Assembly and evaluation of a training module and dataset with feedback for improved interpretation of digital breast tomosynthesis examinations

David Gur; Margarita L. Zuley; Jules H. Sumkin; Christiane M. Hakim; Denise M. Chough; Linda Lovy; Cynthia Sobran; Durwin Logue; Bin Zheng; Amy H. Klym

doi:10.1117/12.910596

22 February 2012 Assembly and evaluation of a training module and dataset with feedback for improved interpretation of digital breast tomosynthesis examinations

David Gur, Margarita L. Zuley, Jules H. Sumkin, Christiane M. Hakim, Denise M. Chough, Linda Lovy, Cynthia Sobran, Durwin Logue, Bin Zheng, Amy H. Klym

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Proceedings Volume 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment; 83181D (2012) https://doi.org/10.1117/12.910596
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

The FDA recently approved Digital Breast Tomosynthesis (DBT) for use in screening for the early detection of breast cancer. However, MQSA qualification for interpreting DBT through training was noted as important. Performance issues related to training are largely unknown. Therefore, we assembled a unique computerized training module to assess radiologists' performances before and after using the training module. Seventy-one actual baseline mammograms (no priors) with FFDM and DBT images were assembled to be read before and after training with the developed module. Fifty examinations of FFDM and DBT images enriched with positive findings were assembled for the training module. Depicted findings were carefully reviewed, summarized, and entered into a specially designed training database where findings were identified by case number and synchronized to the display of the related FFDM plus DBT examinations on a clinical workstation. Readers reported any findings using screening BIRADS (0, 1, or 2) followed by instantaneous feedback of the verified truth. Six radiologists participated in the study and reader average sensitivity and specificity were compared before and after training. Average sensitivity improved and specificity remained relatively the same after training. Performance changes may be affected by disease prevalence in the training set.

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David Gur, Margarita L. Zuley, Jules H. Sumkin, Christiane M. Hakim, Denise M. Chough, Linda Lovy, Cynthia Sobran, Durwin Logue, Bin Zheng, and Amy H. Klym "Assembly and evaluation of a training module and dataset with feedback for improved interpretation of digital breast tomosynthesis examinations", Proc. SPIE 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment, 83181D (22 February 2012); https://doi.org/10.1117/12.910596

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KEYWORDS

Digital breast tomosynthesis

Cancer

Mammography

Breast

Breast cancer

Architectural distortion

Databases

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