Original Article

Effect of Histopathological Explanations for Dermoscopic Criteria on Learning Curves in Skin Cancer Training: A Randomized Controlled Trial

Author Affiliation(s)


Introduction: Case-based training improves novices pattern recognition and diagnostic accuracy in skin cancer diagnostics. However, it is unclear how pattern recognition is best taught in conjunction with the knowledge needed to justify a diagnosis.

Objectives: The aim of this study was to examine whether an explanation of the underlying histopathological reason for dermoscopic criteria improves skill acquisition and retention during case-based training in skin cancer diagnostics.

Methods: In this double-blinded randomized controlled trial, medical students underwent eight days of case-based training in skin cancer diagnostics, which included access to written diagnosis modules. The modules dermoscopic subsections differed between the study groups. All participants received a general description of the criteria, but the intervention group additionally received a histopathological explanation.

Results: Most participants (78%) passed a reliable test in skin cancer diagnostics, following a mean training time of 217 minutes. Access to histopathological explanations did not affect participants' learning curves or skill retention.

Conclusions: The histopathological explanation did not affect the students, but the overall educational approach was efficient and scalable.

Keywords : education, melanoma, pigmented lesions, dermatopathology, carcinoma


1. Lomas, A., Leonardi-Bee, J. & Bath-Hextall, F. A systematic review of worldwide incidence of nonmelanoma skin cancer. British Journal of Dermatology 166, 1069–1080 (2012). 2. Whiteman, D. C., Green, A. C. & Olsen, C. M. The Growing Burden of Invasive Melanoma: Projections of Incidence Rates and Numbers of New Cases in Six Susceptible Populations through 2031. Journal of Investigative Dermatology 136, 1161–1171 (2016). 3. Petrie, T., Samatham, R., Witkowski, A. M., Esteva, A. & Leachman, S. A. Melanoma Early Detection: Big Data, Bigger Picture. J Invest Dermatol 139, 25–30 (2019). 4. Ternov, N. K. et al. Reliable test of clinicians’ mastery in skin cancer diagnostics. Arch Dermatol Res (2020) doi:10.1007/s00403-020-02097-8. 5. Tschandl, P. et al. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. The Lancet Oncology 20, 938–947 (2019). 6. Friedman, R. J., Rigel, D. S. & Kopf, A. W. Early detection of malignant melanoma: the role of physician examination and self-examination of the skin. CA Cancer J Clin 35, 130–151 (1985). 7. Harkemanne, E., Baeck, M. & Tromme, I. Training general practitioners in melanoma diagnosis: a scoping review of the literature. BMJ Open 11, e043926 (2021). 8. Dinnes, J. et al. Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults. Cochrane Database Syst Rev 12, CD011902 (2018). 9. Speelman, C., Martin, K., Flower, S. & Simpson, T. Skill Acquisition in Skin Cancer Detection. Percept Mot Skills 110, 277–297 (2010). 10. Lacy, F. A., Coman, G. C., Holliday, A. C. & Kolodney, M. S. Assessment of Smartphone Application for Teaching Intuitive Visual Diagnosis of Melanoma. JAMA Dermatol 154, 730–731 (2018). 11. Monteiro, S. M. & Norman, G. Diagnostic Reasoning: Where We’ve Been, Where We’re Going. Teaching and Learning in Medicine 25, S26–S32 (2013). 12. Norman, G. R., Rosenthal, D., Brooks, L. R., Allen, S. W. & Muzzin, L. J. The development of expertise in dermatology. Arch Dermatol 125, 1063–1068 (1989). 13. Norman, G. R., Coblentz, C. L., Brooks, L. R. & Babcook, C. J. Expertise in visual diagnosis: a review of the literature. Academic Medicine 67, (1992). 14. Yoon, J.-S. et al. A think-aloud study to inform the design of radiograph interpretation practice. Adv in Health Sci Educ (2020) doi:10.1007/s10459-020-09963-0. 15. Baghdady, M. T., Pharoah, M. J., Regehr, G., Lam, E. W. N. & Woods, N. N. The Role of Basic Sciences in Diagnostic Oral Radiology. Journal of Dental Education 73, 1187–1193 (2009). 16. Baghdady, M. T., Carnahan, H., Lam, E. W. N. & Woods, N. N. Integration of Basic Sciences and Clinical Sciences in Oral Radiology Education for Dental Students. Journal of Dental Education 77, 757–763 (2013). 17. Harvey, V. M., Patel, H., Sandhu, S., Wallington, S. F. & Hinds, G. Social Determinants of Racial and Ethnic Disparities in Cutaneous Melanoma Outcomes. Cancer Control 21, 343–349 (2014). 18. Ibfelt, E. H. et al. Influence of socioeconomic factors and region of residence on cancer stage of malignant melanoma: a Danish nationwide population-based study. CLEP 10, 799–807 (2018). 19. Tschandl, P. et al. Human–computer collaboration for skin cancer recognition. Nat Med 26, 1229–1234 (2020). 20. Ericsson, K. A. Acquisition and Maintenance of Medical Expertise: A Perspective From the Expert-Performance Approach With Deliberate Practice. Academic Medicine 90, 1471–1486 (2015). 21. Loerch, A. G. Learning Curves. in Encyclopedia of Operations Research and Management Science (eds. Gass, S. I. & Fu, M. C.) 871–874 (Springer US, 2013). doi:10.1007/978-1-4419-1153-7_526. 22. Schwartz, D. L., Bransford, J. D. & Sears, D. Efficiency and Innovation in Transfer. In J. Mestre (Ed.) 1–51. 23. Davis, J. et al. Intelligent User Interface Design for Teachable Agent Systems. (2003). 24. Cook, D. A. & Artino Jr, A. R. Motivation to learn: an overview of contemporary theories. Medical Education 50, 997–1014 (2016). 25. Andreasen, L. A. et al. Multicenter randomized trial exploring effects of simulation-based ultrasound training on obstetricians’ diagnostic accuracy: value for experienced operators. Ultrasound in Obstetrics & Gynecology 55, 523–529 (2020). 26. Flavell, J. H. Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist 34, 906–911 (1979). 27. Ericsson, K. A. & Simon, H. A. Protocol Analysis: Verbal Reports as Data. (1993). doi:10.7551/mitpress/5657.001.0001. 28. Fox, M. C., Ericsson, K. A. & Best, R. Do procedures for verbal reporting of thinking have to be reactive? A meta-analysis and recommendations for best reporting methods. Psychol Bull 137, 316–344 (2011).

Send mail to Author

Send Cancel