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

Overview

Journal Dermatol Pract Concept

Specialty Dermatology

Date 2023 May 17

PMID 37196312

Authors

Niels Kvorning Ternov

Martin Tolsgaard

Lars Konge

Anders Nymark Christensen

Sigrid Isabella Pilgaard Kristensen

Lisbet Rosenkrantz Holmich

Jonathan Stretch

Richard Anthony Scolyer

Tine Vestergaard

Pascale Guitera

Annette Hougaard Chakera

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

The effects of procedural and conceptual knowledge on visual learning.

Beeler N, Ziegler E, Volz A, Navarini A, Kapur M Adv Health Sci Educ Theory Pract. 2023; 29(4):1243-1263.

PMID: 38060072 PMC: 11368992. DOI: 10.1007/s10459-023-10304-0.

Improving Skin Cancer Diagnostics Through a Mobile App With a Large Interactive Image Repository: Randomized Controlled Trial.

Nervil G, Ternov N, Vestergaard T, Solvsten H, Chakera A, Tolsgaard M JMIR Dermatol. 2023; 6:e48357.

PMID: 37624707 PMC: 10448292. DOI: 10.2196/48357.

References

Ericsson K . Acquisition and maintenance of medical expertise: a perspective from the expert-performance approach with deliberate practice. Acad Med. 2015; 90(11):1471-86. DOI: 10.1097/ACM.0000000000000939. View

Harkemanne E, Baeck M, Tromme I . Training general practitioners in melanoma diagnosis: a scoping review of the literature. BMJ Open. 2021; 11(3):e043926. PMC: 7993310. DOI: 10.1136/bmjopen-2020-043926. View

Cook D, Artino Jr A . Motivation to learn: an overview of contemporary theories. Med Educ. 2016; 50(10):997-1014. PMC: 5113774. DOI: 10.1111/medu.13074. View

Ternov N, Vestergaard T, Holmich L, Karmisholt K, Wagenblast A, Klyver H . Reliable test of clinicians' mastery in skin cancer diagnostics. Arch Dermatol Res. 2020; 313(4):235-243. DOI: 10.1007/s00403-020-02097-8. View

Tschandl P, Rinner C, Apalla Z, Argenziano G, Codella N, Halpern A . Human-computer collaboration for skin cancer recognition. Nat Med. 2020; 26(8):1229-1234. DOI: 10.1038/s41591-020-0942-0. View

Speelman C, Martin K, Flower S, Simpson T . Skill acquisition in skin cancer detection. Percept Mot Skills. 2010; 110(1):277-97. DOI: 10.2466/PMS.110.1.277-297. View

Monteiro S, Norman G . Diagnostic reasoning: where we've been, where we're going. Teach Learn Med. 2013; 25 Suppl 1:S26-32. DOI: 10.1080/10401334.2013.842911. View

Tschandl P, Codella N, Akay B, Argenziano G, Braun R, Cabo H . Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 2019; 20(7):938-947. PMC: 8237239. DOI: 10.1016/S1470-2045(19)30333-X. View

Whiteman D, Green A, Olsen C . The Growing Burden of Invasive Melanoma: Projections of Incidence Rates and Numbers of New Cases in Six Susceptible Populations through 2031. J Invest Dermatol. 2016; 136(6):1161-1171. DOI: 10.1016/j.jid.2016.01.035. View

10.

Petrie T, Samatham R, Witkowski A, Esteva A, Leachman S . Melanoma Early Detection: Big Data, Bigger Picture. J Invest Dermatol. 2018; 139(1):25-30. PMC: 6685706. DOI: 10.1016/j.jid.2018.06.187. View

11.

Baghdady M, Pharoah M, Regehr G, Lam E, Woods N . The role of basic sciences in diagnostic oral radiology. J Dent Educ. 2009; 73(10):1187-93. View

12.

Lomas A, Leonardi-Bee J, Bath-Hextall F . A systematic review of worldwide incidence of nonmelanoma skin cancer. Br J Dermatol. 2012; 166(5):1069-80. DOI: 10.1111/j.1365-2133.2012.10830.x. View

13.

Dinnes J, Deeks J, Chuchu N, Ferrante di Ruffano L, Matin R, Thomson D . Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults. Cochrane Database Syst Rev. 2018; 12:CD011902. PMC: 6517096. DOI: 10.1002/14651858.CD011902.pub2. View

14.

Andreasen L, Tabor A, Norgaard L, Ringsted C, Sandager P, Rosthoj S . Multicenter randomized trial exploring effects of simulation-based ultrasound training on obstetricians' diagnostic accuracy: value for experienced operators. Ultrasound Obstet Gynecol. 2019; 55(4):523-529. DOI: 10.1002/uog.20362. View

15.

Fox M, Ericsson K, Best R . Do procedures for verbal reporting of thinking have to be reactive? A meta-analysis and recommendations for best reporting methods. Psychol Bull. 2010; 137(2):316-44. DOI: 10.1037/a0021663. View

16.

Harvey V, Patel H, Sandhu S, Wallington S, Hinds G . Social determinants of racial and ethnic disparities in cutaneous melanoma outcomes. Cancer Control. 2014; 21(4):343-9. PMC: 4505912. DOI: 10.1177/107327481402100411. View

17.

Yoon J, Boutis K, Pecaric M, Fefferman N, Ericsson K, Pusic M . A think-aloud study to inform the design of radiograph interpretation practice. Adv Health Sci Educ Theory Pract. 2020; 25(4):877-903. PMC: 7471179. DOI: 10.1007/s10459-020-09963-0. View

18.

Baghdady M, Carnahan H, Lam E, Woods N . Integration of basic sciences and clinical sciences in oral radiology education for dental students. J Dent Educ. 2013; 77(6):757-63. View

19.

Lacy F, Coman G, Holliday A, Kolodney M . Assessment of Smartphone Application for Teaching Intuitive Visual Diagnosis of Melanoma. JAMA Dermatol. 2018; 154(6):730-731. PMC: 6145647. DOI: 10.1001/jamadermatol.2018.1525. View

20.

Friedman R, Rigel D, Kopf A . Early detection of malignant melanoma: the role of physician examination and self-examination of the skin. CA Cancer J Clin. 1985; 35(3):130-51. DOI: 10.3322/canjclin.35.3.130. View