A proposal for a national mammography database: content, purpose, and value.

Osuch, Janet; Anthony, Mwakikunga; Bassett, Lawrence W.; Debor, Marydale; D’Orsi, Carl J.; Hendrick, R. Edward; Linver, Michael N.; Smith, R A

doi:10.2214/ajr.164.6.7754870

Cited by 22 publications

(17 citation statements)

References 16 publications

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“…The radiologist can also add details to the report by typing in free text, but these details are not captured in the database. We consolidated our database in the NMD format (32,38). Although the NMD format contains more than 100 variables, we included only those that were routinely collected in the practice and that were predictive of breast cancer (Table 1).…”

Section: Methodsmentioning

confidence: 99%

Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

Burnside

Davis

Chhatwal³

et al. 2009

Radiology

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Purpose:To determine whether a Bayesian network trained on a large database of patient demographic risk factors and radiologist-observed findings from consecutive clinical mammography examinations can exceed radiologist performance in the classification of mammographic findings as benign or malignant. Materials and Methods:The institutional review board exempted this HIPAA-compliant retrospective study from requiring informed consent. Structured reports from 48 744 consecutive pooled screening and diagnostic mammography examinations in 18 269 patients from April 5, 1999 to February 9, 2004 were collected. Mammographic findings were matched with a state cancer registry, which served as the reference standard. By using 10-fold cross validation, the Bayesian network was tested and trained to estimate breast cancer risk by using demographic risk factors (age, family and personal history of breast cancer, and use of hormone replacement therapy) and mammographic findings recorded in the Breast Imaging Reporting and Data System lexicon. The performance of radiologists compared with the Bayesian network was evaluated by using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results:The Bayesian network significantly exceeded the performance of interpreting radiologists in terms of AUC (0.960 vs 0.939, P ϭ .002), sensitivity (90.0% vs 85.3%, P Ͻ .001), and specificity (93.0% vs 88.1%, P Ͻ .001). Conclusion:On the basis of prospectively collected variables, the evaluated Bayesian network can predict the probability of breast cancer and exceed interpreting radiologist performance. Bayesian networks may help radiologists improve mammographic interpretation.

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Section: Methodsmentioning

confidence: 99%

Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

Burnside

Davis

Chhatwal³

et al. 2009

Radiology

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“…When these databases were created, storing this much data was a herculean task [44]. Until recently, most mammography databases did not communicate or share data across organizations.…”

Section: Data To Big Data and Data Miningmentioning

confidence: 99%

Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining

Margolies

Pandey

Horowitz³

et al. 2016

American Journal of Roentgenology

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OBJECTIVE The purpose of this article is to describe structured reporting and the development of large databases for use in data mining in breast imaging. CONCLUSION The results of millions of breast imaging examinations are reported with structured tools based on the BI-RADS lexicon. Much of these data are stored in accessible media. Robust computing power creates great opportunity for data scientists and breast imagers to collaborate to improve breast cancer detection and optimize screening algorithms. Data mining can create knowledge, but the questions asked and their complexity require extremely powerful and agile databases. New data technologies can facilitate outcomes research and precision medicine.

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“…Usually, it is used to develop and test algorithms for computer-aided detection (CAD) systems but it is used also for teaching and training medical students or as an archive of rare cases. In 1995, Osuch et al [1] proposed a mammography database for a national mammography audit and to monitor patients through a centralised system. More recently, the Radiological Society of North America Medical Imaging Resource Center (RSNA MIRC) project [2] proposed a highly generic system to store and publish medical images, primarily for research and teaching support, that can federate a large number of remote databases and make them accessible as a single one.…”

Section: Introductionmentioning

confidence: 99%

“…Nel 1995 Osuch et al [1] proposero un database mammografico per una indagine mammografica a livello nazionale e per monitorare i pazienti attraverso un sistema centralizzato. Più recentemente il progetto Medical Imaging Resource Center (MIRC) dell'RSNA [2] ha proposto un sistema molto generale per immagazzinare e pubblicare immagini mediche, innanzitutto per supporto di ricerca e di studio, che potesse mettere insieme un gran numero di database remoti e renderli accessibili come un tutt'uno.…”

Section: Introduzioneunclassified

MAGIC-5: an Italian mammographic database of digitised images for research

et al. 2008

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The implementation of a database of digitised mammograms is discussed. The digitised images were collected beginning in 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals as a first step in developing and implementing a computer-aided detection (CAD) system. All 3,369 mammograms were collected from 967 patients and classified according to lesion type and morphology, breast tissue and pathology type. A dedicated graphical user interface was developed to visualise and process mammograms to support the medical diagnosis directly on a high-resolution screen. The database has been the starting point for developing other medical imaging applications, such as a breast CAD, currently being upgraded and optimised for use in a distributed environment with grid services, in the framework of the Instituto Nazionale di Fisicia Nucleare (INFN)-funded Medical Applications on a Grid Infrastructure Connection (MAGIC)-5 project.

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A proposal for a national mammography database: content, purpose, and value.

Cited by 22 publications

References 16 publications

Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining

MAGIC-5: an Italian mammographic database of digitised images for research

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