Purpose:The development of computer-aided diagnostic ͑CAD͒ methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography ͑CT͒ scans. The Lung Image Database Consortium ͑LIDC͒ and Image Database Resource Initiative ͑IDRI͒ completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute ͑NCI͒, further advanced by the Foundation for the National Institutes of Health ͑FNIH͒, and accompanied by the Food and Drug Administration ͑FDA͒ through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ͑"noduleՆ 3 mm," "noduleϽ 3 mm," and "non-noduleՆ 3 mm"͒. In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results:The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "noduleՆ 3 mm" by at least one radiologist, of which 928 ͑34.7%͒ received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions:The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

An important complication of osteoporosis is fracture. Alteration in bone structure, as well as decreased bone mass, contribute to the tendency to fracture in osteoporosis. Current methods that measure bone mass alone show substantial overlap of the measurements of osteoporotic patients who fracture with those that do not. Our aim is to develop noninvasive methods of evaluating bone structure on plain film radiographs to better predict fracture risk in osteoporosis. Regions of interest (ROIs) were selected from digitized lateral lumbar spine radiographs of 43 patients being seen in an osteoporosis clinic. The fractal dimension of these ROIs was estimated using a surface area method. The ability of fractal dimension to distinguish between cases that had fracture elsewhere in the spine from those that did not, was evaluated using receiver operating characteristic (ROC) analysis. These results were compared with ROC analysis for these same patients using bone mineral density (BMD) measurements (bone mass). Significantly larger Az (area under ROC curve) values were obtained using fractal dimension (0.87) than from using BMD (0.58), indicating a better test performance using fractal dimension. Therefore, computerized radiographic methods to evaluate bone structure, such as fractal analysis, may be helpful in better determining fracture risk in osteoporosis.

Measurement of bone mass is important in determining the risk for fracture and in following the course of patients undergoing therapy for osteoporosis. Bone mineral densitometry (BMD) is a good predictor of fracture risk, but there is considerable overlap in BMD measurements between individuals with fracture risk and those without. In this study, computerized texture analysis of the trabecular pattern on conventional spine radiographs was used to evaluate bone structure as a determinant of fracture risk. Standard lumbar spine radiographs of 43 individuals were analyzed and compared with BMD measurements obtained with dual-photon absorptiometry. This method was more effective than BMD in differentiation of patients with fractures elsewhere in the spine from those with no fracture. These preliminary results suggest that this method of bone structure analysis, combined with BMD, may lead to a more sensitive and specific predictor of osteoporosis and risk of fracture.

Rationale and Objectives-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 on computed tomography (CT) scans.Materials and Methods-Twenty-five thoracic CT scans were reviewed by four thoracic radiologists, who independently marked lesions they considered to be nodules ≥ 3 mm in maximum diameter. Panel "truth" sets of nodules then were 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.Results-The number of "true" nodules in the different panel "truth" sets ranged from 15-89 (mean: 49.8±25.6). The mean radiologist nodule-detection sensitivities across radiologists and panel "truth" sets for different panel "truth" conditions ranged from 51.0-83.2%; mean false-positive rates ranged from 0.33-1.39 per case.Conclusion-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