Elsevier

European Journal of Cancer

Volume 160, January 2022, Pages 180-188
European Journal of Cancer

Original Research
Monitoring patients at risk for melanoma: May convolutional neural networks replace the strategy of sequential digital dermoscopy?

https://doi.org/10.1016/j.ejca.2021.10.030Get rights and content

Highlights

  • Differentiating early melanomas from atypical nevi is challenging.

  • Sequential digital dermoscopy helps to uncover the dynamic changes in early melanomas.

  • CNN are equally accurate at diagnosing skin cancer than trained dermatologists.

  • The CNN of our study may not replace the strategy of sequential digital dermoscopy.

  • Development of CNN, including information on lesion evolution, seems indispensable.

Abstract

Background

Sequential digital dermoscopy (SDD) is applied for early melanoma detection by uncovering dynamic changes of monitored lesions. Convolutional neural networks (CNN) are capable of high diagnostic accuracies similar to trained dermatologists.

Objectives

To investigate the capability of CNN to correctly classify melanomas originally diagnosed by mere dynamic changes during SDD.

Methods

A retrospective cross-sectional study using image quartets of 59 high-risk patients each containing one melanoma diagnosed by dynamic changes during SDD and three nevi (236 lesions). Two validated CNN classified quartets at baseline or after SDD follow-up at the time of melanoma diagnosis. Moreover, baseline quartets were rated by 26 dermatologists. The main outcome was the number of quartets with correct classifications.

Results

CNN-1 correctly classified 9 (15.3%) and CNN-2 8 (13.6%) of 59 baseline quartets. In baseline images, CNN-1 attained a sensitivity of 25.4% (16.1%–37.8%) and specificity of 92.7% (87.8%–95.7%), whereas CNN-2 of 28.8% (18.8%–41.4%) and 75.7% (68.9%–81.4%). Expectedly, after SDD follow-up CNN more readily detected melanomas resulting in improved sensitivities (CNN-1: 44.1% [32.2%–56.7%]; CNN-2: 49.2% [36.8%–61.6%]). Dermatologists were told that each baseline quartet contained one melanoma, and on average, correctly classified 24 (22–27) of 59 quartets. Correspondingly, accepting a baseline quartet to be appropriately classified whenever the highest malignancy score was assigned to the melanoma within, CNN-1 and CNN-2 correctly classified 28 (47.5%) and 22 (37.3%) of 59 quartets, respectively.

Conclusions

The tested CNN could not replace the strategy of SDD. There is a need for CNN capable of integrating information on dynamic changes into analyses.

Introduction

Melanoma still accounts for most skin cancer deaths, and incidence remains on a high level [1]. On the one hand, early diagnosis is of utmost importance because surgical excision is curative in thin melanomas [2]. On the other hand, overdiagnosis of melanoma due to increased diagnostic scrutiny, lowered thresholds for surgical biopsies, and lowered thresholds to diagnose melanoma by dermatopathologists is a serious concern [3]. Therefore, new technological advances should not only solely focus on ‘not missing melanoma’ but also on ‘reducing unnecessary excisions’. Compared with the unaided eye, dermoscopy improves the diagnostic accuracy for melanoma particularly when applied by experienced dermatologists [4]. Dermoscopic patterns of melanoma and various diagnostic algorithms have been defined, yet early or featureless melanomas remain difficult to diagnose [5,6]. Because changes in lesion pattern and size frequently indicate malignancy, it is reasonable to monitor pigmented lesions over time. Sequential digital dermoscopy (SDD) allows documentation of dynamic changes and facilitates melanoma detection, while avoiding unnecessary excisions of nevi [7,8]. Monitoring patients by SDD was shown to be particularly effective in patients at higher risk for melanoma, e.g. in patients affected by atypical mole syndrome or familial atypical multiple mole melanoma syndrome [9,10]. Short-term SDD follow-up is helpful for making a correct management decision in a suspicious lesion lacking clear melanoma features, whereas long-term follow-up focuses on monitoring of high-risk patients and also includes inconspicuous lesions [7,8,11]. We previously found that a significant proportion of melanomas diagnosed in the course of SDD could not be identified by dermatologists in baseline images, which emphasises the diagnostic value added by using SDD in high-risk patients [12].

More recently, there have been numerous reports on the high-level diagnostic accuracy of convolutional neural networks (CNN) for skin cancer detection in clinical and dermoscopic images [[13], [14], [15], [16], [17]]. With the present study, we aimed to investigate whether current CNN may replace the strategy of SDD.

Section snippets

Images for evaluation

Our study was approved by the local ethics committee and performed in accordance with the Declaration of Helsinki principles. Digital dermoscopy images of 59 patients at increased risk for melanoma were selected from a database. All patients were monitored by SDD. For every patient, a baseline image quartet of four dermoscopic images was arranged, including three benign nevi and one lesion identified as melanoma during SDD. Moreover, follow-up quartets were created, including a follow-up image

Characteristics of imaged lesions and dermatologists

For each of the 59 patients monitored by SDD, we created a baseline and follow-up image quartet. Fig. 1 depicts representative quartets. At the time of excision, 28 (47.5%) melanomas were in situ, and 31 (52.5%) melanomas were invasive with a mean thickness of 0.4 mm (range: 0.2–1.3 mm). Sixteen (27.1%) melanomas had developed in association with pre-existing nevi. Of the 59 melanomas, 32 (54.2%) melanomas were located on the trunk, 24 (40.7%) melanomas on the extremities and three melanomas

Discussion

Previous studies have clearly shown that deep learning CNN are capable of classifying skin lesions on or above the level of trained dermatologists [[13], [14], [15]]. However, exploring the limitations and benefits of CNN when applied in different clinical scenarios is still an ongoing process [[21], [22], [23]]. With the study, we investigated whether monitoring melanocytic lesions by SDD might be replaced by CNN assessment of baseline lesions.

SDD was shown to be effective for early melanoma

Ethics

Reviewed and approved by the ethic committee of the medical faculty of the University of Heidelberg (Approval number S-629/2017).

Funding

No funding. This research received no specific grant from any public, commercial or not-for-profit sector.

Authors’ contributions

Study concepts: Haenssle, Kittler, Tschandl, Study design: Haenssle, Kittler, Tschandl, Data acquisition: Haenssle, Kittler, Tschandl, Quality control of data and algorithms: Haenssle, Kittler, Tschandl, Winkler, Data analysis and interpretation: Haenssle, Kittler, Tschandl, Winkler, Statistical analysis: Haenssle, Tschandl, Winkler, Manuscript preparation: Haenssle, Tschandl, Winkler, Manuscripts editing: Haenssle, Tschandl, Winkler, Manuscript review: Enk, Fink, Haenssle, Kittler, Sies,

Access to data and data analysis

HA Haenssle and JK Winkler had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Fotofinder Systems was not involved in the design of the study or interpretation of the acquired data.

Study registration

This study is registered at the German Clinical Trial Register (DRKS): Study-ID DRKS00013570.

Financial Disclosure

None.

Conflict of interest statement

P Tschandl reports grants from MetaOptima and Lilly, consulting fees from Silverchair, and speaker honoraria from Lilly and FotoFinder, outside the submitted work. A Enk reports a DFG grant, honoraria from Biotest, MSD, Janssen-Cilag and BMS, participation in an MSD Data Safety Monitoring Board and leadership in the EDF and IDSI outside the submitted work. H Kittler states honoraria from Fotofinder and Pelpharma, as well as receipt of equipment from Fotofinder, Heine, 3Gen, Derma Medical

Acknowledgement

The authors would like to acknowledge Mohamed Souhayel Abassi and Tobias Fuchs (Department of Research and Development, FotoFinder Systems GmbH, Bad Birnbach, Germany) for their technical support regarding CNN assessment.

References (29)

  • H.G. Welch et al.

    The rapid rise in cutaneous melanoma diagnoses

    N Engl J Med

    (2021)
  • M. Vestergaard et al.

    Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting

    Br J Dermatol

    (2008)
  • H. Skvara et al.

    Limitations of dermoscopy in the recognition of melanoma

    Arch Dermatol

    (2005)
  • H. Kittler et al.

    Identification of clinically featureless incipient melanoma using sequential dermoscopy imaging

    Arch Dermatol

    (2006)
  • Cited by (6)

    View full text