Computer aided insights on obscure cases of breast cancer diagnosis

Andreadis, I; Nikita, K; Giannakopoulou, G; Koulocheri, D; Zografos, G; Antaraki, A; Ligomenides, P; Spyrou, G

dc.contributor.author	Andreadis, I	en
dc.contributor.author	Nikita, K	en
dc.contributor.author	Giannakopoulou, G	en
dc.contributor.author	Koulocheri, D	en
dc.contributor.author	Zografos, G	en
dc.contributor.author	Antaraki, A	en
dc.contributor.author	Ligomenides, P	en
dc.contributor.author	Spyrou, G	en
dc.date.accessioned	2014-03-01T02:45:12Z
dc.date.available	2014-03-01T02:45:12Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32196
dc.subject	BI-RADS	en
dc.subject	Breast cancer	en
dc.subject	CAD	en
dc.subject	Microcalcifications	en
dc.subject.other	Classifiers	en
dc.subject.other	Computer aided analysis	en
dc.subject.other	Computer aided diagnosis	en
dc.subject.other	Diagnosis	en
dc.subject.other	Diagnostic radiography	en
dc.subject.other	Digital image storage	en
dc.subject.other	Imaging systems	en
dc.subject.other	Learning systems	en
dc.subject.other	Mammography	en
dc.subject.other	Medical imaging	en
dc.subject.other	Optoelectronic devices	en
dc.subject.other	Technical presentations	en
dc.subject.other	Analysis and evaluations	en
dc.subject.other	BI-RADS	en
dc.subject.other	Breast cancer	en
dc.subject.other	Breast cancer diagnoses	en
dc.subject.other	CAD	en
dc.subject.other	Cad systems	en
dc.subject.other	Classification results	en
dc.subject.other	Clustered microcalcifications	en
dc.subject.other	Evaluation results	en
dc.subject.other	Hippocrates	en
dc.subject.other	Hybrid classifiers	en
dc.subject.other	Microcalcifications	en
dc.subject.other	Calcification (biochemistry)	en
dc.title	Computer aided insights on obscure cases of breast cancer diagnosis	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IST.2008.4659976	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IST.2008.4659976	en
heal.identifier.secondary	4659976	en
heal.publicationDate	2008	en
heal.abstract	Breast cancer is a leading cause of deaths in women. Mammography is considered as the most effective technology presently available for breast cancer screening, being very effective in the detection of clustered microcalcifications which are considered as one of the most important findings associated to the existence of breast cancer. A computer aided diagnosis (CAD) system named ""Hippocrates-mst"" has been already developed in the lab based on detailed analysis and evaluation of related features of microcalcifications (individually and in clusters). Preliminary evaluation results have shown that the system achieves high levels of sensitivity, while suffering from low specificity. For this reason, our current studies aim to a methodology refinement which will lead to optimized classification results. In this paper, we focus on obscure diagnostic cases classified by the radiologists as BI-RADS 3. In such cases, although short-term re-examination is normally advised, radiologists and physicians usually have strong doubts about their recommendations. We tested the performance of two classifiers embedded in the proposed CAD system using a dataset of 63 (57 benign and 6 malignant) mammograms, all classified as BI-RADS 3 and biopsy proven. The sensitivity achieved by the first one (the default Hippocrates-mst classifier) is as high as 100%, classifying correctly all the malignant cases. As far as the benign cases are concerned, system's specificity is 35.09%. Using the second classifier (a rule based and SVM hybrid classifier) the specificity increases to 63.16% with a cost of sensitivity decrease to 66.67%. ©2008 IEEE.	en
heal.journalName	IST 2008 - IEEE Workshop on Imaging Systems and Techniques Proceedings	en
dc.identifier.doi	10.1109/IST.2008.4659976	en
dc.identifier.spage	237	en
dc.identifier.epage	241	en