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Proceedings Paper

The development and testing of a unique and flexible training module for residents and fellows using digital breast tomosythesis (DBT)
Author(s): Christiane M. Hakim; John Drescher; Jill L. King; Durwin Logue; Amy H. Klym; David Gur
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Paper Abstract

The transition from FFDM to digital breast tomosynthesis (DBT) necessitates new approaches for training radiology residents and fellows that highlight depiction differences between the same abnormalities on the two modalities. We developed a unique, flexible training module that enables training with complete feedback, as well as testing performance before and after use of this training module. Currently, 219 examinations, with priors and other relevant information, are included. Using a special interface to the Secure View workstation (Hologic), we developed a management program that displays each case in a randomized manner and in a sequential mode (i.e. FFDM first followed by FFDM+DBT) and allows the reader to rate the case followed by viewing the images side by side with results of the full imaging based history (reporting) by the screening interpreter, the diagnostic workup interpreter (when applicable), and the actual pathology (biopsy and/or surgical). This approach allows the reader to review their correct and/or incorrect interpretation at each step of the management decision making. The module also has sets of pre- and post-training cases, allowing for a test-train-test study to be performed, if so desired. Two observer studies using 18 radiologists, residents, and fellows have been performed using this module, to date. The training module was assembled, tested, and implemented. We found it to be extremely flexible and useful in training. After completing two observer performance studies, the module was installed in our clinical facility and is currently being used to train residents and fellows at their own pace. All users found this module to be useful and extremely informative.

Paper Details

Date Published: 10 March 2017
PDF: 6 pages
Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101361L (10 March 2017); doi: 10.1117/12.2248911
Show Author Affiliations
Christiane M. Hakim, Magee-Womens Hospital of UPMC (United States)
John Drescher, Univ. of Pittsburgh (United States)
Jill L. King, Univ. of Pittsburgh (United States)
Durwin Logue, Univ. of Pittsburgh (United States)
Amy H. Klym, Univ. of Pittsburgh (United States)
David Gur, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 10136:
Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
Matthew A. Kupinski; Robert M. Nishikawa, Editor(s)

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