dc.contributor.advisor |
Nolte, Loren W |
|
dc.contributor.author |
Oraby, Sarah |
|
dc.date.accessioned |
2011-01-05T15:23:12Z |
|
dc.date.available |
2012-09-01T04:30:08Z |
|
dc.date.issued |
2010 |
|
dc.identifier.uri |
https://hdl.handle.net/10161/3061 |
|
dc.description.abstract |
<p>Approximately 20-70% of women with breast cancer who choose to undergo breast-conserving
surgery (BCS) need to return to the operating table for re-excision [1]. Now devices
utilizing optical spectroscopy are emerging as a new platform for intra-operative
tumor margin assessment. This study aims to evaluate an optimal decision fusion approach
for malignancy detection of measured spectroscopic data from a first-generation optical
visible spectral imaging platform that can image the molecular composition of breast
tumor margins by implementing a Monte Carlo method with measured diffuse reflectance
[1, 2]. The device measures the diffuse reflectance across 450-600nm. After implementing
the Monte Carlo algorithm the absorption and scattering spectra is derived and is
used to provide insight on different optical properties present in the tissue mass
[2]. Although the extracted optical properties may provide insight on the biological
composition of a specimen, it may not be ideal for malignancy detection. Demographic
factors may also affect a women's normal tissue breast composition, which makes malignancy
detection more complicated. This optimal decision fusion approach implements the basic
decision fusion methodology on acquired spectroscopic data to evaluate the effect
on malignancy detection for different extracted optical parameters. The results of
this automated and systematic approach indicate that a performance of 90% sensitivity
and 68% specificity can be achieved with this approach for the diffuse reflectance
spectrum, which outperforms the extracted optical properties. However, when only
considering post-menopausal patients, the absorption spectrum can yield a sensitivity
of 90% and specificity of 82% and has the best performance of all other features for
this demographic group.</p>
|
|
dc.subject |
Engineering, Electronics and Electrical |
|
dc.subject |
Engineering, Biomedical |
|
dc.subject |
Breast-Cancer |
|
dc.subject |
Decision Fusion |
|
dc.subject |
Detection |
|
dc.subject |
Margin |
|
dc.title |
Malignancy detection performance using excised breast tumor margin spectroscopic data
and an optimal decision fusion based approach
|
|
dc.type |
Master's thesis |
|
dc.department |
Electrical and Computer Engineering |
|
duke.embargo.months |
24 |
|
dcterms.provenance |
Corrupt PDF replaced with valid copy, 2018-04-04--mjf33 |
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