False-negative MRI breast screening in high-risk women.

AIM: To determine the frequency of and reasons for false-negative breast magnetic resonance imaging (MRI) examinations in high-risk women undergoing annual screening. MATERIALS AND METHODS: The family history clinic database was interrogated and women at high risk of breast cancer who had undergone screening MRI and been diagnosed with breast cancer within 2 years of the MRI examination were identified. All available MRI examinations were reviewed and classified by two radiologists. RESULTS: Of 32 women diagnosed with breast cancer, 23 had MRI images available for review. Fourteen were diagnosed at MRI, four at interim mammography, two symptomatically, one incidentally on ultrasound, and two at risk-reducing mastectomy. Ten women (43%) had potentially avoidable delays in diagnosis. The preceding MRIs were classified as false-negative screens in five women (one prevalent, four incident), false-negative assessment in seven and minimal signs in three (three women were assigned dual classifications). Common reasons for diagnostic delay included small enhancing masses that were overlooked, areas of non-mass enhancement that showed little or no change between screens, false reassurance from normal conventional imaging at assessment, and overreliance on short-interval repeat MRI. CONCLUSION: Small enhancing foci, masses, and areas of segmental non-mass enhancement are common MRI features of early breast cancer. Lack of change of non-mass enhancement on serial examinations does not exclude malignancy. Double reading of both screening and assessment examinations is recommended. Ready access to MRI biopsy is essential. Short-interval repeat MRI should be limited to reassessing low suspicion areas likely to be benign glandular enhancement. Annual mammography remains important in these women.

Towards structured sharing of raw and derived neuroimaging data across existing resources.

Data sharing efforts increasingly contribute to the acceleration of scientific discovery. Neuroimaging data is accumulating in distributed domain-specific databases and there is currently no integrated access mechanism nor an accepted format for the critically important meta-data that is necessary for making use of the combined, available neuroimaging data. In this manuscript, we present work from the Derived Data Working Group, an open-access group sponsored by the Biomedical Informatics Research Network (BIRN) and the International Neuroimaging Coordinating Facility (INCF) focused on practical tools for distributed access to neuroimaging data. The working group develops models and tools facilitating the structured interchange of neuroimaging meta-data and is making progress towards a unified set of tools for such data and meta-data exchange. We report on the key components required for integrated access to raw and derived neuroimaging data as well as associated meta-data and provenance across neuroimaging resources. The components include (1) a structured terminology that provides semantic context to data, (2) a formal data model for neuroimaging with robust tracking of data provenance, (3) a web service-based application programming interface (API) that provides a consistent mechanism to access and query the data model, and (4) a provenance library that can be used for the extraction of provenance data by image analysts and imaging software developers. We believe that the framework and set of tools outlined in this manuscript have great potential for solving many of the issues the neuroimaging community faces when sharing raw and derived neuroimaging data across the various existing database systems for the purpose of accelerating scientific discovery.

Optimal frequency of parathyroid hormone monitoring in chronic hemodialysis patients.

BACKGROUND: Secondary hyperparathyroidism is a common manifestation of chronic kidney disease (CKD). Serum parathyroid hormone (PTH) level is widely used as a marker for hyperparathyroidism. Currently, there is limited data to guide the frequency of PTH monitoring in CKD patients. The present study was undertaken to determine the optimal frequency of monitoring PTH in patients on maintenance hemodialysis. METHODS: A cohort of 154 patients on maintenance dialysis at a single outpatient hemodialysis center was included in this retrospective study. In Phase I of the study, PTH was measured every 3 months as per Kidney Disease Outcomes Quality Initiative (KDOQI) recommendations. In Phase II, PTH was measured monthly. In both phases, dietary education and optimization of medications including phosphate binders, vitamin D analogues and calcimimetics were implemented using standard protocols Data from the two phases was compared with each other and with their respective national norms. RESULTS: The percentage of patients with PTH in target range of 150 - 300 pg/ml increased significantly from Phase I to Phase II of the study (25.4 - 40.3%, p < 0.01). There was a significant reduction in the percentage of patients with PTH levels > 300 pg/ml in Phase II compared with national averages (37% vs. 47%, p < 0.02). There was no significant difference in calcium and phosphorus levels or their product. There was a significant increase in the usage of calcimimetics and vitamin D analogues. CONCLUSION: We observed that increasing the frequency of monitoring PTH from quarterly to monthly was associated with a significant increase in the percentage of patients reaching KDOQI target PTH values.

A national human neuroimaging collaboratory enabled by the Biomedical Informatics Research Network (BIRN).

The aggregation of imaging, clinical, and behavioral data from multiple independent institutions and researchers presents both a great opportunity for biomedical research as well as a formidable challenge. Many research groups have well-established data collection and analysis procedures, as well as data and metadata format requirements that are particular to that group. Moreover, the types of data and metadata collected are quite diverse, including image, physiological, and behavioral data, as well as descriptions of experimental design, and preprocessing and analysis methods. Each of these types of data utilizes a variety of software tools for collection, storage, and processing. Furthermore sites are reluctant to release control over the distribution and access to the data and the tools. To address these needs, the Biomedical Informatics Research Network (BIRN) has developed a federated and distributed infrastructure for the storage, retrieval, analysis, and documentation of biomedical imaging data. The infrastructure consists of distributed data collections hosted on dedicated storage and computational resources located at each participating site, a federated data management system and data integration environment, an Extensible Markup Language (XML) schema for data exchange, and analysis pipelines, designed to leverage both the distributed data management environment and the available grid computing resources.