| Author/Editor | Mlinarič, Marko; Križmarić, Miljenko; Takač, Iztok; Repše-Fokter, Alenka | |
| Title | Identification of women with high grade histopathology results after conisation by artificial neural networks | |
| Type | članek | |
| Vol. and No. | Letnik 56, št. 3 | |
| Publication year | 2022 | |
| Volume | str. 355-364 | |
| ISSN | 1581-3207 - Radiology and oncology | |
| Language | eng | |
| 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. | |
| Keywords | artificial neural networks conisation uterine cervical cancer uterine cervical dysplasia |