A computer-based diagnostic and prognostic system for assessing urinary bladder tumour grade and predicting cancer recurrence

dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Ραβαζούλα, Παναγιώτα	el
dc.contributor.author	Νικηφορίδης, Γεώργιος Χ.	el
dc.date.accessioned	2015-05-08T10:14:34Z
dc.date.issued	2015-05-08
dc.identifier.uri	http://hdl.handle.net/11400/9948
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://informahealthcare.com/doi/abs/10.1080/1463923021000043723	en
dc.subject	Prognostic system
dc.subject	Classification
dc.subject	Προγνωστικό σύστημα
dc.subject	Ταξινόμηση
dc.title	A computer-based diagnostic and prognostic system for assessing urinary bladder tumour grade and predicting cancer recurrence	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.identifier.secondary	doi:10.1080/1463923021000043723
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	2002
heal.bibliographicCitation	Spyridonos, P., Cavouras, D., Ravazoula, P. and Nikiforidis, G. (2002). A computer-based diagnostic and prognostic system for assessing urinary bladder tumour grade and predicting cancer recurrence. Informatics for Health and Social Care. 27(2). pp. 111-122. Informa Healthcare: 2002.	en
heal.abstract	Purpose : A computer-based system was designed, incorporating subjective criteria employed by pathologists in their usual microscopic observation of tissue samples and measurements of nuclear characteristics, with the purpose of automatically assessing urinary bladder tumour grade and predicting cancer recurrence. Material and Methods : Ninety-two cases with urine bladder carcinoma were diagnosed and followed-up. Forty-seven patients had cancer recurrence. Each case was represented by eight histological (subjective) features, evaluated by pathologists, and thirty-six automatically extracted nuclear features. Grading and prognosis were performed by neural-network based classifiers employing both histological and nuclear features. Results : Employing a combination of histological and nuclear features, highest classification accuracy was 82%, 80.5%, and 93.1% for tumours of grade I, II and III respectively. The prognostic-system, gave a significant prognostic assessment of 72.8% with a confidence of 74.5% that cancer might recur and of 71.1% that might not, employing two histological features and two textural nuclear features. Conclusions : The system for grading and predicting tumour recurrence may serve as a second opinion tool and features employed for designing the system may be of value to pathologists using descriptive grading systems.	en
heal.publisher	Informa Healthcare	en
heal.journalName	Informatics for Health and Social Care	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false