Personalised management of women with cervical abnormalities using a clinical decision support scoring system

•The Clinical Decision Support Scoring System can estimate with high accuracy the histological status of women attending for cytology-based screening.•Artificial neural networks improve the prediction of CIN2 or worse when compared with cytology and/or HPV DNA test.•The Clinical Decision Support Scoring System can optimise the personalised management of women with abnormalities at cervical screening.

ObjectivesTo develop a clinical decision support scoring system (DSSS) based on artificial neural networks (ANN) for personalised management of women with cervical abnormalities.MethodsWe recruited women with cervical abnormalities and healthy controls that attended for opportunistic screening between 2006 and 2014 in 3 University Hospitals. We prospectively collected detailed patient characteristics, the colposcopic impression and performed a series of biomarkers using a liquid-based cytology sample. These included HPV DNA typing, E6&E7 mRNA by NASBA or flow cytometry and p16INK4a immunostaining.We used ANNs to combine the cytology and biomarker results and develop a clinical DSSS with the aim to improve the diagnostic accuracy of tests and quantify the individual's risk for different histological diagnoses. We used histology as the gold standard.ResultsWe analysed data from 2267 women that had complete or partial dataset of clinical and molecular data during their initial or followup visits (N = 3565). Accuracy parameters (sensitivity, specificity, positive and negative predictive values) were assessed for the cytological result and/or HPV status and for the DSSS. The ANN predicted with higher accuracy the chances of high-grade (CIN2 +), low grade (HPV/CIN1) and normal histology than cytology with or without HPV test. The sensitivity for prediction of CIN2 or worse was 93.0%, specificity 99.2% with high positive (93.3%) and negative (99.2%) predictive values.ConclusionsThe DSSS based on an ANN of multilayer perceptron (MLP) type, can predict with the highest accuracy the histological diagnosis in women with abnormalities at cytology when compared with the use of tests alone. A user-friendly software based on this technology could be used to guide clinician decision making towards a more personalised care.