ArticleBioMed research international2014
An intelligent clinical decision support system for patient-specific predictions to improve cervical intraepithelial neoplasia detection.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Immediate referral to colposcopy versus cytological surveillance for minor cervical cytological abnormalities in the absence of HPV test.The Cochrane database of systematic reviews · 2017Pooled it
- Evaluation of HPV E6/E7 mRNA in predicting recurrence after Leep in cervical intraepithelial neoplasia: A study of negative findings.PloS one · 2026Article
- Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology.Journal of clinical medicine · 2024Review
- Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile illness.Frontiers in digital health · 2023Article
- Article
- Novel biomarkers with promising benefits for diagnosis of cervical neoplasia: a systematic review.Infectious agents and cancer · 2020Review
- Management of Atypical Squamous Cell Cases: A Prospective Study of Women seen at a Private Health Service in Northeastern Brazil.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2018Observational
- Article
- Development of a clinical decision support system using genetic algorithms and Bayesian classification for improving the personalised management of women attending a colposcopy room.Healthcare technology letters · 2016Article
- Computerized Clinical Decision Support: Contributions from 2014.Yearbook of medical informatics · 2015Review
- The Application of Classification and Regression Trees for the Triage of Women for Referral to Colposcopy and the Estimation of Risk for Cervical Intraepithelial Neoplasia: A Study Based on 1625 Cases with Incomplete Data from Molecular Tests.BioMed research international · 2015Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.
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