Predicting the development of vulvar lichen sclerosus

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Abstract

Relevance. The issue of timely diagnosis and treatment of vulvar lichen sclerosus has become especially acute in recent years due to the “rejuvenation” of the disease and the risk of its malignancy. In this regard, it is urgent to search for effective methods for predicting and early detection of the disease. The aim of the study - to develop a model for predicting vulvar lichen sclerosus based on established clinical and anamnestic risk factors. Materials and Methods. The prospective case-control study included 404 women aged 20 to 70 years, of which 344 were patients with vulvar lichen sclerosus and 60 were women without vulvar diseases. At the first stage, a comparative statistical correlation analysis of the clinical and anamnestic data of the subjects was carried out using the Spearman correlation coefficient (R > 0.15), Chi-square tests, Phi and Cramer statistics, the Mann-­Whitney U test and the Student t test (p  <   0.05). The data obtained were used to develop a neural network model for predicting vulvar lichen sclerosus in the second stage of the study. Results and Discussion. Based on established reliably significant (p  <  0.05) obstetric-­gynecological, somatic, infectious, hygienic and household factors influencing the risk of developing vulvar lichen sclerosus (R indicator - from 0.16 to 0.38 confirms the statistical significance of correlations), a neural network model for predicting vulvar lichen sclerosus was developed (the percentage of correct classification on the test sample is the maximum possible value - 100%) and a computer program was written that automates the procedure for predicting the disease. Conclusion. The neural network model for predicting the disease, developed on the basis of reliably (p  <  0.05) significant risk factors for vulvar lichen sclerosus, has high prognostic properties, and a computer program written on its basis allows the doctor in a matter of minutes to identify the patient at risk for the development of vulvar lichen sclerosus and give she needs preventive recommendations aimed at preventing or early detection of the disease.

About the authors

Ekaterina V. Kolesnikova

Kuban State Medical University

Author for correspondence.
Email: jokagyno@rambler.ru
ORCID iD: 0000-0002-6537-2572
SPIN-code: 3707-3561
Krasnodar, Russian Federation

Alexander V. Zharov

Kuban State Medical University; Regional Clinical Hospital No. 2

Email: jokagyno@rambler.ru
ORCID iD: 0000-0002-5460-5959
SPIN-code: 5292-3261
Krasnodar, Russian Federation

Lyudmila K. Osipova

Kuban State Medical University

Email: jokagyno@rambler.ru
ORCID iD: 0009-0003-0257-6732
Krasnodar, Russian Federation

Artem I. Dupleev

Kuban State Medical University

Email: jokagyno@rambler.ru
ORCID iD: 0009-0008-2554-9741
Krasnodar, Russian Federation

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Supplementary files

Supplementary Files
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1. Fig. 1. Correlation analysis of the influence of significant clinical and anamnestic factors on the risk of developing vulvar LS (p < 0.05). Negative indicators significantly increase the risk of vulvar LS. Positive indicators significantly reduce the risk of vulvar LS

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2. Fig. 2. Architecture of the neural network MLP 39–16–2

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3. Fig. 3. An example of predicting vulvar SL in a developed computer program based on a neural network model

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Copyright (c) 2024 Kolesnikova E.V., Zharov A.V., Osipova L.K., Dupleev A.I.

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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