Skip to main content

Current Methods for Intraoperative Application

  • Chapter
  • First Online:
Intraoperative Flow Cytometry
  • 228 Accesses

Abstract

Breast cancer is the most common cancer in women worldwide. Since 2008, worldwide breast cancer incidence has increased by more than 20%, whilst mortality has increased by 14%. The current gold standard in breast cancer screening includes X-ray mammography providing high diagnostic accuracy, however, diagnostic tools that would facilitate intraoperative assessment of surgical margins have been less extensively explored. Margin classification and a definitive diagnosis of breast malignancies are currently provided by histopathological assessment which can be time-consuming, subjective and is lacking automation. Ongoing research is exploring new technologies to overcome the limitations in breast cancer diagnosis and allow margin clearance within the surgical theatre, which would minimize repeat visits and surgical treatments and improve prognosis. This chapter focuses on emerging technologies and highlights their clinical performance and their potential role in reducing positive margins and re-excision rates in women with breast cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mushlin AI, Kouides RW, Shapiro DE. Estimating the accuracy of screening mammography: a meta-analysis. Am J Prev Med. 1998;14:143–53.

    Article  CAS  PubMed  Google Scholar 

  2. de Boniface J, Szulkin R, Johansson AL. Survival after breast conservation vs mastectomy adjusted for comorbidity and socioeconomic status: a Swedish national 6-year follow-up of 48 986 women. JAMA Surg. 2021;156:628–37.

    Article  PubMed  PubMed Central  Google Scholar 

  3. de Koning SGB, Peeters M-JTV, Jóźwiak K, Bhairosing PA, Ruers TJ. Tumor resection margin definitions in breast-conserving surgery: systematic review and meta-analysis of the current literature. Clin Breast Cancer. 2018;18:e595–600.

    Article  Google Scholar 

  4. Wj H, As E, Js R, Parker C, Dh B. Rates of margin positive resection with breast conservation for invasive breast cancer using the NCDB. Breast. 2021;60:86–9. https://doi.org/10.1016/j.breast.2021.08.012.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Chagpar AB, et al. A randomized, controlled trial of cavity shave margins in breast cancer. N Engl J Med. 2015;373:503–10. https://doi.org/10.1056/NEJMoa1504473.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Coopey S, et al. The safety of multiple re-excisions after lumpectomy for breast cancer. Ann Surg Oncol. 2011;18:3797–801. https://doi.org/10.1245/s10434-011-1802-4.

    Article  PubMed  Google Scholar 

  7. Coopey SB, et al. Lumpectomy cavity shaved margins do not impact re-excision rates in breast cancer patients. Ann Surg Oncol. 2011;18:3036. https://doi.org/10.1245/s10434-011-1909-7.

    Article  PubMed  Google Scholar 

  8. Esbona K, Li Z, Wilke LG. Intraoperative imprint cytology and frozen section pathology for margin assessment in breast conservation surgery: a systematic review. Ann Surg Oncol. 2012;19:3236–45. https://doi.org/10.1245/s10434-012-2492-2.

    Article  PubMed  PubMed Central  Google Scholar 

  9. McCahill LE, et al. Variability in reexcision following breast conservation surgery. JAMA. 2012;307:467–75. https://doi.org/10.1001/jama.2012.43.

    Article  PubMed  Google Scholar 

  10. St John ER, et al. Diagnostic accuracy of intraoperative techniques for margin assessment in breast cancer surgery. Ann Surg. 2017;265:300–10.

    Article  PubMed  Google Scholar 

  11. Laucirica R. Intraoperative assessment of the breast: guidelines and potential pitfalls. Arch Pathol Lab Med. 2005;129:1565–74.

    Article  PubMed  Google Scholar 

  12. Weinberg E, et al. Local recurrence in lumpectomy patients after imprint cytology margin evaluation. Am J Surg. 2004;188:349–54.

    Article  PubMed  Google Scholar 

  13. Pradipta AR, et al. Emerging technologies for real-time intraoperative margin assessment in future breast-conserving surgery. Adv Sci. 2020;7:1901519.

    Article  CAS  Google Scholar 

  14. Thill M, Dittmer C, Baumann K, Friedrichs K, Blohmer J-U. MarginProbe® – final results of the German post-market study in breast conserving surgery of ductal carcinoma in situ. Breast. 2014;23:94–6. https://doi.org/10.1016/j.breast.2013.11.002.

    Article  PubMed  Google Scholar 

  15. Alexiou GA, et al. Fast cell cycle analysis for intraoperative characterization of brain tumor margins and malignancy. J Clin Neurosci. 2015;22:129–32. https://doi.org/10.1016/j.jocn.2014.05.029.

    Article  PubMed  Google Scholar 

  16. Vartholomatos G, et al. Intraoperative cell cycle analysis for tumor margins evaluation: the future is now? Int J Surg. 2018;53:380–1. https://doi.org/10.1016/j.ijsu.2018.03.046.

    Article  PubMed  Google Scholar 

  17. Vartholomatos G, et al. Intraoperative flow cytometry for head and neck lesions. Assessment of malignancy and tumour-free resection margins. Oral Oncol. 2019;99:104344. https://doi.org/10.1016/j.oraloncology.2019.06.025.

    Article  PubMed  Google Scholar 

  18. Shioyama T, Muragaki Y, Maruyama T, Komori T, Iseki H. Intraoperative flow cytometry analysis of glioma tissue for rapid determination of tumor presence and its histopathological grade. J Neurosurg. 2013;118:1232–8.

    Article  PubMed  Google Scholar 

  19. Alexiou GA, et al. The role of fast cell cycle analysis in pediatric brain tumors. Pediatr Neurosurg. 2015;50:257–63.

    Article  PubMed  Google Scholar 

  20. Vartholomatos G, et al. Rapid assessment of resection margins during breast conserving surgery using intraoperative flow cytometry. Clin Breast Cancer. 2021;21:e602–10.

    Article  CAS  PubMed  Google Scholar 

  21. Paraskevaidi M, et al. Clinical applications of infrared and Raman spectroscopy in the fields of cancer and infectious diseases. Appl Spectrosc Rev. 2021;56:804–68.

    Article  CAS  Google Scholar 

  22. Jermyn M, et al. Intraoperative brain cancer detection with Raman spectroscopy in humans. Sci Transl Med. 2015;7:274ra219. https://doi.org/10.1126/scitranslmed.aaa2384.

    Article  CAS  Google Scholar 

  23. Lin K, et al. Rapid fiber-optic Raman spectroscopy for real-time in vivo detection of gastric intestinal metaplasia during clinical gastroscopy. Cancer Prev Res. 2016;9:476–83.

    Article  CAS  Google Scholar 

  24. Malik A, et al. In vivo Raman spectroscopy–assisted early identification of potential second primary/recurrences in oral cancers: an exploratory study. Head Neck. 2017;39:2216–23.

    Article  PubMed  Google Scholar 

  25. McGregor HC, et al. Real-time endoscopic Raman spectroscopy for in vivo early lung cancer detection. J Biophotonics. 2017;10:98–110.

    Article  CAS  PubMed  Google Scholar 

  26. Wang J, et al. Simultaneous fingerprint and high-wavenumber fiber-optic Raman spectroscopy improves in vivo diagnosis of esophageal squamous cell carcinoma at endoscopy. Sci Rep. 2015;5:1–10.

    Google Scholar 

  27. Zhao J, Lui H, Kalia S, Zeng H. Real-time Raman spectroscopy for automatic in vivo skin cancer detection: an independent validation. Anal Bioanal Chem. 2015;407:8373–9.

    Article  CAS  PubMed  Google Scholar 

  28. Zhao J, Zeng H, Kalia S, Lui H. Wavenumber selection based analysis in Raman spectroscopy improves skin cancer diagnostic specificity. Analyst. 2016;141:1034–43.

    Article  CAS  PubMed  Google Scholar 

  29. Schleusener J, et al. In vivo study for the discrimination of cancerous and normal skin using fibre probe-based Raman spectroscopy. Exp Dermatol. 2015;24:767–72.

    Article  PubMed  Google Scholar 

  30. Shipp DW, et al. Intra-operative spectroscopic assessment of surgical margins during breast conserving surgery. Breast Cancer Res. 2018;20:69. https://doi.org/10.1186/s13058-018-1002-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Haka AS, et al. In vivo margin assessment during partial mastectomy breast surgery using Raman spectroscopy. Cancer Res. 2006;66:3317–22.

    Article  CAS  PubMed  Google Scholar 

  32. Talari AC, Rehman S, Rehman IU. Advancing cancer diagnostics with artificial intelligence and spectroscopy: identifying chemical changes associated with breast cancer. Expert Rev Mol Diagn. 2019;19:929–40.

    Article  CAS  PubMed  Google Scholar 

  33. Surmacki J, Brozek-Pluska B, Kordek R, Abramczyk H. The lipid-reactive oxygen species phenotype of breast cancer. Raman spectroscopy and mapping, PCA and PLSDA for invasive ductal carcinoma and invasive lobular carcinoma. Molecular tumorigenic mechanisms beyond Warburg effect. Analyst. 2015;140:2121–33.

    Article  CAS  PubMed  Google Scholar 

  34. Haka AS, et al. Diagnosing breast cancer by using Raman spectroscopy. Proc Natl Acad Sci. 2005;102:12371–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Haka AS, et al. Diagnosing breast cancer using Raman spectroscopy: prospective analysis. J Biomed Opt. 2009;14:054023.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Stone N, Baker R, Rogers K, Parker AW, Matousek P. Subsurface probing of calcifications with spatially offset Raman spectroscopy (SORS): future possibilities for the diagnosis of breast cancer. Analyst. 2007;132:899–905.

    Article  CAS  PubMed  Google Scholar 

  37. Keller MD, et al. Development of a spatially offset Raman spectroscopy probe for breast tumor surgical margin evaluation. J Biomed Opt. 2011;16:077006.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Wang Y, et al. Raman-encoded molecular imaging with topically applied SERS nanoparticles for intraoperative guidance of Lumpectomy. Cancer Res. 2017;77:4506–16.

    Article  CAS  PubMed  Google Scholar 

  39. Stevens O, Petterson IEI, Day JC, Stone N. Developing fibre optic Raman probes for applications in clinical spectroscopy. Chem Soc Rev. 2016;45:1919–34.

    Article  CAS  PubMed  Google Scholar 

  40. Yang N, et al. Urinary glycoprotein biomarker discovery for bladder cancer detection using LC/MS-MS and label-free quantification. Clin Cancer Res. 2011;17:3349–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lubes G, Goodarzi M. GC–MS based metabolomics used for the identification of cancer volatile organic compounds as biomarkers. J Pharm Biomed Anal. 2018;147:313–22.

    Article  CAS  PubMed  Google Scholar 

  42. Rodrigo MAM, et al. MALDI-TOF MS as evolving cancer diagnostic tool: a review. J Pharm Biomed Anal. 2014;95:245–55.

    Article  PubMed  Google Scholar 

  43. Balog J, et al. Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci Transl Med. 2013;5:194ra193.

    Article  Google Scholar 

  44. Paraskevaidi M, et al. Laser-assisted rapid evaporative ionisation mass spectrometry (LA-REIMS) as a metabolomics platform in cervical cancer screening. EBioMedicine. 2020;60:103017.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Tzafetas M, et al. The intelligent-knife (i-knife) and its intraoperative diagnostic advantage for the treatment of cervical disease. Proc Natl Acad Sci U S A. 2020;117:7338–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Phelps DL, et al. The surgical intelligent knife distinguishes normal, borderline and malignant gynaecological tissues using rapid evaporative ionisation mass spectrometry (REIMS). Br J Cancer. 2018;118:1349–58. https://doi.org/10.1038/s41416-018-0048-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Dória ML, et al. Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging. Sci Rep. 2016;6:39219.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Alexander J, et al. A novel methodology for in vivo endoscopic phenotyping of colorectal cancer based on real-time analysis of the mucosal lipidome: a prospective observational study of the iKnife. Surg Endosc. 2017;31:1361–70. https://doi.org/10.1007/s00464-016-5121-5.

    Article  PubMed  Google Scholar 

  49. St John ER, et al. Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery. Breast Cancer Res. 2017;19:59.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Calligaris D, et al. Application of desorption electrospray ionization mass spectrometry imaging in breast cancer margin analysis. Proc Natl Acad Sci U S A. 2014;111:15184–9. https://doi.org/10.1073/pnas.1408129111.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zhang J, et al. Nondestructive tissue analysis for ex vivo and in vivo cancer diagnosis using a handheld mass spectrometry system. Sci Transl Med. 2017;9:eaan3968.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Takáts Z, Wiseman JM, Gologan B, Cooks RG. Mass spectrometry sampling under ambient conditions with desorption electrospray ionization. Science. 2004;306:471–3.

    Article  PubMed  Google Scholar 

  53. Buchberger AR, DeLaney K, Johnson J, Li L. Mass spectrometry imaging: a review of emerging advancements and future insights. Anal Chem. 2018;90:240–65. https://doi.org/10.1021/acs.analchem.7b04733.

    Article  CAS  PubMed  Google Scholar 

  54. Ifa DR, Eberlin LS. Ambient ionization mass spectrometry for cancer diagnosis and surgical margin evaluation. Clin Chem. 2016;62:111–23.

    Article  CAS  PubMed  Google Scholar 

  55. Dill AL, Ifa DR, Manicke NE, Ouyang Z, Cooks RG. Mass spectrometric imaging of lipids using desorption electrospray ionization. J Chromatogr B. 2009;877:2883–9.

    Article  CAS  Google Scholar 

  56. Guenther S, et al. Spatially resolved metabolic phenotyping of breast cancer by desorption electrospray ionization mass spectrometry. Cancer Res. 2015;75:1828–37. https://doi.org/10.1158/0008-5472.Can-14-2258.

    Article  CAS  PubMed  Google Scholar 

  57. Dixon JM, et al. Intra-operative assessment of excised breast tumour margins using ClearEdge imaging device. Eur J Surg Oncol. 2016;42:1834–40. https://doi.org/10.1016/j.ejso.2016.07.141.

    Article  CAS  PubMed  Google Scholar 

  58. Thill M. MarginProbe®: intraoperative margin assessment during breast conserving surgery by using radiofrequency spectroscopy. Expert Rev Med Devices. 2013;10:301–15.

    Article  CAS  PubMed  Google Scholar 

  59. Karni T, et al. A device for real-time, intraoperative margin assessment in breast-conservation surgery. Am J Surg. 2007;194:467–73.

    Article  PubMed  Google Scholar 

  60. Allweis TM, et al. A prospective, randomized, controlled, multicenter study of a real-time, intraoperative probe for positive margin detection in breast-conserving surgery. Am J Surg. 2008;196:483–9. https://doi.org/10.1016/j.amjsurg.2008.06.024.

    Article  PubMed  Google Scholar 

  61. Pappo I, et al. Diagnostic performance of a novel device for real-time margin assessment in lumpectomy specimens. J Surg Res. 2010;160:277–81. https://doi.org/10.1016/j.jss.2009.02.025.

    Article  PubMed  Google Scholar 

  62. Rivera RJ, Holmes DR, Tafra L. Analysis of the impact of intraoperative margin assessment with adjunctive use of MarginProbe versus standard of care on tissue volume removed. Int J Surg Oncol. 2012;2012:868623.

    PubMed  PubMed Central  Google Scholar 

  63. Schnabel F, et al. A randomized prospective study of lumpectomy margin assessment with use of MarginProbe in patients with nonpalpable breast malignancies. Ann Surg Oncol. 2014;21:1589–95. https://doi.org/10.1245/s10434-014-3602-0.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Sebastian M, Akbari S, Anglin B, Lin EH, Police AM. The impact of use of an intraoperative margin assessment device on re-excision rates. Springerplus. 2015;4:1–6.

    Article  Google Scholar 

  65. Blohmer J-U, et al. MarginProbe© reduces the rate of re-excision following breast conserving surgery for breast cancer. Arch Gynecol Obstet. 2016;294:361–7.

    Article  PubMed  Google Scholar 

  66. Coble J, Reid V. Achieving clear margins. Directed shaving using MarginProbe, as compared to a full cavity shave approach. Am J Surg. 2017;213:627–30.

    Article  PubMed  Google Scholar 

  67. Kupstas A, et al. A novel modality for intraoperative margin assessment and its impact on re-excision rates in breast conserving surgery. Am J Surg. 2018;215:400–3.

    Article  PubMed  Google Scholar 

  68. Gooch JC, et al. The relationship of breast density and positive lumpectomy margins. Ann Surg Oncol. 2019;26:1729–36.

    Article  PubMed  Google Scholar 

  69. Geha RC, Taback B, Cadena L, Borden B, Feldman S. A single institution’s randomized double-armed prospective study of lumpectomy margins with adjunctive use of the MarginProbe in nonpalpable breast cancers. Breast J. 2020;26:2157–62.

    Article  PubMed  Google Scholar 

  70. LeeVan E, Ho BT, Seto S, Shen J. Use of MarginProbe as an adjunct to standard operating procedure does not significantly reduce re-excision rates in breast conserving surgery. Breast Cancer Res Treat. 2020;183:145–51. https://doi.org/10.1007/s10549-020-05773-5.

    Article  PubMed  Google Scholar 

  71. Cen C, et al. Margin assessment and re-excision rates for patients who have neoadjuvant chemotherapy and breast-conserving surgery. Ann Surg Oncol. 2021;28:5142–8.

    Article  PubMed  Google Scholar 

  72. Hoffman A, Ashkenazi I. The efficiency of MarginProbe in detecting positive resection margins in epithelial breast cancer following breast conserving surgery. Eur J Surg Oncol. 2022;48:1498–502. https://doi.org/10.1016/j.ejso.2022.02.021.

    Article  PubMed  Google Scholar 

  73. Boppart SA, et al. In vivo cellular optical coherence tomography imaging. Nat Med. 1998;4:861–5.

    Article  CAS  PubMed  Google Scholar 

  74. Huang D, et al. Optical coherence tomography. Science. 1991;254:1178–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Nguyen FT, et al. Intraoperative evaluation of breast tumor margins with optical coherence tomography. Cancer Res. 2009;69:8790–6. https://doi.org/10.1158/0008-5472.Can-08-4340.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Erickson-Bhatt SJ, et al. Real-time imaging of the resection bed using a handheld probe to reduce incidence of microscopic positive margins in cancer surgery. Cancer Res. 2015;75:3706–12. https://doi.org/10.1158/0008-5472.Can-15-0464.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Yang H, et al. Use of high-resolution full-field optical coherence tomography and dynamic cell imaging for rapid intraoperative diagnosis during breast cancer surgery. Cancer. 2020;126:3847–56. https://doi.org/10.1002/cncr.32838.

    Article  CAS  PubMed  Google Scholar 

  78. Gufler H, Franke FE, Wagner S, Rau WS. Fine structure of breast tissue on micro computed tomography: a feasibility study. Acad Radiol. 2011;18:230–4.

    Article  PubMed  Google Scholar 

  79. Gufler H, Wagner S, Franke FE. The interior structure of breast microcalcifications assessed with micro computed tomography. Acta Radiol. 2011;52:592–6.

    Article  PubMed  Google Scholar 

  80. Ritman EL. Current status of developments and applications of micro-CT. Annu Rev Biomed Eng. 2011;13:531–52.

    Article  CAS  PubMed  Google Scholar 

  81. Tang R, et al. A pilot study evaluating shaved cavity margins with micro-computed tomography: a novel method for predicting lumpectomy margin status intraoperatively. Breast J. 2013;19:485–9. https://doi.org/10.1111/tbj.12146.

    Article  PubMed  Google Scholar 

  82. DiCorpo D, et al. The role of micro-CT in imaging breast cancer specimens. Breast Cancer Res Treat. 2020;180:343–57. https://doi.org/10.1007/s10549-020-05547-z.

    Article  PubMed  Google Scholar 

  83. Smith BL, et al. Real-time, intraoperative detection of residual breast cancer in lumpectomy cavity walls using a novel cathepsin-activated fluorescent imaging system. Breast Cancer Res Treat. 2018;171:413–20.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Smith BL, et al. Feasibility study of a novel protease-activated fluorescent imaging system for real-time, intraoperative detection of residual breast cancer in breast conserving surgery. Ann Surg Oncol. 2020;27:1854–61.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Hwang ES, et al. Clinical impact of intraoperative margin assessment in breast-conserving surgery with a novel pegulicianine fluorescence–guided system: a nonrandomized controlled trial. JAMA Surg. 2022;157:573.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Weber WP, et al. Accuracy of frozen section analysis versus specimen radiography during breast-conserving surgery for nonpalpable lesions. World J Surg. 2008;32:2599–606.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maria Paraskevaidi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Paraskevaidi, M. (2023). Current Methods for Intraoperative Application. In: Alexiou, G., Vartholomatos, G. (eds) Intraoperative Flow Cytometry. Springer, Cham. https://doi.org/10.1007/978-3-031-33517-4_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-33517-4_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33516-7

  • Online ISBN: 978-3-031-33517-4

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics