Breast Cancer Detection

Abstract

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.