10.5826/dpc.1304a247
text/xml
2160-9381
3748
en
H. Peter  Soyer
https://creativecommons.org/licenses/by-nc/4.0
Dermatology Practical & Conceptual
dermoscopy
Overdiagnosis in Melanoma Screening: Is it a Real Problem?
Text.Serial.Journal
https://dpcj.org/index.php/dpc
Kramer vs Kramer
2023-10-31
2023-08-07
2023-10-31
2024-01-16
e2023247
Dermatol Pract Concept
H. Peter  Soyer
2023/10/31
10.5826/dpc.1304a247
e2023247
Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Australia
2160-9381
dermoscopy
en
e2023247
Grant SR, Andrew TW, Alvarez EV, Huss WJ, Paragh G. Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma. Cancers (Basel). 2022;14(24).
Overdiagnosis in Melanoma Screening: Is it a Real Problem?
https://dpcj.org/index.php/dpc/article/download/3748/2330
                 Overdiagnosis in Melanoma Screening:
                         Is It a Real Problem?
                                           Brigid Betz-Stablein1, H Peter Soyer1


1 Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Australia



Key words: underdiagnosis, melanoma incidence, biopsy rates, total body photography, dermoscopy
Citation: Betz-Stablein B, Soyer HP. Overdiagnosis in Melanoma Screening: Is it a Real Problem? Dermatol Pract Concept.
2023;13(4):e2023247. DOI: https://doi.org/10.5826/dpc.1304a247
Accepted: August 7, 2023; Published: October 2023
Copyright: ©2023 Betz-Stablein et al. This is an open-access article distributed under the terms of the Creative Commons
Attribution-NonCommercial License (BY-NC-4.0), https://creativecommons.org/licenses/by-nc/4.0/, which permits unrestricted
noncommercial use, distribution, and reproduction in any medium, provided the original authors and source are credited.
Funding: None.
Competing Interests: HPS is a shareholder of MoleMap NZ Limited and e-derm consult GmbH and undertakes regular teledermatological
reporting for both companies. HPS is a Medical Consultant for Canfield Scientific Inc, Blaze Bioscience Inc, and a Medical Advisor for First
Derm.
Authorship: All authors have contributed significantly to this publication.
Corresponding Author: H Peter Soyer, Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Australia.
E-mail: p.soyer@uq.edu.au




The Phenomenon of Overdiagnosis                                          Overdiagnosis Versus True Increase
Is Not Specific to Melanoma                                              The most credible evidence for the concept of overdiagnosis
Overdiagnosis is a relevant health challenge worldwide, not              is the significant rise in melanoma incidence with no corre-
just in cancer but across several medical conditions. Overdi-            sponding increase in mortality. This may also be explained
agnosis is the diagnosis of a condition/disease that will never          by a true increase due to improvements in detection given
cause symptoms or death during a person’s expected lifetime              the advances in diagnostic imaging, and the lower threshold
[1]. In the case of cancer, it either represents a tumor that            of histopathologic diagnosis known as ‘diagnostic drift’ [2].
will not progress (or potentially regress), or a slowly pro-             Harms et al. discusses other factors including the introduc-
gressing tumor where a patient will die of other causes be-              tion of screening and patient awareness campaigns, increased
fore becoming symptomatic [1]. Melanoma o
                                        ­ verdiagnosis is                ­biopsy rates, and improved medical record keeping [3]. The
an increasing topical issue today particularly as the number             increasing incidence, therefore, must be analyzed in the con-
of in situ melanomas is skyrocketing. In this opinion piece,             text of changing public awareness, dermatologic care and sun
we focus on the phenomenon which has been referred to as                 exposure [4]. Thus, Kurtansky et al. analyzed these trends ad-
the ‘epidemic of overdiagnosis’ in melanoma.                             justing for age and sex, and accounting for period and ­cohort
                                                                         effects, and identified evidence of both a true increase –
                                                                         most apparent in older males – and overdiagnosis – most
                                                                         apparent in middle aged and younger females [4].




Opinion | Dermatol Pract Concept. 2023;13(4):e2023247                                                                                     1
Figure 1. Self-perpetuating cycle of melanoma over-diagnosis [5].




    Drivers of melanoma overdiagnosis have formed a some-           3D Total Body Photography,
what self-perpetual cycle where heighted awareness leads to
                                                                    Sequential Dermoscopy Imaging
more screening, leading to more biopsies and thus more mel-
anoma diagnoses, leading again to higher awareness (Figure
                                                                    and Artificial Intelligence (AI)
1) [5]. In hope of identifying areas to address overdiagnosis,      Improvements can be introduced to screening practices that
Pathirana et al. mapped potential drivers to five interrelated      will allow for those at high risk to still benefit from early
domains, namely, i) culture – eg beliefs that more is better, ii)   detection, while minimizing potential overdiagnosis. Com-
financial incentives at the health system level, iii) technolog-    bining total body photography and sequential dermoscopy
ical change, iv) professional angst of missing disease and v)       imaging has been shown to improve diagnostic accuracy in
public expectations in clinicians to do something [6].              high risk individuals, as has the use of AI, particularly in less
                                                                    experienced clinicians [10,11]. 3D total body photography

Is the Melanoma Epidemic Mostly                                     now allows unprecedented monitoring of the total skin sur-
                                                                    face and the longitudinal collection of such images. This has
an In Situ Melanoma Epidemic?
                                                                    the potential to reduce excisions and correspondingly reduce
While both invasive and in situ melanoma rates are rising,          the potential for overdiagnosis. For example, if a clinician
the increase of in situ melanoma is alarming [4,5]. The rise of     or patient is concerned about a lesion, the clinician can now
in situ melanoma has been well documented, however there            show it has not changed over time. This allows the clini-
is sparse evidence on its natural history. What evidence exists     cian to adopt ‘watchful waiting’, a strategy we are currently
suggests a low probability of progression to invasive mela-         evaluating in a high-risk melanoma cohort in Brisbane,
noma [2]. Further, Olsen et al. postulate that if in situ mel-      ­Australia [12]. While AI on digital images has the potential
anoma is indeed an obligate precursor lesion, excising them         to reduce excisions and thus overdiagnosis, this has yet to
should correspond to fewer invasive melanomas, and thus             be been shown in the real world clinical setting. Algorithms
then the mean age of those diagnosed with in situ melanoma          must be developed with a clear clinical application in mind,
should be younger than those diagnosed with invasive mela-          on diverse datasets and reported using standardized crite-
noma [7]. However, they found similar or higher mean ages           ria, ensuring potential bias due to training datasets clearly
of those with in situ melanoma. Coupled with the increasing         described [13]. Also of concern is the lack of gold standard
ratio of in situ to invasive melanomas this finding is sugges-      in the algorithm training sets, due to the aforementioned dis-
tive of the detection of more indolent lesions. Additionally,       cordance in pathologist diagnosis [9]. AI applied to whole
screening has been shown to be associated with a higher risk        slide pathology images could therefore be a promising ap-
of in situ, but not invasive melanoma [8]. Also adding to           proach, however to date models have been limited by small,
this epidemic is the diagnostic shift from dysplastic nevus to      largely homogenous datasets, with few models externally
in situ melanoma [2], which is further complicated by low           validated or compared head-to-head with pathologists [14].
concordance between histopathologists [9].                          We hypothesize combining clinical information, total body
                                                                    photography, sequential dermoscopy images and whole slide
                                                                    pathology images associated by AI will provide a holistic,
                                                                    more robust diagnostic approach.




2                                                                              Opinion | Dermatol Pract Concept. 2023;13(4):e2023247
Conclusions                                                          6. Pathirana T, Clark J, Moynihan R. Mapping the drivers of
                                                                        overdiagnosis to potential solutions. BMJ. 2017;358:j3879.
In sum, while the term overdiagnosis lacks precision it is a            DOI: 10.1136/bmj.j3879. PMID: 28814436.
                                                                     7. Olsen CM, Pandeya N, Rosenberg PS, Whiteman DC. Inci-
very relevant and complex problem that needs to be tackled.
                                                                        dence of in Situ vs Invasive Melanoma: Testing the “Obligate
A large driver of overdiagnosis is the increasing incidence             Precursor” Hypothesis. J Natl Cancer Inst. 2022;114(10):1364-
of in situ melanomas and in this context we refer to the re-            1370. DOI: 10.1093/jnci/djac138. PMID: 36042554. PMCID:
cent research letter by Semsarian et al. asking the question:           PMC9552301.
“Do we need to rethink the diagnoses melanoma in situ and            8. Whiteman DC, Olsen CM, MacGregor S, et al. The effect of
severely dysplastic naevus?” [2]. A robust discussion as ini-           screening on melanoma incidence and biopsy rates. Br J Der-
                                                                        matol. 2022;187(4):515-522. DOI: 10.1111/bjd.21649. PMID:
tiated by this Kramer vs Kramer series in the clinical, pa-
                                                                        35531668. PMCID: PMC9796145.
thology and AI community is recommended and should be                9. Piepkorn MW, Eguchi MM, Barnhill RL, et al. Reproducibility
followed by an international summit on the topic. We fore-              of the histopathologic diagnosis of melanoma and related me-
see that combining 3D Total Body Photography, sequential                lanocytic lesions: Results from a testing study and a reference
dermoscopy images and whole slide pathology images sup-                 guide for providers. JAAD Int. 2022;9:7-10. DOI: 10.1016/j.
ported by AI will lead to a diagnostic solution in the best             jdin.2022.06.017. PMID: 35996751. PMCID: PMC9391572.
                                                                    10. Moloney FJ, Guitera P, Coates E, et al. Detection of primary mel-
interest of our patients.
                                                                        anoma in individuals at extreme high risk: a prospective 5-year
                                                                        follow-up study. JAMA Dermatol. 2014;150(8):819-827. DOI:

References                                                              10.1001/jamadermatol.2014.514. PMID: 24964862.
                                                                    11. Tschandl P, Rinner C, Apalla Z, et al. Human-computer col-
1. Welch HG, Black WC. Overdiagnosis in cancer. J Natl Cancer           laboration for skin cancer recognition. Nat Med. 2020;26(8):
   Inst. 2010;102(9):605-613. DOI: 10.1093/jnci/djq099. PMID:           1229-1234. DOI: 10.1038/s41591-020-0942-0. PMID: 32572267.
   20413742.                                                        12. Primiero CA, McInerney-Leo AM, Betz-Stablein B, et al. Evaluation
2. Semsarian CR, Ma T, Nickel B, et al. Do we need to rethink           of the efficacy of 3D total-body photography with sequential digi-
   the diagnoses melanoma in situ and severely dysplastic naevus?       tal dermoscopy in a high-risk melanoma cohort: protocol for a ran-
   Br J Dermatol. 2022;186(6):1030-1032. DOI: 10.1111/bjd               domised controlled trial. BMJ Open. 2019;9(11):e032969. DOI:
   .21010. PMID: 35007335. PMCID: PMC9546461.                           10.1136/bmjopen-2019-032969. PMID: 31712348. PMCID:
3. Harms PW, Chan MP, Bresler SC, et al. Comment on “An                 PMC6858160.
   Epidemiologic Analysis of Melanoma Overdiagnosis in the          13. Daneshjou R, Barata C, Betz-Stablein B, et al. Checklist for
   United States, 1975-2017”. J Invest Dermatol. 2022;142(11):          Evaluation of Image-Based Artificial Intelligence Reports in
   3120-3122. DOI: 10.1016/j.jid.2022.03.028. PMID: 36273837.           Dermatology: CLEAR Derm Consensus Guidelines From the
4. Kurtansky NR, Dusza SW, Halpern AC, et al. An Epidemio-              International Skin Imaging Collaboration Artificial Intelligence
   logic Analysis of Melanoma Overdiagnosis in the United States,       Working Group. JAMA Dermatol. 2022;158(1):90-96. DOI:
   1975-2017. J Invest Dermatol. 2022;142(7):1804-1811.e6.              10.1001/jamadermatol.2021.4915. PMID: 34851366. PMCID:
   DOI: 10.1016/j.jid.2021.12.003. PMID: 34902365. PMCID:               PMC9845064.
   PMC9187775.                                                      14. Grant SR, Andrew TW, Alvarez EV, Huss WJ, Paragh G. Diag-
5. Welch HG, Mazer BL, Adamson AS. The Rapid Rise in Cutane-            nostic and Prognostic Deep Learning Applications for Histo-
   ous Melanoma Diagnoses. N Engl J Med. 2021;384(1):72-79.             logical Assessment of Cutaneous Melanoma. Cancers (Basel).
   DOI: 10.1056/NEJMsb2019760. PMID: 33406334.                          2022;14(24):6231. DOI: 10.3390/cancers14246231. PMID:
                                                                        36551716. PMCID: PMC9776963.




Opinion | Dermatol Pract Concept. 2023;13(4):e2023247                                                                                   3
