Cascade pattern recognition structure for improving quantitative assessment of estrogen receptor status in breast tissue carcinomas

dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Δασκαλάκης, Αντώνης	el
dc.contributor.author	Καγκάδης, Γεώργιος Χ.	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.date.accessioned	2015-05-12T13:38:49Z
dc.date.issued	2015-05-12
dc.identifier.uri	http://hdl.handle.net/11400/10204
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.ncbi.nlm.nih.gov/pubmed/18773740	en
dc.subject	Histopathology
dc.subject	Image analysis
dc.subject	Ιστοπαθολογία
dc.subject	Ανάλυση εικόνας
dc.title	Cascade pattern recognition structure for improving quantitative assessment of estrogen receptor status in breast tissue carcinomas	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Oncology
heal.classification	Ιατρική
heal.classification	Ογκολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85094724
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ογκολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh98002813
heal.contributorName	Γεωργιάδης, Παντελής	el
heal.contributorName	Ραβαζούλα, Παναγιώτα	el
heal.contributorName	Νικηφορίδης, Γεώργιος Σ.	el
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2008
heal.bibliographicCitation	Kostopoulos, S., Cavouras, D., Daskalakis, A., Kagadis, G., Kalatzis, I., et al. (August 2008). Cascade pattern recognition structure for improving quantitative assessment of ER-status in breast tissue carcinomas. Analytical and Quantitative Cytology and Histology. 30(4). pp. 218-225. Journal of Reproductive Medicine: 2008.	en
heal.abstract	OBJECTIVE: To develop and validate a computer-based approach for the quantitative assessment of estrogen receptor (ER) status in breast tissue specimens for breast cancer management. STUDY DESIGN: Microscopy images of 32 immunohistochemically (IHC) stained specimens of breast cancer biopsies were digitized and were primarily assessed for ER status (percentage of positively stained nuclei) by a histopathologist. A pattern recognition system was designed for automatically assessing the ER status of the IHC-stained specimens. Nuclei were automatically segmented from background by a pixel-based unsupervised clustering algorithm and were characterized as positively stained or unstained by a supervised classification algorithm. This cascade structure boosted the system's classification accuracy. RESULTS: System performance in correctly characterizing the nuclei was 95.48%. When specifying each case's ER status, system performance was statistically not significantly different to the physician's assessment (p = 0.13); when ranking each case to a particular 5-scale ER-scoring system (giving the chance of response to endocrine treatment), the system's score and the physician's score were in agreement in 29 of 32 cases. CONCLUSION: The need for reliable and operator independent ER-status estimation procedures may be served by the design of efficient pattern recognition systems to be employed as support opinion tools in clinical practice.	en
heal.publisher	Journal of Reproductive Medicine	en
heal.journalName	Analytical and Quantitative Cytology and Histology	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true