Advertisement

Journal of Cancer Research and Clinical Oncology

, Volume 142, Issue 7, pp 1475–1486 | Cite as

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

  • Jenna L. MuellerEmail author
  • Jennifer E. Gallagher
  • Rhea Chitalia
  • Marlee Krieger
  • Alaattin Erkanli
  • Rebecca M. Willett
  • Joseph Geradts
  • Nimmi Ramanujam
Original Article – Cancer Research

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.

Keywords

Optical fluorescence imaging Breast cancer Image analysis Logistic models 

Notes

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).

Compliance with ethical standards

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.

Supplementary material

432_2016_2165_MOESM1_ESM.pdf (566 kb)
Supplementary material 1 (PDF 565 kb)

References

  1. Adeyi OA (2011) Pathology services in developing countries-The West African experience. Arch Pathol Lab Med 135:183–186PubMedGoogle Scholar
  2. Ballard D (1981) Generalizing the hough transform to detect arbitrary shapes, vol 13. Pattern Recognition,Google Scholar
  3. Balu M et al (2014) Distinguishing between benign and malignant melanocytic nevi by in vivo multiphoton microscopy. Cancer Res 74:2688–2697. doi: 10.1158/0008-5472.CAN-13-2582 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Boppart SA, Luo W, Marks DL, Singletary KW (2004) Optical coherence tomography: feasibility for basic research and image-guided surgery of breast cancer. Breast Cancer Res Treat 84:85–97. doi: 10.1023/B:BREA.0000018401.13609.54 CrossRefPubMedGoogle Scholar
  5. Chasles F, Dubertret B, Boccara AC (2007) Optimization and characterization of a structured illumination microscope. Opt Express 15:16130–16140CrossRefPubMedGoogle Scholar
  6. Clark AL, Gillenwater AM, Collier TG, Alizadeh-Naderi R, El-Naggar AK, Richards-Kortum RR (2003) Confocal microscopy for real-time detection of oral cavity neoplasia. Clin Cancer Res 9:4714–4721PubMedGoogle Scholar
  7. Clark AL, Gillenwater A, Alizadeh-Naderi R, El-Naggar AK, Richards-Kortum R (2004) Detection and diagnosis of oral neoplasia with an optical coherence microscope. J Biomed Opt 9:1271–1280. doi: 10.1117/1.1805558 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Cohen C (1996) Image cytometric analysis in pathology. Hum Pathol 27:482–493CrossRefPubMedGoogle Scholar
  9. Dobbs JL, Ding H, Benveniste AP, Kuerer HM, Krishnamurthy S, Yang W, Richards-Kortum R (2013) Feasibility of confocal fluorescence microscopy for real-time evaluation of neoplasia in fresh human breast tissue. J Biomed Opt 18:106016. doi: 10.1117/1.JBO.18.10.106016 CrossRefPubMedGoogle Scholar
  10. Drezek RA et al (2003) Optical imaging of the cervix. Cancer 98:2015–2027. doi: 10.1002/cncr.11678 CrossRefPubMedGoogle Scholar
  11. Ferguson LR, Denny WA (1991) The genetic toxicology of acridines. Mutat Res 258:123–160CrossRefPubMedGoogle Scholar
  12. Gareau DS, Jeon H, Nehal KS, Rajadhyaksha M (2012) Rapid screening of cancer margins in tissue with multimodal confocal microscopy. J Surg Res 178:533–538. doi: 10.1016/j.jss.2012.05.059 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Gustafsson MGL (2000) Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J Microsc Oxford 198:82–87. doi: 10.1046/j.1365-2818.2000.00710.x CrossRefGoogle Scholar
  14. Hagen N, Gao L, Tkaczyk TS (2012) Quantitative sectioning and noise analysis for structured illumination microscopy. Opt Express 20:403–413. doi: 10.1364/OE.20.000403 CrossRefPubMedGoogle Scholar
  15. Hsiung PL, Phatak DR, Chen Y, Aguirre AD, Fujimoto JG, Connolly JL (2007) Benign and malignant lesions in the human breast depicted with ultrahigh resolution and three-dimensional optical coherence tomography. Radiology 244:865–874. doi: 10.1148/radiol.2443061536 CrossRefPubMedGoogle Scholar
  16. Jacobs L (2008) Positive margins: the challenge continues for breast surgeons. Ann Surg Oncol 15:1271–1272. doi: 10.1245/s10434-007-9766-0 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Karen JK, Gareau DS, Dusza SW, Tudisco M, Rajadhyaksha M, Nehal KS (2009) Detection of basal cell carcinomas in Mohs excisions with fluorescence confocal mosaicing microscopy. Br J Dermatol 160:1242–1250. doi: 10.1111/j.1365-2133.2009.09141.x CrossRefPubMedPubMedCentralGoogle Scholar
  18. Krolenko SA, Adamyan SY, Belyaeva TN, Mozhenok TP (2006) Acridine orange accumulation in acid organelles of normal and vacuolated frog skeletal muscle fibres. Cell Biol Int 30:933–939. doi: 10.1016/j.cellbi.2006.06.017 CrossRefPubMedGoogle Scholar
  19. Kumar V, Abbas A, Fausto N (2005) Robbins and Cotran pathologic basis of disease, 7th edn. Elsevier Saunders, PhiladelphiaGoogle Scholar
  20. Millot C, Dufer J (2000) Clinical applications of image cytometry to human tumour analysis. Histol Histopathol 15:1185–1200PubMedGoogle Scholar
  21. Moran MS et al (2014) Society of surgical oncology-American Society for radiation oncology consensus guideline on margins for breast-conserving surgery with whole-breast irradiation in stages I and II invasive breast cancer. J Clin Oncol 32:1507–1516CrossRefPubMedGoogle Scholar
  22. Mueller JL et al (2013) Quantitative segmentation of fluorescence microscopy images of heterogeneous tissue: application to the detection of residual disease in tumor margins. PLoS ONE 8:e66198. doi: 10.1371/journal.pone.0066198 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Muldoon TJ, Pierce MC, Nida DL, Williams MD, Gillenwater A, Richards-Kortum R (2007) Subcellular-resolution molecular imaging within living tissue by fiber microendoscopy. Opt Express 15:16413–16423CrossRefPubMedPubMedCentralGoogle Scholar
  24. Muldoon TJ et al (2010) Evaluation of quantitative image analysis criteria for the high-resolution microendoscopic detection of neoplasia in Barrett’s esophagus. J Biomed Opt 15:026027. doi: 10.1117/1.3406386 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Nandakumar V et al (2012) Isotropic 3D nuclear morphometry of normal, fibrocystic and malignant breast epithelial cells reveals new structural alterations. PLoS ONE 7:e29230. doi: 10.1371/journal.pone.0029230 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Nguyen FT et al (2009) Intraoperative evaluation of breast tumor margins with optical coherence tomography. Cancer Res 69:8790–8796. doi: 10.1158/0008-5472.CAN-08-4340 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Nyirenda N, Farkas DL, Ramanujan VK (2011) Preclinical evaluation of nuclear morphometry and tissue topology for breast carcinoma detection and margin assessment. Breast Cancer Res Treat 126:345–354. doi: 10.1007/s10549-010-0914-z CrossRefPubMedGoogle Scholar
  28. Rakha EA et al (2010) Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res 12:1–12Google Scholar
  29. Rambau PF (2011) Pathology practice in a resource-poor setting: Mwanza, Tanzania. Arch Pathol Lab Med 135:191–193. doi: 10.1043/1543-2165-135.2.191 PubMedGoogle Scholar
  30. Schlichenmeyer TC, Wang M, Elfer KN, Brown JQ (2014) Video-rate structured illumination microscopy for high-throughput imaging of large tissue areas Biomed. Opt Express 5:366–377. doi: 10.1364/BOE.5.000366 CrossRefGoogle Scholar
  31. Sun JG, Adie SG, Chaney EJ, Boppart SA (2013) Segmentation and correlation of optical coherence tomography and X-ray images for breast cancer diagnostics. J Innov Opt Health Sci 6:1350015. doi: 10.1142/S1793545813500156 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Tanbakuchi AA, Rouse AR, Udovich JA, Hatch KD, Gmitro AF (2009) Clinical confocal microlaparoscope for real-time in vivo optical biopsies. J Biomed Opt 14:044030. doi: 10.1117/1.3207139 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Tanbakuchi AA, Udovich JA, Rouse AR, Hatch KD, Gmitro AF (2010) In vivo imaging of ovarian tissue using a novel confocal microlaparoscope. Am J Obstet Gynecol 202:90.e91–90.e99. doi: 10.1016/j.ajog.2009.07.027 CrossRefGoogle Scholar
  34. Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35CrossRefPubMedGoogle Scholar
  35. Zysk AM, Nguyen FT, Oldenburg AL, Marks DL, Boppart SA (2007) Optical coherence tomography: a review of clinical development from bench to bedside. J Biomed Opt 12:051403. doi: 10.1117/1.2793736 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jenna L. Mueller
    • 1
    Email author
  • Jennifer E. Gallagher
    • 2
  • Rhea Chitalia
    • 1
  • Marlee Krieger
    • 1
  • Alaattin Erkanli
    • 3
  • Rebecca M. Willett
    • 4
  • Joseph Geradts
    • 5
  • Nimmi Ramanujam
    • 1
  1. 1.Department of Biomedical EngineeringDuke UniversityDurhamUSA
  2. 2.Department of SurgeryDuke University Medical CenterDurhamUSA
  3. 3.Department of Biostatistics and BioinformaticsDuke UniversityDurhamUSA
  4. 4.Department of Electrical and Computer EngineeringUniversity of Wisconsin - MadisonMadisonUSA
  5. 5.Department of PathologyBrigham and Women’s HospitalBostonUSA

Personalised recommendations