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

Publication ,  Journal Article
Dobbs, JL; Mueller, JL; Krishnamurthy, S; Shin, D; Kuerer, H; Yang, W; Ramanujam, N; Richards-Kortum, R
Published in: Breast cancer research : BCR
August 2015

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.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.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 %.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.

Duke Scholars

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Published In

Breast cancer research : BCR

DOI

EISSN

1465-542X

ISSN

1465-5411

Publication Date

August 2015

Volume

17

Start / End Page

105

Related Subject Headings

  • Sensitivity and Specificity
  • Optical Imaging
  • Oncology & Carcinogenesis
  • Microscopy, Confocal
  • Humans
  • Female
  • Diagnosis, Differential
  • Contrast Media
  • Breast Neoplasms
  • Breast
 

Citation

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ICMJE
MLA
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Dobbs, J. L., Mueller, J. L., Krishnamurthy, S., Shin, D., Kuerer, H., Yang, W., … Richards-Kortum, R. (2015). Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues. Breast Cancer Research : BCR, 17, 105. https://doi.org/10.1186/s13058-015-0617-9
Dobbs, Jessica L., Jenna L. Mueller, Savitri Krishnamurthy, Dongsuk Shin, Henry Kuerer, Wei Yang, Nirmala Ramanujam, and Rebecca Richards-Kortum. “Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues.Breast Cancer Research : BCR 17 (August 2015): 105. https://doi.org/10.1186/s13058-015-0617-9.
Dobbs JL, Mueller JL, Krishnamurthy S, Shin D, Kuerer H, Yang W, et al. Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues. Breast cancer research : BCR. 2015 Aug;17:105.
Dobbs, Jessica L., et al. “Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues.Breast Cancer Research : BCR, vol. 17, Aug. 2015, p. 105. Epmc, doi:10.1186/s13058-015-0617-9.
Dobbs JL, Mueller JL, Krishnamurthy S, Shin D, Kuerer H, Yang W, Ramanujam N, Richards-Kortum R. Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues. Breast cancer research : BCR. 2015 Aug;17:105.

Published In

Breast cancer research : BCR

DOI

EISSN

1465-542X

ISSN

1465-5411

Publication Date

August 2015

Volume

17

Start / End Page

105

Related Subject Headings

  • Sensitivity and Specificity
  • Optical Imaging
  • Oncology & Carcinogenesis
  • Microscopy, Confocal
  • Humans
  • Female
  • Diagnosis, Differential
  • Contrast Media
  • Breast Neoplasms
  • Breast