The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

PURPOSE: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) 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 ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =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 "nodule > or =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.

Implementation and use of a web-based interface for confidential communication of data between the clinical and research environments.

Policies and regulations in the current health care environment have impacted the manner in which patient data - especially protected health information (PHI) - are handled in the clinical and research settings. Specifically, it is now more challenging to obtain de-identified PHI from the clinic for use in research while still adhering to the requirements dictated by the new policies and regulations. To meet this challenge, we have designed and implemented a novel web-based interface that uses a workflow model to manage the communication of data (for example, biopsy results) between the clinic and research environments without revealing PHI to the research team or associated research identifiers to the clinical collaborators. At the heart of the scheme is a web application that coordinates message passing between researchers and clinical collaborators by use of a protocol that protects confidentiality. We describe the design requirements of the messaging/communication protocol, as well as implementation details of the web application and its associated database. We conclude that this scheme provides a useful communication mechanism that facilitates clinical research while maintaining confidentiality of patient data.

The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation.

RATIONALE AND OBJECTIVES: The Lung Image Database Consortium (LIDC) is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource to promote the development of computer-aided detection or characterization of pulmonary nodules. To obtain the best estimate of the location and spatial extent of lung nodules, expert thoracic radiologists reviewed and annotated each scan. Because a consensus panel approach was neither feasible nor desirable, a unique two-phase, multicenter data collection process was developed to allow multiple radiologists at different centers to asynchronously review and annotate each CT scan. This data collection process was also intended to capture the variability among readers. MATERIALS AND METHODS: Four radiologists reviewed each scan using the following process. In the first or "blinded" phase, each radiologist reviewed the CT scan independently. In the second or "unblinded" review phase, results from all four blinded reviews were compiled and presented to each radiologist for a second review, allowing the radiologists to review their own annotations together with the annotations of the other radiologists. The results of each radiologist's unblinded review were compiled to form the final unblinded review. An XML-based message system was developed to communicate the results of each reading. RESULTS: This two-phase data collection process was designed, tested, and implemented across the LIDC. More than 500 CT scans have been read and annotated using this method by four expert readers; these scans either are currently publicly available at http://ncia.nci.nih.gov or will be in the near future. CONCLUSIONS: A unique data collection process was developed, tested, and implemented that allowed multiple readers at distributed sites to asynchronously review CT scans multiple times. This process captured the opinions of each reader regarding the location and spatial extent of lung nodules.

A web-based interface for communication of data between the clinical and research environments without revealing identifying information.

RATIONALE AND OBJECTIVES: Recent health care policies and regulations have affected the manner in which patient data--especially protected health information (PHI)--are handled in both the clinical and research settings. Specifically, it is now more challenging to obtain de-identified PHI from the clinic for use in research while adhering to the requirements of this new environment. MATERIALS AND METHODS: To meet this challenge, we have devised a novel web-based interface that facilitates the communication of data (eg, biopsy results) between the clinic and research environments without revealing PHI to the research team or associated research identifiers to the clinical collaborators. At the heart of the scheme is a web application that coordinates message passing between the researchers (in general, the requesters of de-identified PHI) and clinical collaborators (who have access to PHI) by use of a protocol that protects confidentiality. RESULTS: We describe the design requirements of this communication scheme and present implementation details of the web application and its associated database. CONCLUSIONS: We conclude that this scheme provides a useful communication mechanism that facilitates clinical research while maintaining confidentiality of patient data.