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dc.contributor.authorDobbs, Jessica L.
Mueller, Jenna L.
Krishnamurthy, Savitri
Shin, Dongsuk
Kuerer, Henry
Yang, Wei
Ramanujam, Nirmala
Richards-Kortum, Rebecca
dc.date.accessioned 2015-09-23T19:33:32Z
dc.date.available 2015-09-23T19:33:32Z
dc.date.issued 2015
dc.identifier.citation Dobbs, Jessica L., Mueller, Jenna L., Krishnamurthy, Savitri, et al.. "Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues." Breast Cancer Research, 17, (2015) BioMed Central: http://dx.doi.org/10.1186/s13058-015-0617-9.
dc.identifier.urihttps://hdl.handle.net/1911/81708
dc.descriptionNEWS COVERAGE: A news release based on this journal publication is available online: Imaging software could speed breast cancer diagnosis [http://news.rice.edu/2015/08/21/imaging-software-could-speed-breast-cancer-diagnosis/]
dc.description.abstract 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 81ᅠ% sensitivity and 93ᅠ% specificity, corresponding to an area under the curve of 0.93 and an overall accuracy of 90ᅠ%. The model classified IDC and DCIS with 92ᅠ% and 96ᅠ% accuracy, respectively. The cross-validated model achieved 75ᅠ% sensitivity and 93ᅠ% specificity and an overall accuracy of 88ᅠ%. 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.
dc.language.iso eng
dc.publisher BioMed Central
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.title Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues
dc.type Journal article
dc.contributor.funder U.S. Department of Defense
dc.contributor.funder National Institutes of Health
dc.contributor.funder Susan G. Komen for the Cure
dc.citation.journalTitle Breast Cancer Research
dc.citation.volumeNumber 17
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1186/s13058-015-0617-9
dc.identifier.grantID Era of Hope Award W81XWH-09-1-0410 (U.S. Department of Defense)
dc.identifier.grantID 1R01EB01157 (National Institutes of Health)
dc.identifier.grantID KG091020 (Susan G. Komen for the Cure)
dc.type.publication publisher version


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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Except where otherwise noted, this item's license is described as This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.