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Cancer of the lung and bronchus is the leading fatal malignancy in the United States. Five-year survival is low, but treatment of early stage disease considerably improves chances of survival. Advances in multidetector-row computed tomography technology provide detection of smaller lung nodules and offer a potentially effective screening tool. The large number of images per exam, however, requires considerable radiologist time for interpretation and is an impediment to clinical throughput. Thus, computer-aided diagnosis (CAD) methods are needed to assist radiologists with their decision making. To promote the development of CAD methods, the National Cancer Institute formed the Lung Image Database Consortium (LIDC). The LIDC is charged with developing the consensus and standards necessary to create an image database of multidetector-row computed tomography lung images as a resource for CAD researchers. To develop such a prospective database, its potential uses must be anticipated. The ultimate applications will influence the information that must be included along with the images, the relevant measures of algorithm performance, and the number of required images. In this article we outline assessment methodologies and statistical issues as they relate to several potential uses of the LIDC database. We review methods for performance assessment and discuss issues of defining "truth" as well as the complications that arise when truth information is not available. We also discuss issues about sizing and populating a database.

Purpose. The purpose of the study was to determine if use of a correction function derived from a phantom with known calcium density reduces variation in coronary artery calcium scores measured with electron-beam computed tomography (CT) and to evaluate the influence of body girth, as measured by body mass index (BMI), on coronary artery calcium-score variability in a clinical screening cohort. Materials and Methods. Two hundred fifty consecutive patients underwent two electron-beam CT examinations with a standardized phantom. Region-of-interest (ROI) measurements of the phantom were correlated with the patient BMI. In a subset of 28 patients with low but positive coronary artery calcium scores, ROI values of the phantom were used as a correction for signal attenuation. Results. Patient BMI was not significantly correlated to signal attenuation. Application of a correction function resulted in a small but insignificant decrease in interscan coronary artery calcium-score variation. The interscan coronary artery calcium score variation was 24.5% ± 23.9, it decreased to 24.3% ± 21.6 with use of the 75 mg/cm portion of the phantom and to 22.2% ± 21.4 with use of the 150 mg/cm 3 portion of the phantom. Conclusion. The use of a coronary artery calcium phantom does not reduce interscan variation sufficiently to warrant regular clinical usage. Interscan variation in the coronary artery calcium score is not significantly influenced by patient BMI.

Rationale and Objectives. The aim of this study was to determine a more precise and accurate method of quantitating coronary artery calcium (CAC) detected with electron-beam computed tomography (CT) in patients with low CAC scores. Materials and Methods. Two 40-section, 3-mm-collimation, electrocardiographically gated electron-beam CT examinations of the heart were performed in each patient. Fifty patients with average scores between 2 and 100, as determined with the conventional scoring algorithm, were selected. The modified conventional scoring algorithm was compared with two techniques: calculated calcium volume and approximated calcium mass. Results. The percentage difference between scans ranged from 37.2% for the conventional scoring method to 28.2% and 28.4% for volume- and mass-based methods, respectively. Increasing lesion size thresholds does not improve quantitative precision and reduces accuracy in patients with small amounts of CAC. Conclusion. Quantification methods based on calcification volume or mass decrease score variation compared with the conventional scoring method, and increased size threshold does not improve accuracy.

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.

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 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.

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.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.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.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.

Studies that evaluate the lung nodule detection performance of radiologists or computerized methods depend on an initial inventory of the nodules within the thoracic images (the "truth"). The purpose of this study was to analyze (1) variability in the "truth" defined by different combinations of experienced thoracic radiologists and (2) variability in the performance of other experienced thoracic radiologists based on these definitions of "truth" in the context of lung nodule detection in computed tomographic (CT) scans.Twenty-five thoracic CT scans were reviewed by four thoracic radiologists, who independently marked lesions they considered to be nodulesor=3 mm in maximum diameter. Panel "truth" sets of nodules were then derived from the nodules marked by different combinations of two and three of these four radiologists. The nodule detection performance of the other radiologists was evaluated based on these panel "truth" sets.The number of "true" nodules in the different panel "truth" sets ranged from 15 to 89 (mean 49.8 +/- 25.6). The mean radiologist nodule detection sensitivities across radiologists and panel "truth" sets for different panel "truth" conditions ranged from 51.0 to 83.2%; mean false-positive rates ranged from 0.33 to 1.39 per case.Substantial variability exists across radiologists in the task of lung nodule identification in CT scans. The definition of "truth" on which lung nodule detection studies are based must be carefully considered, because even experienced thoracic radiologists may not perform well when measured against the "truth" established by other experienced thoracic radiologists.

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. © AUR, 2008

Rationale and Objectives The American College of Radiology Imaging Network is participating in the National Lung Screening Trial, a large, multicenter, randomized controlled trial, comparing multidetector helical computed tomography (MDCT) versus chest radiography (CXR) in screening for lung cancer. Because the threshold for detection of disease is an inherent function of image quality, and consistent image quality is necessary to track changes in suspicious findings, our purpose was to develop an image quality control (QC) program across all clinical sites for both modalities. Materials and Methods The primary goals of the QC program include standardization of imaging protocols, certification of imaging equipment, and ongoing, periodic evaluation of the equipment calibration and image quality. Minimum standards for equipment and standardized cross-platform acquisition protocols are achieved via radiologist and physicist attestation forms and web-distributed technique charts, respectively. Imaging equipment performance standards are implemented through an initial machine certification process that includes equipment calibration. Ongoing assessment of equipment performance and calibration, as well as adherence to established imaging protocols. is accomplished via periodic submission of calibration records and phantom images. Participant-specific image acquisition parameters are entered into a web-based centralized database and variations from established protocols are automatically flagged for review. Participant radiation dose can be estimated from the image acquisition parameters applied to the imaging equipment calibration measurements. A radiologist visual review committee also evaluates participant images for diagnostic quality. Data are collected from 23 independent centers, representing 14 models of MDCT scanners from four manufacturers, and CXR systems that include film-screen, computed radiography, and direct digital radiography systems. Results Widespread imaging protocol variation in extant clinical practice—as well as variability in equipment technology, image acquisition parameters, manufacturer terminology, and user interface—have required careful standardization as a prerequisite to trial participation and ongoing image QC. Acceptable ranges for image acquisition parameters have been refined to accommodate continuously evolving equipment platforms and the scope of participant size and body habitus. Conclusion Standardization of imaging protocols is a critical component of image-based clinical trials, predicated on ongoing dialogue between sites and a centralized review committee.

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 ≥5 mm and 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 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.

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.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.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.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.

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.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.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.Computer-aided diagnosis has the potential to assist radiologists in the task of differentiating solitary pulmonary nodules and in the management of these patients.

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.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.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).Diagnostic performance increased significantly with the addition of CAD output. With further validation CAD output may play a significant role in SPN management.

To study the agreement in treatment response classifications between unidimensional (1D), bidimensional (2D), and volumetric (3D) methods of measuring metastatic lung nodules on chest computed tomography (CT).Chest CT scans of 15 patients undergoing treatment for metastatic colorectal, renal cell, or breast carcinoma to the lungs were analyzed. CT images were acquired with 3 mm collimation and contiguous reconstruction. Two or three lung lesions were selected for each patient. Lesions were analyzed at baseline and two follow-up intervals of 1-4 months. 1D and 2D measurements were made with electronic calipers, while nodule volume was measured using a semiautomated segmentation system. Following the World Health Organization and RECIST (Response Evaluation Criteria in Solid Tumors) criteria, patients were categorized into four treatment response classifications. Volumetric criteria were used to classify response based on 3D measurements.Thirty-two lesions from 15 patients were analyzed. Because each patient had a baseline and two follow-up scans, this yielded 30 response classifications for each measurement technique. The 1D, 2D, and 3D measurements were concordant in 21 of 30 classifications. The 1D and 3D measurements were concordant in 29 of 30 classifications, while the 2D and 3D measurements were concordant in 23 of 30 classifications. Level of agreement among the three methods was measured using a kappa statistic (K). For 1D compared with 3D, K = 0.739 +/- 0.345 (visits 1, 2) and 0.273 +/- 0.323 (visits 2, 3). For 2D compared with 3D, K = 0.655 +/- 0.325 (visits 1, 2) and 0.200 +/- 0.208 (visits 2, 3). Agreement among the methods for round and ovoid nodules was also fair to poor.The three methods of tumor measurement show fair to poor agreement in treatment response classification. These findings have negative implications for the accuracy in which patients are classified under the World Health Organization or RECIST criteria and managed under cancer treatment protocols.