Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues.
BACKGROUND
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.
Department of Bioengineering, Rice University, 6500 Main Street, BRC 502, Houston, TX, 77030, USA. jessica.l.dobbs@gmail.com.
Jessica L
JL
Dobbs
Department of Biomedical Engineering, Duke University, 101 Science Drive, Room 136 Hudson Hall, Box 90281, Durham, NC, 27708, USA. jenna.mueller@duke.edu.
Jenna L
JL
Mueller
Department of Pathology, University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1350, Houston, TX, 77030, USA. skrishna@mdanderson.org.
Savitri
S
Krishnamurthy
Department of Bioengineering, Rice University, 6500 Main Street, BRC 502, Houston, TX, 77030, USA. biomechanicalengineer@gmail.com.
Dongsuk
D
Shin
Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 0444, Houston, TX, 77030, USA. hkuerer@mdanderson.org.
Henry
H
Kuerer
Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1350, Houston, TX, 77030, USA. wyang@mdanderson.org.
Wei
W
Yang
Department of Biomedical Engineering, Duke University, 101 Science Drive, Room 136 Hudson Hall, Box 90281, Durham, NC, 27708, USA. nimmi@duke.edu.
Nirmala
N
Ramanujam
Department of Bioengineering, Rice University, 6500 Main Street, BRC 502, Houston, TX, 77030, USA. rkortum@rice.edu.
Rebecca
R
Richards-Kortum
D000465:Algorithms; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000473:pathology*; D003287:Contrast Media / Q000008:administration & dosage; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D018613:Microscopy, Confocal / Q000379:methods; D061848:Optical Imaging / Q000379:methods; D012680:Sensitivity and Specificity
D003287:Contrast Media