A preliminary study of the potential of tree classifiers in triage of high-grade squamous intraepithelial lesions

Department of Cytopathology, University of Athens Medical School, Attikon University Hospital.
Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology (Impact Factor: 0.49). 06/2011; 33(3):132-40.
Source: PubMed


To investigate the potential value of tree classifiers for the triage of high-grade squamous intraepithelial lesions.
The dataset comprised 808 histologically confirmed cases having a complete range of the cytologic sample assessments--liquid-based cytology, reflex human papillomavirus (HPV) DNA test, E6/E7 HPV mRNA test, and p16 immunocytochemical examinations. Data include 488 histologically negative (cervical intraepithelial neoplasia [CIN] 1 and below) or clinically negative cases and 320 with histologic diagnosis of CIN 2 or worse. Cytologic diagnosis was made according to the criteria of the Bethesda System. Cases were classified in two groups according to histology: those with CIN 2 or worse and those with CIN 1 and below. Fifty percent were randomly selected as a training set and the remaining were as a test set.
Application of tree classifier on the test set gave correct classification of 66.9% for CIN 2 and above cases and 97.3% for CIN 1 and below, producing overall accuracy of 91.5%, outperforming cytologic diagnosis alone.
Application of tree classifiers, based on standard cytologic diagnosis and expression of studied biomarkers, produces improved classification results for cervical precancerous lesions and cancer diagnosis and

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Available from: Christos Meristoudis, Sep 30, 2014
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    • "The majority of published studies, regarding intelligent systems for cervical cancer support, are concerned about computer aided diagnosis systems based on either cytology or colposcopy image analysis [28] [29] [30] [31]. On the other hand, various papers have been published in the past few years concerning bioinformatics' CDSSs based on ANNs for cancer improved detection, treatment, and follow-up support [32] [33] [34] [35] [36] [37]. To the best of our knowledge, however, a similar bioinformatics intelligent CDSS for supporting and improving cervical cancer detection and triage, like the proposed system, has not been reported in the literature. "
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    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.
    04/2014; 2014(1):341483. DOI:10.1155/2014/341483
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    • "New emerging technologies and biomarkers such as HPV DNA genotyping, E6&7 mRNA testing, and P16 immunostaining are continuously being investigated [5] [6] [7] [8] [9] [10] [11] [12]. Various classification techniques such as neural networks [13] [14] [15] [16] [17] [18], discriminant analysis [16] [19] [20], decision trees [21] [22], or genetic algorithms [23] have been used in medicine and, particularly, in diagnostic cytology. The implementation of new diagnostic tools and molecular techniques that are increasingly used in the diagnostic cytology laboratory [24] may improve the accuracy of the final diagnosis in comparison to that of cytology alone. "
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    ABSTRACT: Objective of this study is to investigate the potential of the learning vector quantizer neural network (LVQ-NN) classifier on various diagnostic variables used in the modern cytopathology laboratory and to build an algorithm that may facilitate the classification of individual cases. From all women included in the study, a liquid-based cytology sample was obtained; this was tested via HPV DNA test, E6/E7 HPV mRNA test, and p16 immunostaining. The data were classified by the LVQ-NN into two groups: CIN-2 or worse and CIN-1 or less. Half of the cases were used to train the LVQ-NN; the remaining cases (test set) were used for validation. Out of the 1258 cases, cytology identified correctly 72.90% of the CIN-2 or worst cases and 97.37% of the CIN-1 or less cases, with overall accuracy 94.36%. The application of the LVQ-NN on the test set allowed correct classification for 84.62% of the cases with CIN-2 or worse and 97.64% of the cases with CIN-1 or less, with overall accuracy of 96.03%. The use of the LVQ-NN with cytology and the proposed biomarkers improves significantly the correct classification of cervical precancerous lesions and/or cancer and may facilitate diagnosis and patient management.
    BioMed Research International 10/2012; 2012:303192. DOI:10.1155/2012/303192 · 2.71 Impact Factor
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    ABSTRACT: 'The objective of this study is to investigate the potential of classification and regression trees (CARTs) in discriminating benign from malignant endometrial nuclei and lesions. The study was performed on 222 histologically confirmed liquid based cytological smears, specifically: 117 benign cases, 62 malignant cases and 43 hyperplasias with or without atypia. About 100 nuclei were measured from each case using an image analysis system; in total, we collected 22783 nuclei. The nuclei from 50% of the cases (the training set) were used to construct a CART model that was used for knowledge extraction. The nuclei from the remaining 50% of cases (test set) were used to evaluate the stability and performance of the CART on unknown data. Based on the results of the CART for nuclei classification, we propose two classification methods to discriminate benign from malignant cases. The CART model had an overall accuracy for the classification of endometrial nuclei equal to 85%, specificity 90.68%, and sensitivity 72.05%. Both methods for case classification had similar performance: overall accuracy in the range 94-95%, specificity 95%, and sensitivity 91-94%. The results of the proposed system outperform the standard cytological diagnosis of endometrial lesions. This study highlights interesting diagnostic features of endometrial nuclear morphology and provides a new classification approach for endometrial nuclei and cases. The proposed method can be a useful tool for the everyday practice of the cytological laboratory. Diagn. Cytopathol. 2013;. © 2013 Wiley Periodicals, Inc.
    Diagnostic Cytopathology 07/2014; 42(7). DOI:10.1002/dc.23077 · 1.12 Impact Factor
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