Predicting the development of vulvar lichen sclerosus
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 th...
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| Format: | Article |
| Language: | English |
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Peoples’ Friendship University of Russia (RUDN University)
2024-03-01
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| Series: | RUDN Journal of Medicine |
| Subjects: | |
| Online Access: | https://journals.rudn.ru/medicine/article/viewFile/38299/23217 |
| _version_ | 1827312274050646016 |
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| author | Ekaterina V. Kolesnikova Alexander V. Zharov Lyudmila K. Osipova Artem I. Dupleev |
| author_facet | Ekaterina V. Kolesnikova Alexander V. Zharov Lyudmila K. Osipova Artem I. Dupleev |
| author_sort | Ekaterina V. Kolesnikova |
| collection | DOAJ |
| description | 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 amodel 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, acomparative statistical correlation analysis of the clinical and anamnestic data of the subjects was carried out using the Spearman correlation coefficient (R0.15), 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 aneural network model for predicting vulvar lichen sclerosus in the second stage of the study. Results and Discussion. Based on established reliably significant (p0.05) obstetric-gynecological, somatic, infectious, hygienic and household factors influencing the risk of developing vulvar lichen sclerosus (Rindicator - from 0.16 to 0.38 confirms the statistical significance of correlations), aneural 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 acomputer 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 (p0.05) significant risk factors for vulvar lichen sclerosus, has high prognostic properties, and acomputer program written on its basis allows the doctor in amatter 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. |
| first_indexed | 2024-04-24T21:40:19Z |
| format | Article |
| id | doaj.art-3f2e54b6227f4db9b2b1228c8b5f143a |
| institution | Directory Open Access Journal |
| issn | 2313-0245 2313-0261 |
| language | English |
| last_indexed | 2024-04-24T21:40:19Z |
| publishDate | 2024-03-01 |
| publisher | Peoples’ Friendship University of Russia (RUDN University) |
| record_format | Article |
| series | RUDN Journal of Medicine |
| spelling | doaj.art-3f2e54b6227f4db9b2b1228c8b5f143a2024-03-21T07:50:13ZengPeoples’ Friendship University of Russia (RUDN University)RUDN Journal of Medicine2313-02452313-02612024-03-01281768510.22363/2313-0245-2024-28-1-76-8521144Predicting the development of vulvar lichen sclerosusEkaterina V. Kolesnikova0https://orcid.org/0000-0002-6537-2572Alexander V. Zharov1https://orcid.org/0000-0002-5460-5959Lyudmila K. Osipova2https://orcid.org/0009-0003-0257-6732Artem I. Dupleev3https://orcid.org/0009-0008-2554-9741Kuban State Medical UniversityKuban State Medical UniversityKuban State Medical UniversityKuban State Medical UniversityRelevance. 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 amodel 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, acomparative statistical correlation analysis of the clinical and anamnestic data of the subjects was carried out using the Spearman correlation coefficient (R0.15), 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 aneural network model for predicting vulvar lichen sclerosus in the second stage of the study. Results and Discussion. Based on established reliably significant (p0.05) obstetric-gynecological, somatic, infectious, hygienic and household factors influencing the risk of developing vulvar lichen sclerosus (Rindicator - from 0.16 to 0.38 confirms the statistical significance of correlations), aneural 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 acomputer 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 (p0.05) significant risk factors for vulvar lichen sclerosus, has high prognostic properties, and acomputer program written on its basis allows the doctor in amatter 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.https://journals.rudn.ru/medicine/article/viewFile/38299/23217vulvar lichen sclerosusscleroatrophic lichenrisk factorscomorbiditypathology of the vulvapredictionneural network model |
| spellingShingle | Ekaterina V. Kolesnikova Alexander V. Zharov Lyudmila K. Osipova Artem I. Dupleev Predicting the development of vulvar lichen sclerosus RUDN Journal of Medicine vulvar lichen sclerosus scleroatrophic lichen risk factors comorbidity pathology of the vulva prediction neural network model |
| title | Predicting the development of vulvar lichen sclerosus |
| title_full | Predicting the development of vulvar lichen sclerosus |
| title_fullStr | Predicting the development of vulvar lichen sclerosus |
| title_full_unstemmed | Predicting the development of vulvar lichen sclerosus |
| title_short | Predicting the development of vulvar lichen sclerosus |
| title_sort | predicting the development of vulvar lichen sclerosus |
| topic | vulvar lichen sclerosus scleroatrophic lichen risk factors comorbidity pathology of the vulva prediction neural network model |
| url | https://journals.rudn.ru/medicine/article/viewFile/38299/23217 |
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