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.

The SF-36 avoids the "ceiling effect" of most disability scales and provides a valid measure of physical and mental health after stroke, but it does not appear to characterize well social functioning. Thus, the instrument may need to be supplemented by other measures for a comprehensive assessment of stroke outcome.

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A population-based sample (N = 787) was followed for 8 years and assessed three times on measures of well-being (depressive symptoms, morale, and control) and fall history. Marginal models assessed the association between baseline well-being measures and falling. Random effects models assessed change in well-being indicators as well as change in fall rate over 8 years. After adjustment for sociodemographics, psychotropic medication, health, and sensorimotor function, our results showed that depressive symptoms, control, and morale were risk factors for subsequent falling, and an increase in depressive symptoms or a reduction in morale was associated with an increasing fall rate. We conclude that the three well-being measures are independently associated with falling and need to be considered in fall-risk assessments and population-based prevention and intervention strategies.

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