1.6% for 10-mm spherical, 20-mm spherical, 20-mm elliptical, 10-mm lobulated, and 10-mm spiculated nodules compared to 1.4%, -0.1%, -26.5%, -7.8%, and -39.8% for 1D. The three-dimensional measurements were significantly less biased than 1D for elliptical, lobulated, and spiculated nodules. The relative standard deviations for 3D were 7.5%, 3.9%, 3.6%, 9.7%, and 8.3% compared to 5.7%, 2.6%, 20.3%, 5.3%, and 16.4% for 1D. Unidimensional sizing was significantly less variable than 3D for the lobulated nodule and significantly more variable for the ellipsoid and spiculated nodules. Three-dimensional bias and variability were smaller for thin 0.8-mm slice data compared to thick 5.0-mm data., Conclusions: The study shows that radiologist-controlled 3D volumetric lesion sizing can not only achieve smaller bias but also achieve similar or smaller variability compared to 1D sizing, especially for complex lesion shapes., (Published by Elsevier Inc.)

Rationale and Objectives: Lung and lobar volume measurements from computed tomographic (CT) imaging are being used in clinical trials to assess new minimally invasive emphysema treatments aiming to reduce lung volumes. Establishing the reproducibility of lung volume measurements is important if they are to be accepted as treatment planning and outcome variables. The aims of this study were to (1) investigate the correlation between lung volumes assessed on CT imaging and on pulmonary function testing (PFT), (2) compare the two methods' reproducibility, and (3) assess the reproducibility of CT lobar volumes., Materials and Methods: CT imaging and body plethysmography were performed at baseline and after a 9-month interval in multicenter emphysema treatment trials. Lung volumes were measured at total lung capacity (TLC) and at residual volume (RV). Lobar volumes were measured on CT imaging using a semiautomated technique. The correlations between CT and PFT volumes were computed for 486 subjects at baseline. Reproducibility was assessed in terms of the intraclass correlation coefficient (ICC) for 126 subjects from the control group at TLC and 120 subjects at RV., Results: Correlations between CT and PFT lung volumes were 0.86 at TLC and 0.67 at RV. At TLC, the ICCs were 0.943 for CT imaging and 0.814 for PFT. At RV, the ICCs were 0.886 for CT imaging and 0.683 for PFT. CT lobar volumes showed good reproducibility (all P values < .05)., Conclusion: CT lung and lobar volume measurements could be captured in a multicenter trial setting with high reproducibility and were highly correlated with those obtained on PFT. CT imaging showed significantly better reproducibility than PFT between interval lung volume measurements, offering the potential for designing emphysema treatment trials involving fewer subjects.

Rationale and Objectives: New ways to understand biology as well as increasing interest in personalized treatments requires new capabilities for the assessment of therapy response. The lack of consensus methods and qualification evidence needed for large-scale multicenter trials, and in turn the standardization that allows them, are widely acknowledged to be the limiting factor in the deployment of qualified imaging biomarkers., Materials and Methods: The Quantitative Imaging Biomarker Alliance is organized to establish a methodology whereby multiple stakeholders collaborate. It has charged the Volumetric Computed Tomography (CT) Technical Subcommittee with investigating the technical feasibility and clinical value of quantifying changes over time in either volume or other parameters as biomarkers. The group selected solid tumors of the chest in subjects with lung cancer as its first case in point. Success is defined as sufficiently rigorous improvements in CT-based outcome measures to allow individual patients in clinical settings to switch treatments sooner if they are no longer responding to their current regimens, and reduce the costs of evaluating investigational new drugs to treat lung cancer., Results: The team has completed a systems engineering analysis, has begun a roadmap of experimental groundwork, documented profile claims and protocols, and documented a process for imaging biomarker qualification as a general paradigm for qualifying other imaging biomarkers as well., Conclusion: This report addresses a procedural template for the qualification of quantitative imaging biomarkers. This mechanism is cost-effective for stakeholders while simultaneously advancing the public health by promoting the use of measures that prove effective.

Rationale and Objectives: To retrospectively investigate the prevalence of tracheal collapse in an emphysema cohort. The occurrence of a large degree of tracheal collapse may have important implications for the clinical management of respiratory symptoms and air trapping in patients with emphysema., Materials and Methods: Paired full-inspiratory and end-expiratory thin-section volumetric computed tomographic scans were available for 1071 long-term smokers with clinically and physiologically confirmed emphysema. The percentage reduction in the cross-sectional tracheal luminal area from full-inspiration to end-expiration was automatically computed at 2.5-mm intervals along the centerline of the trachea using customized software., Results: Maximal tracheal collapse did not follow a normal distribution in the emphysema cohort (P < .0001, skewness/kurtosis tests for normality); the median collapse was 18% (intraquartile range, 11%-30%). Statistically significant differences were found in the distribution of maximal collapse by gender (P < .005, Wilcoxon rank sum test). Overall, 10.5% of men and 17.1% of women showed evidence of tracheomalacia on the basis of the criterion of a reduction of 50% or greater in cross-sectional tracheal luminal area at end-expiration., Conclusion: This study offers insights into the prevalence of tracheal collapse in a cohort of patients with emphysema; future work is needed to determine the possible relationship between tracheal collapse and air trapping in subjects with emphysema.

Rationale and Objectives: Segmentation of lungs using high-resolution computer tomographic images in the setting of diffuse lung diseases is a major challenge in medical image analysis. Threshold-based techniques tend to leave out lung regions that have increased attenuation, such as in the presence of interstitial lung disease. In contrast, streak artifacts can cause the lung segmentation to "leak" into the chest wall. The purpose of this work was to perform segmentation of the lungs using a technique that selects an optimal threshold for a given patient by comparing the curvature of the lung boundary to that of the ribs., Methods: Our automated technique goes beyond fixed threshold-based approaches to include lung boundary curvature features. One would expect the curvature of the ribs and the curvature of the lung boundary around the ribs to be very close. Initially, the ribs are segmented by applying a threshold algorithm followed by morphologic operations. The lung segmentation scheme uses a multithreshold iterative approach. The threshold value is verified until the curvature of the ribs and the curvature of the lung boundary are closely matched. The curve of the ribs is represented using polynomial interpolation, and the lung boundary is matched in such a way that there is minimal deviation from this representation. Performance of this technique was compared with conventional (fixed threshold) lung segmentation techniques on 25 subjects using a volumetric overlap fraction measure., Results: The performance of the rib segmentation technique was significantly different from conventional techniques with an average higher mean volumetric overlap fraction of about 5%., Conclusions: The technique described here allows for accurate quantification of volumetric computed tomography and more advanced segmentation of abnormal areas.

Rationale and Objectives: Computerized classification techniques have been developed to offer accurate and robust pattern recognition in interstitial lung disease using texture features. However, these techniques still present challenges when analyzing computed tomographic (CT) image data from multiprotocols because of disparate acquisition protocols or from standardized, multicenter clinical trials because of noise variability. Our objective is to investigate the utility of denoising thin section CT image data to improve the classification of scleroderma disease patterns. The patterns are lung fibrosis (LF), groundglass (GG), honeycomb (HC), or normal lung (NL) within small regions of interest (ROIs)., Methods: High-resolution CT images were scanned in a multicenter clinical trial for the Scleroderma Lung Study. A thoracic radiologist contoured a training set (38 patients) consisting of 148 ROIs with 46 LF, 85 GG, 4 HC, and 13 NL patterns and contoured a test set (33 new patients) consisting of 132 ROIs with 44 LF, 72 GG, 4 HC, and 12 NL patterns. The corresponding CT slices of a contoured ROI were denoised using Aujol's mathematic partial differential equation algorithm. The algorithm's noise parameter was estimated as the standard deviation of grey-level signal (in Hounsfield units) in a homogeneous, non-lung region: the aorta. Within each contoured ROI, every pixel within a 4 x 4 neighborhood was sampled (4 x 4 grid sampling). All sampled pixels from a contoured ROI were assumed to be the same disease pattern as labeled by the radiologist. 5,690 pixels (3,009 LF, 1,994 GG, 348 HC, and 339 NL) and 5,045 pixels (2,665 LF, 1,753 GG, 291 HC, and 336 NL) were sampled in training and test sets, respectively. Next, 58 texture features from the original and denoised image were calculated for each pixel. Using a multinomial logistic model, subsets of features (one from original and another from denoised images) were selected to classify disease patterns. Finally, pixels were classified into disease patterns using a support vector machine procedure., Results: From the training set, multinomial logistic model selected 45 features from the original images and 38 features from denoised images to classify disease patterns. Using the test set, the overall pixel classification rate by SVM increased from 87.8% to 89.5% with denoising. The specific classification rates (original/denoised) were 96.3/96.4% for LF, 88.8/89.4% for GG, 21.3/28.9% for HC, and 73.5/88.4% for NL. Denoising significantly improved the NL and overall classification rates (P = .037 and P = .047 respectively) at ROI level., Conclusions: Analyzing multicenter data using a denoising approach led to more parsimonious classification models with increasing accuracy. This approach offers a novel alternate classification strategy for heterogeneous technical and disease components. Furthermore, the model offers the potential to discriminate the multiple patterns of scleroderma disease correctly.

Rationale and Objectives: We sought to determine how measures of nodule diameter and volume on computed tomography (CT) vary with changes in inspiratory level., Materials and Methods: CT scans were performed with inspiration suspended at total lung capacity (TLC) and then at residual volume (RV) in 41 subjects, in whom 75 indeterminate lung nodules were detected. A fully automated contouring program was used to segment the lungs; followed by segmentation of all nodules and the corresponding lobe using semiautomated contouring in both TLC and RV scans. The percent changes in lung and lobar volumes between TLC and RV were correlated with percent changes in nodule diameters and volumes., Results: Both nodule diameter and volume varied nonuniformly from TLC to RV-some nodules decreased in size, while others increased. There was a 16.8% mean change in absolute volume across all nodules. Stratified by size, the mean value of the absolute percent volume changes for nodules > or =5 mm and <5 mm were not significantly different (P = .26). Stratified by maximum attenuation, the mean value of the absolute percent volume changes between the TLC and RV series for noncalcified (17.7%, SD = 13.1) and completely calcified nodules (8.6% SD = 5.7) were significantly different (P < .05)., Conclusion: Significant differences in nodule size were measured between TLC and RV scans. This has important implications for standardizing acquisition protocols in any setting where size and, more important, size change are being used for purposes of lung cancer staging, nodule characterization, or treatment response assessment.

Rationale and Objectives: To investigate the utility of a computer-aided diagnosis (CAD) in the task of differentiating malignant nodules from benign nodules based on quantitative features extracted from volumetric thin section CT image data acquired before and after the injection of contrast media., Materials and Methods: 35 volumetric CT datasets of solitary pulmonary nodules (SPN) with proven diagnoses (19 malignant/16 benign) were contoured by a thoracic radiologist. All studies had at least a baseline series obtained without contrast media and at least one series following an intravenous contrast injection at 45, 90, 180, and 360 seconds. Two separate contours were created for each nodule: one including only the solid portion and another including the ground-glass component, if any, of the nodule. For each contour 31 features were calculated that measured the attenuation, shape, and enhancement of the nodule due to the injection of contrast. These features were input into a feature selection step and three different classifiers to determine if the diagnosis could be predicted from the resulting feature vector. In addition, observer input was introduced to two of the classifiers as an a priori probability of malignancy and the resulting performance was compared. Training and testing was conducted in a resubstitution and leave-one-out fashion and performance was evaluated using ROC analysis., Results: In a leave-one-out testing methodology, the classifiers achieved areas under the ROC curves AZ that ranged from 0.69 to 0.92. A classifier based on logistic regression performed the best with an AZ of 0.92 while a classifier based on quadratic discriminant analysis performed the poorest (AZ, 0.69). The AZ increased when using a priori observer input in most cases reaching a maximum of 0.95., Conclusion: Based on this initial work with a limited number of nodules in our dataset, it appears that CAD using volumetric and contrast-enhanced data has the potential to assist radiologists in the task of differentiating solitary pulmonary nodules and in the management of these patients. Further studies with an increased number of patients are required to validate these results.

Rationale and Objectives: The objective is to study the incremental effects of using a computer-aided lung nodule detection (CAD) system on the performance of a large pool of observers., Materials and Methods: A set of eight thin-section computed tomographic data sets with limited longitudinal coverage, containing a total of 22 lung nodules, was analyzed by using the automated nodule detection system. When applied to all eight cases, the CAD system alone achieved a detection rate of 86.4%, with 2.64 false-positive results per case. This study included 202 observers at a national radiology meeting: 39 thoracic radiologists, 95 non-thoracic radiologists, and 68 non-radiologists. Each participant read from one to eight cases in random order, first without and then with CAD system output available. Observer performance in nodule detection was measured before and after CAD was made available. Differences in performance of groups of observers before and after CAD were tabulated by mean, median, and SD in detection rate and number of false-positive results and tested by using nonparametric methods., Results: In an analysis involving only the first randomly selected case read by all 202 participants, there were statistically significant increases in nodule detection rates and numbers of false-positive results for all types of observers. There was a significant difference in detection rates between radiologists and non-radiologists before CAD, but after CAD, there was no significant difference in detection rates between these observer types. In a second analysis involving 13 participants who read all eight cases, mean detection rates were 64.0% before CAD and 81.9% after CAD. Mean numbers of false-positive results were 0.144 per case before CAD and 0.173 after CAD., Conclusion: In a large observer study, use of a CAD system for nodule detection resulted in an incremental increase in detection rate, but also led to an increase in number of false-positive results. Also, CAD appears to be an equalizer of detection rates between observers of different levels of experience.

Rationale and Objectives: We sought to investigate the utility of a computer-aided diagnosis in the task of differentiating malignant nodules from benign nodules based on single thin-section computed tomography image data., Materials and Methods: Eighty-one thin-section computed tomography data sets of solitary pulmonary nodules with proven diagnoses (48 malignant and 33 benign) were contoured manually on a single representative slice by a thoracic radiologist (>10 years of experience). Two separate contours were created for each nodule, one including only the solid portion of the nodule and one including any ground-glass components. For each contour 75 features were calculated that measured the attenuation, shape, and texture of the nodule. These features were than input into a feature selection step and four different classifiers to determine if the diagnosis could be predicted from the feature vector. Training and testing was conducted in a resubstitution and leave-one-out fashion and performance was evaluated using ROC techniques., Results: In a leave-one-out testing methodology the classifiers resulted with areas under the ROC curve (A(Z)) that ranged from 0.68 to 0.92. When evaluating with resubstitution the A(Z) ranged from 0.93 to 1.00., Conclusion: Computer-aided diagnosis has the potential to assist radiologists in the task of differentiating solitary pulmonary nodules and in the management of these patients.

Rationale and Objectives: To investigate the performance of observers with different levels of experience in distinguishing between benign and malignant solitary pulmonary nodules (SPN) on CT, and to determine the effects on interpretation of three different conditions: image data alone, the addition of clinical data, and the addition of output from a computer-aided diagnosis (CAD) system., Materials and Methods: 28 thin-section CT datasets of SPNs with proven diagnoses (15 malignant and 13 benign) were used to measure observer performance. Readers were categorized according to their experience and read the cases in random order. For each case readers were asked to assign a level of confidence on a scale from 0.0-1.0 (0.0 benign, 1.0 malignant) for the diagnosis of the nodule. Each reader scored the cases based on review of image data alone (phase 1), then with limited clinical data (phase 2), and finally with CAD output (phase 3). To assess performance, multiple reader multiple case (MRMC) receiver operating characteristic (ROC) analysis was used., Results: 2 thoracic radiologists, 1 thoracic radiology fellow, 2 nonthoracic radiologists, and 3 radiology residents read the cases. The average area under the ROC curve for all readers (A(z)) at each stage was 0.68, 0.75, and 0.81, for image data alone, with clinical data, and with CAD output respectively. The difference in performance between phases (2 and 3) and (1 and 3) was significantly different (P = 0.018 and P = 0.020). However, the difference between phases (1 and 2) was not significantly different (P = 0.155)., Conclusion: Diagnostic performance increased significantly with the addition of CAD output. With further validation CAD output may play a significant role in SPN management.