| Title: | Identification of women with high grade histopathology results after conisation by artificial neural networks |
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| Authors: | ID Mlinarič, Marko (Author)
ID Križmarić, Miljenko (Author)
ID Takač, Iztok (Author)
ID Repše-Fokter, Alenka (Author) |
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| Files: |  URL - Source URL, visit https://sciendo.com/article/10.2478/raon-2022-0023
 PDF - Presentation file, download (663,31 KB)
MD5: 9E50E559F3FCAA0025A371436A677E1F
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  OI - Institute of Oncology
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| Abstract: | Background: The aim of the study was to evaluate if artificial neural networks can predict high-grade histopathology results after conisation from risk factors and their combinations in patients undergoing conisation because of pathological changes on uterine cervix. Patients and methods: We analysed 1475 patients who had conisation surgery at the University Clinic for Gynaecology and Obstetrics of University Clinical Centre Maribor from 1993-2005. The database in different datasets was arranged to deal with unbalance data and enhance classification performance. Weka open-source software was used for analysis with artificial neural networks. Last Papanicolaou smear (PAP) and risk factors for development of cervical dysplasia and carcinoma were used as input and high-grade dysplasia Yes/No as output result. 10-fold cross validation was used for defining training and holdout set for analysis. Results: Baseline classification and multiple runs of artificial neural network on various risk factors settings were performed. We achieved 84.19% correct classifications, area under the curve 0.87, kappa 0.64, F-measure 0.884 and Matthews correlation coefficient (MCC) 0.640 in model, where baseline prediction was 69.79%. Conclusions: With artificial neural networks we were able to identify more patients who developed high-grade squamous intraepithelial lesion on final histopathology result of conisation as with baseline prediction. But, characteristics of 1475 patients who had conisation in years 1993-2005 at the University Clinical Centre Maribor did not allow reliable prediction with artificial neural networks for every-day clinical practice. |
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| Keywords: | artificial neural networks, conisation, uterine cervical cancer, uterine cervical dysplasia, displazija materničnega vratu, rak materničnega vratu, konizacija, umetne nevronske mreže |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 01.01.2022 |
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| Publisher: | Association of Radiology and Oncology |
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| Year of publishing: | 2022 |
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| Number of pages: | str. 355-364 |
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| Numbering: | Vol. 56, iss. 3 |
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| Source: | Ljubljana |
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| PID: | 20.500.12556/DiRROS-19765  |
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| UDC: | 618.146-006-07 |
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| ISSN on article: | 1318-2099 |
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| DOI: | 10.2478/raon-2022-0023 |
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| COBISS.SI-ID: | 115112451 |
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| Copyright: | by Authors |
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| Note: | Soavtorji: Miljenko Krizmaric, Iztok Takac, Alenka Repse Fokter;
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| Publication date in DiRROS: | 24.07.2024 |
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| Views: | 834 |
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| Downloads: | 624 |
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