Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues

Simple item page
dc.citation.journalTitleBreast Cancer Researchen_US
dc.contributor.authorDobbs, Jessica L.en_US
dc.contributor.authorMueller, Jenna L.en_US
dc.contributor.authorKrishnamurthy, Savitrien_US
dc.contributor.authorShin, Dongsuken_US
dc.contributor.authorKuerer, Henryen_US
dc.contributor.authorYang, Weien_US
dc.contributor.authorRamanujam, Nirmalaen_US
dc.contributor.authorRichards-Kortum, Rebeccaen_US
dc.date.accessioned2017-02-02T07:05:09Zen_US
dc.date.available2017-02-02T07:05:09Zen_US
dc.date.issued2015en_US
dc.date.updated2017-02-02T07:05:09Zen_US
dc.description.abstractAbstract Introduction Pathologists currently diagnose breast lesions through histologic assessment, which requires fixation and tissue preparation. The diagnostic criteria used to classify breast lesions are qualitative and subjective, and inter-observer discordance has been shown to be a significant challenge in the diagnosis of selected breast lesions, particularly for borderline proliferative lesions. Thus, there is an opportunity to develop tools to rapidly visualize and quantitatively interpret breast tissue morphology for a variety of clinical applications. Methods Toward this end, we acquired images of freshly excised breast tissue specimens from a total of 34 patients using confocal fluorescence microscopy and proflavine as a topical stain. We developed computerized algorithms to segment and quantify nuclear and ductal parameters that characterize breast architectural features. A total of 33 parameters were evaluated and used as input to develop a decision tree model to classify benign and malignant breast tissue. Benign features were classified in tissue specimens acquired from 30 patients and malignant features were classified in specimens from 22 patients. Results The decision tree model that achieved the highest accuracy for distinguishing between benign and malignant breast features used the following parameters: standard deviation of inter-nuclear distance and number of duct lumens. The model achieved 81 % sensitivity and 93 % specificity, corresponding to an area under the curve of 0.93 and an overall accuracy of 90 %. The model classified IDC and DCIS with 92 % and 96 % accuracy, respectively. The cross-validated model achieved 75 % sensitivity and 93 % specificity and an overall accuracy of 88 %. Conclusions These results suggest that proflavine staining and confocal fluorescence microscopy combined with image analysis strategies to segment morphological features could potentially be used to quantitatively diagnose freshly obtained breast tissue at the point of care without the need for tissue preparation.en_US
dc.identifier.citationDobbs, Jessica L., Mueller, Jenna L., Krishnamurthy, Savitri, et al.. "Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues." <i>Breast Cancer Research,</i> (2015) BioMed Central: http://dx.doi.org/10.1186/s13058-015-0617-9.en_US
dc.identifier.doihttp://dx.doi.org/10.1186/s13058-015-0617-9en_US
dc.identifier.urihttps://hdl.handle.net/1911/93840en_US
dc.language.isoengen_US
dc.publisherBioMed Centralen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.titleMicro-anatomical quantitative optical imaging: toward automated assessment of breast tissuesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
local.sword.agentBioMed Centralen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
13058_2015_Article_617.pdf
Size:
3.2 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.61 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Faculty Publications
Bioengineering Publications