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Breast Cancer Detection

The Development and Pilot Study of a “Tactile Landscape” as a Standardized Testing Tool

Veitch, Daisy E., PhD Candidate; Bochner, Melissa, MB, BS, MS, FRACS; Molenbroek, Johan F. M., MSc, PhD; Goossens, Richard H. M., MSc, PhD; Owen, Harry, MD, FRCA, FANZCA, MB ChB

doi: 10.1097/SIH.0000000000000365
Technical Reports: PDF Only
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PAP

Introduction There is still a need for competent breast lump detection palpation skills, especially in developing countries. Our goal is to design, develop, and establish a test to determine whether students can, by touch alone, identify and discriminate between a range of different simulated lesions at different adiposity levels.

Methods Common lesions, breast cancers, and cysts were physically simulated and hidden in a test object referred to as the “tactile landscape” (TL). Ribs, intercostal muscle, and nodularity—normal anatomical features—increased their realistic complexity. Varying depths of features simulated varying degrees of adiposity. A testing protocol was created to determine the testee's ability to identify and discriminate different commonly occurring breast masses using palpation. Five experts (four breast surgeons and one general practitioner) and 20 inexperienced medical students were recruited and tested. Results were compared.

Results The TL has been based on previously verified breast models and has softness similar to 53% of women's breasts and nodularity similar to 60% as assessed in a breast clinic by breast surgeons. The five experts indicated that the simulated lesions felt like those they might encounter in clinical practice and all of them identified the lesions and nonlesions hidden in the TL 100% correctly, thus indicating the value of the model. In contrast, only one student was able to identify all the lesions. One student identified none of them. The remaining students mean score was 65%.

Conclusions All students but one performed poorly in comparison to the experts. This indicates that the test could be useful to test students' ability to identify and discriminate breast masses. If successful, it will add previously missing capability to the mix of assessment instruments already used, thus potentially improving clinical breast examination training and assessment.

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

From the Faculty of Industrial Design Engineering (D.E.V.), Delft University of Technology (TU Delft), Delft, the Netherlands; Breast Endocrine and Surgical Oncology Unit (M.B.), Royal Adelaide Hospital, Adelaide, SA, Australia; Faculty of Industrial Design Engineering (J.F.M.M., R.H.M.G.), Delft University of Technology (TU Delft), Delft, the Netherlands; and College of Medicine and Public Health (H.O.), Flinders University, Adelaide, SA, Australia.

Reprints: Daisy Ellen Veitch, PhD Candidate, 102 Gloucester Avenue, Belair, 5052, South Australia, Australia (e-mail: daisy@sharpdummies.com.au;daisy.veitch@gmail.com).

The authors declare no conflict of interest.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.simulationinhealthcare.com).

© 2019 Society for Simulation in Healthcare