<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec id="j_raon-2022-0023_s_005">
<h3>Background</h3>
<p>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.</p></sec>
<sec id="j_raon-2022-0023_s_006">
<h3>Patients and methods</h3>
<p>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.</p></sec>
<sec id="j_raon-2022-0023_s_007">
<h3>Results</h3>
<p>Bas eline 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%.</p></sec>
<sec id="j_raon-2022-0023_s_008">
<h3>Conclusions</h3>
<p>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.</p></sec>
</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
Bas eline 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.

Identification of women with high grade histopathology results after conisation by artificial neural networks

Radiology and Oncology

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

Background
The aim of the study was to evaluate if artificial neural networks can predict high-grade histopathology results after conisation from risk factors...

Identification of women with high grade histopathology results after conisation by artificial neural networks

Article Category: research article

Published Online: Aug 14, 2022

Page range: 355 - 364

Received: Jan 16, 2022

Accepted: Apr 25, 2022

DOI: https://doi.org/10.2478/raon-2022-0023

© 2022 Marko Mlinaric, Miljenko Krizmaric, Iztok Takac, Alenka Repse Fokter, published by Sciendo

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