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.

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Measurements of lung tumor size on CT scans are often inconsistent and can lead to an incorrect interpretation of tumor response. Consistency can be improved if the same reader performs serial measurements for any one patient.

Purpose To evaluate three coronary artery calcification (CAC) scoring methods to assess risk of coronary heart disease (CHD) death and all-cause mortality in National Lung Screening Trial (NLST) participants across levels of CAC scores. Materials and Methods The NLST was approved by the institutional review board at each participating institution, and informed consent was obtained from all participants. Image review was HIPAA compliant. Five cardiothoracic radiologists evaluated 1575 low-dose computed tomographic (CT) scans from three groups: 210 CHD deaths, 315 deaths not from CHD, and 1050 participants who were alive at conclusion of the trial. Radiologists used three scoring methods: overall visual assessment, segmented vessel-specific scoring, and Agatston scoring. Weighted Cox proportional hazards models were fit to evaluate the association between scoring methods and outcomes. Results In multivariate analysis of time to CHD death, Agatston scores of 1–100, 101–1000, and greater than 1000 (reference category 0) were associated with hazard ratios of 1.27 (95% confidence interval: 0.69, 2.53), 3.57 (95% confidence interval: 2.14, 7.48), and 6.63 (95% confidence interval: 3.57, 14.97), respectively; hazard ratios for summed segmented vessel-specific scores of 1–5, 6–11, and 12–30 (reference category 0) were 1.72 (95% confidence interval: 1.05, 3.34), 5.11 (95% confidence interval: 2.92, 10.94), and 6.10 (95% confidence interval: 3.19, 14.05), respectively; and hazard ratios for overall visual assessment of mild, moderate, or heavy (reference category none) were 2.09 (95% confidence interval: 1.30, 4.16), 3.86 (95% confidence interval: 2.02, 8.20), and 6.95 (95% confidence interval: 3.73, 15.67), respectively. Conclusion By using low-dose CT performed for lung cancer screening in older, heavy smokers, a simple visual assessment of CAC can be generated for risk assessment of CHD death and all-cause mortality, which is comparable to Agatston scoring and strongly associated with outcome.

Benign chest wall tumors are uncommon lesions that originate from blood vessels, nerves, bone, cartilage, or fat. Chest radiography is an important technique for evaluation of such tumors, especially those that originate from bone, because it can depict mineralization and thus indicate the diagnosis. Computed tomography (CT) and magnetic resonance (MR) imaging are helpful in further delineating the location and extent of the tumor and in identifying tumor tissues and types. Although the radiologic manifestations of benign and malignant chest wall tumors frequently overlap, differences in characteristic location and appearance occasionally allow a differential diagnosis to be made with confidence. Such features include the presence of mature fat tissue with little or no septation (lipoma), the presence of phleboliths and characteristic vascular enhancement (cavernous hemangioma), evidence of neural origin combined with a targetlike appearance on MR images (neurofibroma), well-defined continuity of cortical and medullary bone with the site of origin (osteochondroma), or fusiform expansion and ground-glass matrix (fibrous dysplasia). Both aneurysmal bone cysts and giant cell tumors typically manifest as expansile osteolytic lesions and occasionally show fluid-fluid levels suggestive of diagnosis.