Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass
| dc.contributor.author | Kelly, J. G. | en |
| dc.contributor.author | Angelov, P. P. | en |
| dc.contributor.author | Trevisan, J. | en |
| dc.contributor.author | Vlachopoulou, A. | en |
| dc.contributor.author | Paraskevaidis, E. | en |
| dc.contributor.author | Martin-Hirsch, P. L. | en |
| dc.contributor.author | Martin, F. L. | en |
| dc.date.accessioned | 2015-11-24T18:53:16Z | |
| dc.date.available | 2015-11-24T18:53:16Z | |
| dc.identifier.issn | 1618-2650 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/18528 | |
| dc.rights | Default Licence | - |
| dc.subject | *Algorithms | en |
| dc.subject | Female | en |
| dc.subject | Humans | en |
| dc.subject | *Neoplasm Staging/instrumentation/methods | en |
| dc.subject | Predictive Value of Tests | en |
| dc.subject | Spectroscopy, Fourier Transform Infrared | en |
| dc.subject | Uterine Cervical Neoplasms/*pathology/physiopathology | en |
| dc.title | Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass | en |
| heal.abstract | Although the UK cervical screening programme has reduced mortality associated with invasive disease, advancement from a high-throughput predictive methodology that is cost-effective and robust could greatly support the current system. We combined analysis by attenuated total reflection Fourier-transform infrared spectroscopy of cervical cytology with self-learning classifier eClass. This predictive algorithm can cope with vast amounts of multidimensional data with variable characteristics. Using a characterised dataset [set A: consisting of UK cervical specimens designated as normal (n = 60), low-grade (n = 60) or high-grade (n = 60)] and one further dataset (set B) consisting of n = 30 low-grade samples, we set out to determine whether this approach could be robustly predictive. Variously extending the training set consisting of set A with set B data produced good classification rates with three two-class cascade classifiers. However, a single three-class classifier was equally efficient, producing a user-friendly, applicable methodology with improved interpretability (i.e., better classification with only one set of fuzzy rules). As data from set B were added incrementally to the training set, the model learned and evolved. Additionally, monitoring of results of the set B low-grade specimens (known to be low-grade cervical cytology specimens) provided the opportunity to explore the possibility of distinguishing patients likely to progress towards invasive disease. eClass exhibited a remarkably robust predictive power in a user-friendly fashion (i.e., high throughput, ease of use) compared to other classifiers (k-nearest neighbours, support vector machines, artificial neural networks). Development of eClass to classify such datasets for applications such as screening exhibits robustness in identifying a dichotomous marker of invasive disease progression. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | 10.1007/s00216-010-4179-5 | - |
| heal.identifier.secondary | http://www.ncbi.nlm.nih.gov/pubmed/20857283 | - |
| heal.identifier.secondary | http://www.springerlink.com/content/121451423505hn27/fulltext.pdf | - |
| heal.journalName | Anal Bioanal Chem | en |
| heal.journalType | peer-reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2010 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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