Rapid staining and imaging of subnuclear features to differentiate between malignant and benign breast tissues at a point-of-care setting.
OBJECTIVE
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.
Department of Biomedical Engineering, Duke University, 136 Hudson Hall Box 90281, Durham, NC, 27708, USA. jenna.mueller@duke.edu.
Jenna L
JL
Mueller
Department of Surgery, Duke University Medical Center, 30 Medicine Drive White Zone, 3rd Floor, Suite 3570, Durham, NC, 27710, USA.
Jennifer E
JE
Gallagher
Department of Biomedical Engineering, Duke University, 136 Hudson Hall Box 90281, Durham, NC, 27708, USA.
Rhea
R
Chitalia
Department of Biomedical Engineering, Duke University, 136 Hudson Hall Box 90281, Durham, NC, 27708, USA.
Marlee
M
Krieger
Department of Biostatistics and Bioinformatics, Duke University, Brightleaf Square Suite 22B, 905 West Main Street, Durham, NC, 27701, USA.
Alaattin
A
Erkanli
Department of Electrical and Computer Engineering, University of Wisconsin - Madison, 1415 Engineering Drive, Madison, WI, 53706, USA.
Rebecca M
RM
Willett
Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Joseph
J
Geradts
Department of Biomedical Engineering, Duke University, 136 Hudson Hall Box 90281, Durham, NC, 27708, USA.
Nimmi
N
Ramanujam
D001706:Biopsy; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D019095:Point-of-Care Systems; D013194:Staining and Labeling
Breast cancer; Image analysis; Logistic models; Optical fluorescence imaging