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Rapid staining and imaging of subnuclear features to differentiate between malignant and benign breast tissues at a point-of-care setting

Abstract

Purpose

Histopathology is the clinical standard for tissue diagnosis; however, it requires tissue processing, laboratory personnel and infrastructure, and a highly trained pathologist to diagnose the tissue. Optical microscopy can provide real-time diagnosis, which could be used to inform the management of breast cancer. The goal of this work is to obtain images of tissue morphology through fluorescence microscopy and vital fluorescent stains and to develop a strategy to segment and quantify breast tissue features in order to enable automated tissue diagnosis.

Methods

We combined acriflavine staining, fluorescence microscopy, and a technique called sparse component analysis to segment nuclei and nucleoli, which are collectively referred to as acriflavine positive features (APFs). A series of variables, which included the density, area fraction, diameter, and spacing of APFs, were quantified from images taken from clinical core needle breast biopsies and used to create a multivariate classification model. The model was developed using a training data set and validated using an independent testing data set.

Results

The top performing classification model included the density and area fraction of smaller APFs (those less than 7 µm in diameter, which likely correspond to stained nucleoli).When applied to the independent testing set composed of 25 biopsy panels, the model achieved a sensitivity of 82 %, a specificity of 79 %, and an overall accuracy of 80 %.

Conclusions

These results indicate that our quantitative microscopy toolbox is a potentially viable approach for detecting the presence of malignancy in clinical core needle breast biopsies.

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Acknowledgments

We thank Dr. Rebecca Richards-Kortum and her student, Jessica Dobbs, for providing the imaging system and guidance on image acquisition. We acknowledge financial support from Department of Defense Grant Number W81XWH-09-1-0410 and NIH Grant Number 1R01EB01157.

Funding

This study was funded by the Department of Defense (Grant Number W81XWH-09-1-0410) and the NIH (Grant Number 1R01EB01157).

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Authors

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Correspondence to Jenna L. Mueller.

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Conflict of interest

Dr. Ramanujam has founded a company called Zenalux Biomedical, and she and other team members have developed technologies related to this work where the investigators or Duke may benefit financially if this system is sold commercially. The other authors declare they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Mueller, J.L., Gallagher, J.E., Chitalia, R. et al. Rapid staining and imaging of subnuclear features to differentiate between malignant and benign breast tissues at a point-of-care setting. J Cancer Res Clin Oncol 142, 1475–1486 (2016). https://doi.org/10.1007/s00432-016-2165-9

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  • DOI: https://doi.org/10.1007/s00432-016-2165-9

Keywords

  • Optical fluorescence imaging
  • Breast cancer
  • Image analysis
  • Logistic models