20.500.12556/DiRROS-19765 Identification of women with high grade histopathology results after conisation by artificial neural networks 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. artificial neural networks conisation uterine cervical cancer uterine cervical dysplasia displazija materničnega vratu rak materničnega vratu konizacija umetne nevronske mreže true false true Association of Radiology and Oncology Ljubljana Angleški jezik Ni določen by Authors Neznano 2024-07-24 14:54:29 2024-07-24 14:54:29 2024-07-26 03:54:41 0000-00-00 00:00:00 2022 0 0 Soavtorji: Miljenko Krizmaric, Iztok Takac, Alenka Repse Fokter; str. 355-364 iss. 3 Vol. 56 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2022-01-01 618.146-006-07 1318-2099 10.2478/raon-2022-0023 115112451 https://sciendo.com/article/10.2478/raon-2022-0023 1 1bab07ca-49ab-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=27364 RAZ_Mlinaric_Marko_2022.pdf RAZ_Mlinaric_Marko_2022.pdf 1 9E50E559F3FCAA0025A371436A677E1F cb7a900dd927bd9fb2a55dbdfe8261fd17801e6b0fe0a8b6138a24e866cfc37b 44f12e15-49ab-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=27365 Onkološki inštitut Ljubljana 0 0 0