Automated melanoma detection. An algorithm inspired
from human intelligence characterizing disordered
pattern of melanocytic lesions improving a convolutional
neural network.

Jilliana Monnier, Arthur Cartel Gouabou Foahom, Meryem Serdi, Jules

Collenne, Rabah Iguernaissi, Marie-Aleth Richard, Caroline

Gaudy-Marqueste, Jean-Luc Damoiseaux, Jean-Jacques Grob, Djamal Merad

► To cite this version:

Jilliana Monnier, Arthur Cartel Gouabou Foahom, Meryem Serdi, Jules Collenne, Rabah Iguernaissi, et al.. Automated melanoma detection. An algorithm inspired from human intelligence characterizing disordered pattern of melanocytic lesions improving a convolutional neural network.. Journal of The American Academy of Dermatology, 2024, 91 (2), pp.350-352. 10.1016/j.jaad.2024.02.063. hal-04569966

HAL Id: hal-04569966

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
size for presurgical planning. For subungual glomus tumor with characteristic clinical findings and symptoms, particularly intermittent pain, imaging may not be necessary, and excision with histopathology confirms the diagnosis and relieves symptoms. Future studies are needed to analyze cost and utility of Doppler ultrasound in detecting subungual glomus tumors.⁵

Debra K. Lee, MD,ª Rachel C. Hill, BS,ᵇ Amar D. Desai, MPH,ᶜ and Shari R. Lipner, MD, PhDᵈ

From the University of Texas Medical Branch, Galveston, Texasª; Weill Cornell Medical College, New York, New Yorkᵇ; Rutgers New Jersey Medical School, Newark, New Jerseyᶜ; and Department of Dermatology, Weill Cornell Medicine New York, New York, New Yorkᵈ

Authors Lee and Hill contributed equally to the article.

Funding sources: None.

Patient consent: Not applicable.

IRB approval status: No. 19-07020452 was reviewed and granted exemption from the institutional review board at Weill Cornell Medicine.

Financial disclosures: Dr Lipner has served as a consultant for Ortho-Dermatologics, Eli Lilly, Moberg Pharmaceuticals, and BelleTorus Corporation.

Author's note: Our study includes one patient that was previously reported in the following publication: Lipner SR, Ko D, Husain S. Subungual Leiyomyoma Presenting as Erythronychia: Case Report and Review of the Literature. J Drugs Dermatol. 2019;18(5):465-467.

Key words: glomus tumor; magnetic resonance imaging; MRI; nail biopsy; subungual glomus tumor; X-ray.

Correspondence to: Shari R. Lipner, MD, PhD, Department of Dermatology, Weill Cornell Medicine, 1305 York Ave, New York, NY 10021

E-mail: shl9032@med.cornell.edu

Conflicts of interest

Dr Lee, Ms Hill, Mr Desai, and Dr Lipner have no conflicts of interest to disclose.

REFERENCES

1. Holzberg M. Nail imaging. In: Scher and Daniels Nails. 4th ed. Springer International Publishing AG; 2018:432-433.

2. Al-Qattan MM, Al-Namla A, Al-Thunayan A, Al-Subhi F, El-Shayeb AF. Magnetic resonance imaging in the diagnosis of glomus tumours of the hand. J Hand Surg Br. 2005;30(5): 535-540. https://doi.org/10.1016/j.jhsb.2005.06.009

3. Van Geertruyden J, Lorea P, Goldschmidt D, et al. Glomus tumours of the hand. A retrospective study of 51 cases. *J Hand Surg Br.* 1996;21(2):257-260. https://doi.org/10.1016/s0266-76 81(96)80110-0

4. Montandon C, Costa JC, Dias LA, et al. Subungual glomus tumors: imaging findings. *Radiol Bras.* 2009;42:371-374.

5. de Almeida CÁ, Nakamura R, Leverone A, Marchiori E, Canella C. High-frequency ultrasonography for subungual glomus tumor evaluation - imaging findings. *Skeletal Radiol.* 2024;53:891-898. https://doi.org/10.1007/s00256-023-04506-1

https://doi.org/10.1016/j.jaad.2024.03.044

Automated melanoma detection. An algorithm inspired from human intelligence characterizing disordered pattern of melanocytic lesions improving a convolutional neural network

<figure><img src="image_2.png" /></figure>

To the Editor: Computer-aided diagnosis (CAD) based on convolutional neural network (CNN) has showed high performance and may assist dermatologists for early melanoma detection,¹ but some limitations remain, lack of transparency and challenging interpretation. A handcrafted system based on relevant dermatologic concepts may be more interpretable and acceptable by dermatologists. Most melanomas share disorder and anar-chic color distribution.² In contrast, most nevi share an organized color distribution. To our knowledge, such an approach based on overall ordered or disordered aspect of melanocytic lesions (MLs) has never been integrated into a handcrafted CAD system.

Our objective was to compare a CAD model attempting to integrate cognitive concepts from human intelligence based on features characterizing color disorder of ML with entropy feature, characterizing disorder in image processing named the “disorder model” with a CNN model and to test if the fusion of the two different models could increase CNN performances.

We developed two binary models to differentiate melanoma from nevi on dermoscopic images on the same data set including 6296 nevi and 1361 melanomas from the International Skin Imaging Collaboration public data set (ISIC 2019)³,⁴,⁵: a hand-crafted algorithm characterizing order or disorder color in ML (the disorder model), based on 48 features characterizing disorder (entropy, skewness, SD, and
<figure><img src="image_3.png" /><figcaption>**Fig 1.** Melanoma and nevi images in RGB and HSV color spaces and histograms representing entropy. **A**, Melanoma is represented in the RGB (inset) and HSV color spaces. The histogram of the melanoma image encoded in HSV reveals a random distribution of pixels on gray levels and high entropy. **B**, Nevus represented in RGB (inset) and HSV color spaces. The histogram of the nevi image encoded in HSV reveals a narrow peak in the distribution of pixels on gray levels and a low entropy. *HSV*, hue, saturation, value; *RBG*, red, green, blue.</figcaption></figure>

kurtosis from 4 color spaces) associated with a
classifier (K-nearest neighbors), and a CNN based on
a pretrained architecture ResNet-50 (Supplementary
Fig 1, available via Mendeley at https://data.
mendeley.com/datasets/mbg44h56zx/2).

The disorder model reached high performances,
with area under the curve (AUC) of 0.91, sensi-
tivity of 91%, specificity of 74%, and balanced
accuracy of 83%. These performances seem to be
superior to those of other published handcrafted
models (Supplementary Figs 2 and 3 and
Supplementary Table I, available via Mendeley at
https://data.mendeley.com/datasets/mbg44h56zx/2).
Entropy was the most discriminating feature for
melanoma detection (Fig 1, A, B).

The CNN model displayed equivalent perfor-
mances, with AUC of 0.89, sensitivity of 86%,
specificity of 75%, and balanced accuracy of 81%,
and, equivalent performances to those described in a
Cochrane review (Supplementary Figs 4 and 5 and
Supplementary Tables I and II, available via
Mendeley at https://data.mendeley.com/datasets/
mbg44h56zx/2). The fusion of the two algorithms
yielded higher performances, with AUC of 0.921,
sensitivity of 86%, specificity of 82%, and balanced
accuracy of 84% (Supplementary Fig 6, available via

Mendeley at https://data.mendeley.com/datasets/
mbg44h56zx/2), with good performances with
conclusions also on amelanotic melanomas
(Supplementary Fig 7, available via Mendeley at
https://data.mendeley.com/datasets/mbg44h56zx/2).
Those performances were better than that previously
reported for other fusion models (AUC, 0.90), but not
using the disorder approach (Supplementary Table
I). However, these two models seem to make
different errors; classification errors made by disor-
der model, are intuitively understandable by derma-
tologists (Supplementary Figs 8 and 9, available via
Mendeley at https://data.mendeley.com/datasets/
mbg44h56zx/2). Whereas CNN classification errors
may be challenging to interpretate and may be less
acceptable in the clinical practice in case of contra-
intuitive classification without a pathologic control to
ascertain results (Supplementary Fig 10, available via
Mendeley at https://data.mendeley.com/datasets/
mbg44h56zx/2).

We removed dermoscopic images with major
artifacts from the data set (Supplementary Fig 11,
available via Mendeley at https://data.mendeley.
com/datasets/mbg44h56zx/2), which is a limitation
of our study but still necessary to not bias our
performances.

To conclude, a CAD system based on features
inspired from human intelligence, ie, a process likely
to be used by the human cognitive recognition of
MLs characterizing order or disorder color, can
unexpectedly have the same level performances as
that of as CNN. The fusion of the two models
increased performances, notably the specificity,
and reduced obvious errors made by each, revealing
the complementarity of those two approaches and
may improved dermatologists confidence on its
conclusion.

We thank M. Dieudonne Anderson Loundou for his
help on the statistical analysis.

Jilliana Monnier, MD, PhD,ª,ᵇ,ᶜ Arthur Cartel
Foabom Gouabou, PhD,ª Meryem Serdi, MS,ª
Jules Collenne, MS,ª Rabah Iguernaissi, PhD,ª
Marie-Aleth Richard, MD,ᶜ Caroline Gaudy-
Marqueste, MD, PhD,ᵇ,ᶜ Jean-Luc Damoiseaux,
PhD,ª Jean-Jacques Grob, MD, PhD,ᵇ,ᶜ and
Djamal Merad, PhDª

From the Aix-Marseille Univ, Université de Mar-
seille, CNRS UMR 7020, Laboratoire d'Informa-
tique et Systèmes, Marseille, Franceª; Centre de
Recherche en Cancérologie de Marseille, Inserm
UMR1068, CNRS UMR7258, Aix-Marseille
University, Marseille, France<sup>b</sup>; and Dermatology and Skin cancer department, La Timone Hospi-tal, Assistance Publique-Hôpitaux de Marseille, Aix-Marseille University, Marseille, France.<sup>c</sup>

Prs Grob and Merad contributed equally to this
article as senior authors.

Funding source: This work was supported by ADEREM, DIAMELEX ANR, Canceropôle PACA

Patient consent: Not applicable.

IRB approval status: Not applicable.

Data availability statement: The data set used is from a public data set ISIC (International Skin Imaging Collaboration) 2019, available at https://challenge.isic-archive.com/data. We adopted this public data set to develop our models with training and test steps, providing presented results. We can make public synthetic images from datageneration.

Key words: computer assisted diagnosis; convolu-
tional neural network; disorder; entropy; global
cognitive analysis; handcrafted method; mela-
noma detection.

Correspondence to: Jilliana Monnier, MD, PhD, Dermatology and skin cancer department, La Timone hospital, AP-HM, 264 rue Saint Pierre, 13005 Marseille, France

E-mail: jilliana.monnier@ap-hm.fr

X handle: Automated detection of melanoma's disorder

Conflicts of interest

No conflicts of interest related to this study.

REFERENCES

1. Tschandl P, Rinner C, Apalla Z, et al. Human–computer collaboration for skin cancer recognition. *Nat Med*. 2020; 26(8):1229-1234. https://doi.org/10.1038/s41591-020-0942-0

2. Gachon J, Beaulieu P, Sei JF, et al. First prospective study of the recognition process of melanoma in dermatological practice. *Arch Dermatol*. 2005;141(4):434-438. https://doi.org/10.1001/archde rm.141.4.434

3. Codella NCF, Gutman D, Celebi ME, et al. Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). *Disponible Sur*. 2018:168-172. https://doi.org/10.1109/ISBI.2018.8363547

4. Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. *Sci Data*. 2018;5:180161. https://doi.org/10.1038/sdata.2018.161

5. Combalia M, Codella NCF, Rotemberg V, et al. BCN20000: Dermoscopic lesions in the wild. *arXiv*. 2019:1908.02288.

https://doi.org/10.1016/j.jaad.2024.02.063

HIV-related exclusion criteria in
atopic dermatitis and prurigo
nodularis clinical trials

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To the Editor: Atopic dermatitis (AD) and prurigo nodularis (PN) are common among persons with HIV (PWH).¹ With the advent of new therapies for AD and PN, the morbidity of these skin conditions has improved over time. However, scant data exist to guide treatment decisions using novel systemic therapies for AD and PN among PWH, underscored by a scoping review noting limited evidence to guide systemic treatment decisions for older patients and those with comorbidities including HIV.²

We examined HIV-related exclusion criteria in clinical trials for AD and PN. [ClinicalTrials.gov](http://ClinicalTrials.gov) was queried for “atopic dermatitis” and “prurigo nodularis” trials starting between January 1, 2010 and January 1, 2023. In the query, we included all age groups and sexes as well as trials that enrolled healthy volunteers. Among resulted trials, exclusion criteria related to immune status and/or HIV were summarized descriptively.

A total of 432 clinical trials were identified, including 405 AD and 27 PN trials. The most common intervention was biologic injections (n = 125, 28.9%). PWH could have been excluded based on at least 1 of the following exclusion criteria in 235 (58.0%) AD and 18 (66.7%) PN trials: any immunodeficiency, chronic infection, history of immunosuppression, any systemic disease, or “HIV infection, regardless of viral suppression or CD4 count” (the latter was explicitly listed as an exclusion criterion in 132 AD and 5 PN trials) (Table 1).

We found that the majority of AD and PN clinical
trials listed eligibility criteria that excluded PWH,
irrespective of viral suppression or immune status.
Overall, there is a lack of high-quality data on
systemic and biologic therapies for AD and PN in
PWH. A systematic review of 112 case reports and
retrospective reviews on the use of biologic
therapies (ie, TNF inhibitors, interleukin 12 or 23
inhibitors, CD20 inhibitors, and dupilumab)
among PWH for a variety of inflammatory diseases
such as atopic dermatitis and psoriasis showed an
overall favorable safety profile.³ Moreover, use of
oral ruxolitinib to treat systemic inflammation was
well tolerated and reduced markers of systemic
inflammation in a trial of 60 PWH receiving anti-
retroviral therapy.⁴ In cancer clinical trials, the