DE RMATO-VENE R EOLOGY
variations (odds ratios [OR] 0.4-0.8) and seven risk alleles (OR 1.1-1.3) in their patient cohort. Individuals with risk alleles were less successful at recognising the HPV virus and therefore more likely to present with genital warts – conversely, participants with protective alleles had better immune responses and were more effective at recognising HPV, limiting the likelihood of presenting with condylomas.
“Condylomas is one of the most prevalent of all sexually transmitted diseases, but its association with the HLA system is poorly understood,” explains Dr Pernille Lindsø Andersen, PhD fellow, Department of Clinical Immunology and Department of Dermatology at Zealand University Hospital in Denmark. “Our research identifies key immunologic features that prove there is a link between the immune system and condylomas.” A cohort of 65,791 blood donors were examined, with 4,199 participants considered as condyloma acuminata cases and the remaining 61,592 participants used as a control group. Cases were defined as those registered with a minimum of one redeemed prescription of medication for condyloma acuminata or had a diagnosis of condyloma acuminata. Genetic information (HLA types) and its association with being a case or control was assessed in all participants.
Additional research is needed to determine whether protective alleles (HLA types) can recognise specific proteins made by HPV. “The promising results presented in this study are an exciting breakthrough which could lead to potential avenues for future mRNA vaccinations against genital warts,” commented Mariano Suppa, EADV board member and associate professor at the Université Libre de Bruxelles in Brussels, Belgium.
Skin cancer detection
A new study presented at the Congress also raised concerns around the use of consumer apps for diagnosis of skin cancers. Researchers found that a direct-to-consumer machine learning model for detecting skin cancers incorrectly classified rare and
aggressive cancers as low-risk. The breakthrough findings5
79.4% [95% confidence interval (CI) 69.3- 89.4%] and specificity was 37.7% [95% CI 24.7-50.8]. For Model 2, MCC was not included in the top five diagnosis for any of the 28 MCC images analysed, raising the possibility that the model had not been trained that this disease class exists. The high false positive rate of Model 1 has potentially negative consequences on a personal and societal level. The results pose a bigger question of the safety of other artificial intelligence (AI) models for detecting skin cancer available on the market. Lloyd Steele, lead author of the study at the Blizard Institute, Queen Mary University of London, UK explained: “In order to improve, machine learning model evaluations should consider the spectrum of diseases that will be seen in practice. At the moment, most of the performance of those models is driven by the imaging data available, which is particularly scarce when it comes to rare skin cancers.”
suggest
that making apps based on such models available directly to the public without transparency on performance metrics for rare but potentially life-threatening skin cancers is ethically questionable. Researchers in London focused on two types of skin cancer, Merkel cell carcinoma (MCC) and amelanotic melanoma, both of which are rare but particularly aggressive cancers that tend to grow fast and require early treatment. They created a dataset of 116 images of these rare cancers and of the benign lesions seborrahoeic keratosis and haemangiomas, and assessed these images with two machine-learning models. The first model studied was a certified medical device, directly sold to the public via the App store and advertised as being able to diagnose 95% of skin cancers (Model 1). The second model was available for research purposes only and used as a reference (Model 2).
The results showed that Model 1 incorrectly classified 17.9% of MCCs and 22.9% of amelanotic melanomas as low-risk. In turn, 62.2% of benign lesions were classified as high risk. For detecting malignancy, Model 1’s sensitivity was
The fact that one in two people across Europe live with skin disease on a daily basis makes the skin the most affected organ in the body and, as an organisation, we are therefore committed to making skin disease a public
health priority. Marie-Aleth Richard, EADV board member.
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A global collaboration between research groups and hospitals can be a step towards tackling the gap of skin cancer imaging data, which is a crucial element for a high- performance rate of machine learning. Marie-Aleth Richard, EADV board
member and professor at the University Hospital of La Timone, Marseille, said: “The number of skin cancer detection apps available for consumer use is growing but, as demonstrated in this research, there must be more transparency around the safety and efficacy of these apps. Furthermore, such devices detect only what they are shown to analyse and do not make systematic analysis of all the skin’s surface. Failure to be transparent could put lives at risk.”
At-home skin cancer treatment The findings of a pilot study evaluating the use of a new prototype photodynamic therapy (PDT) device were also presented at the Congress. The new device can be used at home and significantly reduces pain levels during treatment of basal cell carcinoma (BCC), while achieving efficacy comparable with a hospital stay.10 The efficacy of PDT, a treatment that involves light-sensitive medicine and a light source to destroy abnormal cancer cells, for low-risk BCC has been proven through multiple studies. However, a need to reduce the pain experienced during treatment and the long hospital stay prompted the development of a new device, even before the COVID-19 pandemic. Standard PDT treatment consists of two sessions performed within a hospital environment that usually requires a 1.5-2 hour wait with a one-week interval.
Ana Gabriela Salvio, lead author of the
study, commented: “The importance of a portable PDT device is crucial in its country
JANUARY 2022
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