Advertisement

Three-dimensional tumor visualization of invasive breast carcinomas using whole-mount serial section histopathology: implications for tumor size assessment

  • G. M. Clarke
  • C. M. B. Holloway
  • J. T. Zubovits
  • S. Nofech-Mozes
  • M. Murray
  • K. Liu
  • D. Wang
  • A. Kiss
  • M. J. YaffeEmail author
Preclinical study
  • 84 Downloads

Abstract

Purpose

Linear tumor size (T-size) estimated with conventional histology informs breast cancer management. Previously we demonstrated significant differences in margin and focality estimates using conventional histology versus digital whole-mount serial sections (WMSS). Using WMSS we can measure T-size or volume. Here, we compare WMSS T-size with volume, and with T-size measured conventionally. We also compare the ellipsoid model for calculating tumor volume to direct, WMSS measurement.

Methods

Two pathologists contoured regions of invasive carcinoma and measured T-size from both WMSS and (simulated) conventional sections in 55 consecutive lumpectomy specimens. Volume was measured directly from the contours. Measurements were compared using the paired t-test or Spearman’s rank-order correlation. A five-point ‘border index’ was devised and assigned to each case to parametrize tumor shape considering ‘compactness’ or cellularity. Tumor volumes calculated assuming ellipsoid geometry were compared with direct, WMSS measurements.

Results

WMSS reported significantly larger T-size than conventional histology in the majority of cases [61.8%, 34/55; means = (2.34 cm; 1.99 cm), p < 0.001], with a 16.4% (9/55) rate of ‘upstaging’. The majority of discordances were due to undersampling. T-size and volume were strongly correlated (r = 0.838, p < 0.001). Significantly lower volume was obtained with WMSS versus ellipsoid modeling [means = (1.18 cm3; 1.45 cm3), p < 0.001].

Conclusions

Significantly larger T-size is measured with WMSS than conventionally, due primarily to undersampling in the latter. Volume and linear size are highly correlated. Diffuse tumors interspersed with normal or non-invasive elements may be sampled less extensively than more localized masses. The ellipsoid model overestimates tumor volume.

Keywords

Breast cancer Lumpectomy Whole-mount serial sections Large-format histology Histologic size Tumor volume 

Abbreviations

TNM

Tumor Node Metastasis

AJCC/UICC

American Joint Committee on Cancer/Union for International Cancer Control

3-D

Three-dimensional

WMSS

Whole-mount serial sections

WM

Whole-mount

H&E

Hematoxylin and eosin

SCS

Simulated conventional sections

AP

Anterior–posterior

SI

Superior–inferior

ML

Medial–lateral

BI

Border Index

Notes

Acknowledgements

The authors are grateful to Ms Anoma Gunasekara for her assistance with recruitment, and together with Ms Yulia Yerofeyeva with organizing all aspects of the study logistics and patient tracking. Finally we acknowledge the co-operation of pathologist assistants Ms Anna-Marie Moskaluk, Mr Ian Cooper. Dr Laibao Sun and Dr Peter Leventis for performing the virtual sampling and overseeing the research protocol, and Adebayo Adeeko for assistance with specimen preparation.

Funding

This work was supported by the Canadian Breast Cancer Research Alliance/Canadian Cancer Society Research Institute.

Compliance with ethical standards

Conflict of interest

The authors do not have a conflict of interest.

Ethical approval

This study was conducted in full compliance with the applicable, current Canadian law.

References

  1. 1.
    Fisher B, Slack NH, Bross ID (1969) Cancer of the breast: size of neoplasm and prognosis. Cancer 24:1071–1080CrossRefGoogle Scholar
  2. 2.
    Tabar L, Vitak B, Chen HH et al (2000) The Swedish Two-County Trial twenty years later. updated mortality results and new insights from long-term follow-up. Radiol Clin N Am 38:625–651CrossRefGoogle Scholar
  3. 3.
    Leitner SP, Swern AS, Weinberger D et al (1995) Predictors of recurrence for patients with small (one centimeter or less) localized breast cancer (T1a,b N0 M0). Cancer 76:2266–2274CrossRefGoogle Scholar
  4. 4.
    Koscielny S, Tubiana M, Le MG et al (1984) Breast cancer: relationship between the size of the primary tumour and the probability of metastatic dissemination. Br J Cancer 49:709–715CrossRefGoogle Scholar
  5. 5.
    Amin M, Edge S, Greene F et al (2017) AJCC cancer staging manual, 8th edn. Springer, ChicagoCrossRefGoogle Scholar
  6. 6.
    Carter CL, Allen C, Henson DE (1989) Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer 63:181–187CrossRefGoogle Scholar
  7. 7.
    Koscielny S, Tubiana M, Lê MG et al (1984) Breast cancer: relationship between the size of the primary tumour and the probability of metastatic dissemination. Br J Cancer 49:709–715CrossRefGoogle Scholar
  8. 8.
    Smart CR, Myers MH, Gloeckler LA (1978) Implications from SEER data on breast cancer management. Cancer 41:787–789CrossRefGoogle Scholar
  9. 9.
    Lester S, Bose S, Chen Y-Y et al (2013) Protocol for the examination of specimens from patients with invasive carcinoma of the breast protocol applies to all invasive carcinomas of the breast, including ductal. Arch Pathol Lab Med 133(10):1515–1538Google Scholar
  10. 10.
    Graham R, Homer MJ, Katz J et al (2002) The pancake phenomenon contributes to the inaccuracy of margin assessment in patients with breast cancer. Am J Surg 184:89–93CrossRefGoogle Scholar
  11. 11.
    Clarke GM, Holloway CMB, Zubovits JT et al (2016) Whole-mount pathology of breast lumpectomy specimens improves detection of tumour margins and focality. Histopathology 69:35–44.  https://doi.org/10.1111/his.12912 CrossRefGoogle Scholar
  12. 12.
    Michaelson JS, Silverstein M, Wyatt J et al (2002) Predicting the survival of patients with breast carcinoma using tumor size. Cancer 95:713–723.  https://doi.org/10.1002/cncr.10742 CrossRefGoogle Scholar
  13. 13.
    Hofmeyer S, Pekár G, Gere M et al (2012) Comparison of the subgross distribution of the lesions in invasive ductal and lobular carcinomas of the breast: a large-format histology study. Int J Breast Cancer 2012:436141.  https://doi.org/10.1155/2012/436141 CrossRefGoogle Scholar
  14. 14.
    Dekker TJA, van de Velde CJH, van Pelt GW et al (2013) Prognostic significance of the tumor-stroma ratio: validation study in node-negative premenopausal breast cancer patients from the EORTC perioperative chemotherapy (POP) trial (10854). Breast Cancer Res Treat 139:371–379.  https://doi.org/10.1007/s10549-013-2571-5 CrossRefGoogle Scholar
  15. 15.
    Downey CL, Simpkins SA, White J et al (2014) The prognostic significance of tumour-stroma ratio in oestrogen receptor-positive breast cancer. Br J Cancer 110:1744–1747.  https://doi.org/10.1038/bjc.2014.69 CrossRefGoogle Scholar
  16. 16.
    Moorman AM, Vink R, Heijmans HJ et al (2012) The prognostic value of tumour-stroma ratio in triple-negative breast cancer. Eur J Surg Oncol 38:307–313.  https://doi.org/10.1016/j.ejso.2012.01.002 CrossRefGoogle Scholar
  17. 17.
    Guth U, Brenckle D, Huang DJ et al (2009) Three-dimensional pathological size assessment in primary breast carcinoma. Breast Cancer Res Treat 116:257–262CrossRefGoogle Scholar
  18. 18.
    Kerr KM, Lamb D (1988) A comparison of patient survival and tumour growth kinetics in human bronchogenic carcinoma. Br J Cancer 58:419–422CrossRefGoogle Scholar
  19. 19.
    Tsai CH, Lin CM, Hsieh CC et al (2006) Tumor volume is a better prognostic factor than greatest tumor diameter in stage Ia non-small cell lung cancer. Thorac Cardiovasc Surg 54:537–543CrossRefGoogle Scholar
  20. 20.
    Mai KT, Mokhtar G, Burns BF et al (2003) A simple technique for calculation of the volume of prostatic adenocarcinomas in radical prostatectomy specimens. Pathol Res Pr 199:599–604CrossRefGoogle Scholar
  21. 21.
    Clarke GM, Murray M, Holloway CMB et al (2012) 3D pathology volumetric technique: a method for calculating breast tumour volume from whole-mount serial section images. Int J Breast Cancer 2012:691205.  https://doi.org/10.1155/2012/691205 CrossRefGoogle Scholar
  22. 22.
    Sun L, Wang D, Zubovits JT et al (2009) An improved processing method for breast whole-mount serial sections for three-dimensional histopathology imaging. Am J Clin Pathol 131:383–392.  https://doi.org/10.1309/AJCPVBZZ4IKJHY3U CrossRefGoogle Scholar
  23. 23.
    Clarke GM, Eidt S, Sun L et al (2007) Whole-specimen histopathology: a method to produce whole-mount breast serial sections for 3-D digital histopathology imaging. Histopathology 50:232–242.  https://doi.org/10.1111/j.1365-2559.2006.02561.x CrossRefGoogle Scholar
  24. 24.
    Clarke GM, Peressotti C, Constantinou P et al (2010) Increasing specimen coverage using digital whole-mount breast pathology: implementation, clinical feasibility and application in research. Comput Med Imaging Graph 35:531–541.  https://doi.org/10.1016/j.compmedimag.2011.05.002 CrossRefGoogle Scholar
  25. 25.
    Clarke GM, Zubovits JT, Katic M et al (2007) Spatial resolution requirements for acquisition of the virtual screening slide for digital whole-specimen breast histopathology. Hum Pathol 38:1764–1771.  https://doi.org/10.1016/j.humpath.2007.04.006 CrossRefGoogle Scholar
  26. 26.
    Wapnir IL, Wartenberg DE, Greco RS (1996) Three dimensional staging of breast cancer. Breast Cancer Res Treat 41:15–19CrossRefGoogle Scholar
  27. 27.
    Merrill AL, Buckley J, Tang R et al (2016) A study of the growth patterns of breast carcinoma using 3D reconstruction: a pilot study. Breast J.  https://doi.org/10.1111/tbj.12688 Google Scholar
  28. 28.
    Moon HG, Kim N, Jeong S et al (2015) The clinical significance and molecular features of the spatial tumor shapes in breast cancers. PLoS ONE 10:1–14.  https://doi.org/10.1371/journal.pone.0143811 Google Scholar
  29. 29.
    Jackson PA, Merchant W, McCormick CJ, Cook MG (1994) A comparison of large block macrosectioning and conventional techniques in breast pathology. Virchows Arch 425:243–248CrossRefGoogle Scholar
  30. 30.
    Foschini MP, Baldovini C, Ishikawa Y, Eusebi V (2012) The value of large sections in surgical pathology. Int J Breast Cancer 2012:785947.  https://doi.org/10.1155/2012/785947 CrossRefGoogle Scholar
  31. 31.
    Foster MR, Harris L, Biesemier KW (2012) Large format histology may aid in the detection of unsuspected pathologic findings of potential clinical significance: a prospective multiyear single institution study. Int J Breast Cancer 2012:532547.  https://doi.org/10.1155/2012/532547 CrossRefGoogle Scholar
  32. 32.
    Tot T (2003) The diffuse type of invasive lobular carcinoma of the breast: morphology and prognosis. Virchows Arch 443:718–724.  https://doi.org/10.1007/s00428-003-0881-4 CrossRefGoogle Scholar
  33. 33.
    Tot T (2007) Clinical relevance of the distribution of the lesions in 500 consecutive breast cancer cases documented in large-format histologic sections. Cancer 110:2551–2560.  https://doi.org/10.1002/cncr.23052 CrossRefGoogle Scholar
  34. 34.
    Tot T (2016) Diffuse invasive breast carcinoma of no special type. Virchows Arch 468:199–206.  https://doi.org/10.1007/s00428-015-1873-x CrossRefGoogle Scholar
  35. 35.
    Güth U, Brenckle D, Huang DJ et al (2009) Three-dimensional pathological size assessment in primary breast carcinoma. Breast Cancer Res Treat 116:257–262.  https://doi.org/10.1007/s10549-008-0115-1 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Physical Sciences PlatformSunnybrook Research InstituteTorontoCanada
  2. 2.Department of Surgery, Faculty of MedicineUniversity of TorontoTorontoCanada
  3. 3.Department of SurgerySunnybrook Health Sciences CentreTorontoCanada
  4. 4.Department of Laboratory Medicine and Pathobiology, Faculty of MedicineUniversity of TorontoTorontoCanada
  5. 5.Department of PathologyScarborough and Rouge HospitalTorontoCanada
  6. 6.Sunnybrook Health Sciences CentreTorontoCanada
  7. 7.Physical Sciences PlatformSunnybrook Research InstituteTorontoCanada
  8. 8.Physical Sciences PlatformSunnybrook Research InstituteTorontoCanada
  9. 9.Research Design and BiostatisticsSunnybrook Research InstituteTorontoCanada
  10. 10.Institute of Health Policy, Management and EvaluationUniversity of TorontoTorontoCanada
  11. 11.Departments of Medical Biophysics and Medical Imaging, Faculty of MedicineUniversity of TorontoTorontoCanada
  12. 12.Physical Sciences Platform, Sunnybrook Research InstituteTorontoCanada

Personalised recommendations