Robot-assisted biopsy sampling for online Raman spectroscopy cancer confirmation in the operating room | International Journal of Computer Assisted Radiology and Surgery
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Robot-assisted biopsy sampling for online Raman spectroscopy cancer confirmation in the operating room

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Abstract

Purpose

Cancer confirmation in the operating room (OR) is crucial to improve local control in cancer therapies. Histopathological analysis remains the gold standard, but there is a lack of real-time in situ cancer confirmation to support margin confirmation or remnant tissue. Raman spectroscopy (RS), as a label-free optical technique, has proven its power in cancer detection and, when integrated into a robotic assistance system, can positively impact the efficiency of procedures and the quality of life of patients, avoiding potential recurrence.

Methods

A workflow is proposed where a 6-DOF robotic system (optical camera + MECA500 robotic arm) assists the characterization of fresh tissue samples using RS. Three calibration methods are compared for the robot, and the temporal efficiency is compared with standard hand-held analysis. For healthy/cancerous tissue discrimination, a 1D-convolutional neural network is proposed and tested on three ex vivo datasets (brain, breast, and prostate) containing processed RS and histopathology ground truth.

Results

The robot achieves a minimum error of 0.20 mm (0.12) on a set of 30 test landmarks and demonstrates significant time reduction in 4 of the 5 proposed tasks. The proposed classification model can identify brain, breast, and prostate cancer with an accuracy of 0.83 (0.02), 0.93 (0.01), and 0.71 (0.01), respectively.

Conclusion

Automated RS analysis with deep learning demonstrates promising classification performance compared to commonly used support vector machines. Robotic assistance in tissue characterization can contribute to highly accurate, rapid, and robust biopsy analysis in the OR. These two elements are an important step toward real-time cancer confirmation using RS and OR integration.

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References

  1. David S, Tran T, Dallaire F, Sheehy G, Azzi F, Trudel D, Tremblay F, Omeroglu A, Leblond F, Meterissian S (2023) In-situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer. J Biomed Opt 28(3):036009–036009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Desroches J, Jermyn M, Pinto M, Picot F, Tremblay MA, Obaid S, Marple E, Urmey K, Trudel D, Soulez G, Guiot MC, Wilson BC, Petrecca K, Leblond F (2018) A new method using Raman spectroscopy for in-vivo targeted brain cancer tissue biopsy. Sci Rep 8(1):1792

    Article  PubMed  PubMed Central  Google Scholar 

  3. Gaba F, Tipping WJ, Salji M, Faulds K, Graham D, Leung HY (2022) Raman spectroscopy in prostate cancer: techniques, applications and advancements. Cancers 14(6):1535

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Grajales D, Picot F, Shams R, Dallaire F, Sheehy G, Alley S, Barkati M, Delouya G, Carrier JF, Birlea M, Trudel D, Leblond F, Ménard C, Kadoury S (2022) Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. part 2: In-vivo tumor-targeting using a classification model combining spectral and MRI-radiomics features. J Biomed Opt 27(9):095004–095004

    Article  PubMed Central  Google Scholar 

  5. Grajales D, Le W, Dallaire F, Sheehy G, David S, Tran T, Leblond F, Ménard C, Kadoury S (2023) Towards real-time confirmation of breast cancer in the OR using CNN-based Raman spectroscopy. Cancer Prev Through Early Detect 2:17–28

    Article  Google Scholar 

  6. Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman DJ (2021) 1d convolutional neural networks and applications: a survey. Mech Syst Signal Process 151:107398

    Article  Google Scholar 

  7. Lemoine É, Dallaire F, Yadav R, Agarwal R, Kadoury S, Trudel D, Guiot MC, Petrecca K, Leblond F (2019) Feature engineering applied to intraoperative in-vivo Raman spectroscopy sheds light on molecular processes in brain cancer: a retrospective study of 65 patients. Analyst 144(22):6517–6532

    Article  CAS  PubMed  Google Scholar 

  8. Lester SC, Bose S, Chen YY, Connolly JL, de Baca ME, Fitzgibbons PL, Hayes DF, Kleer C, O’Malley FP, Page DL, Smith BL, Tan LK, Weaver DL, Winer E (2009) Protocol for the examination of specimens from patients with invasive carcinoma of the breast. Arch Pathol Lab Med 133(10):1515–1538

    Article  PubMed  Google Scholar 

  9. Lou KL, Wang PY, Yang RQ, Gao YY, Tian HN, Dang YY, Li Y, Huang WH, Chen M, Liu XL, Zhang GJ (2022) Fabrication of tumor targeting rare-earth nanocrystals for real-time NIR-IIb fluorescence imaging-guided breast cancer precise surgery. Nanomed Nanotechnol Biol Med 43:102555

    Article  CAS  Google Scholar 

  10. Ma D, Shang L, Tang J, Bao Y, Fu J, Yin J (2021) Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network. Spectrochim Acta Part A Mol Biomol Spectrosc 256

  11. Nunez A, Jones V, Schulz-Costello K, Schmolze D (2020) Accuracy of gross intraoperative margin assessment for breast cancer: experience since the SSO-ASTRO margin consensus guidelines. Sci Rep 10(1):17344

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Parent F, Gérard M, Monet F, Loranger S, Soulez G, Kashyap R, Kadoury S (2018) Intra-arterial image guidance with optical frequency domain reflectometry shape sensing. IEEE Trans Med Imaging 38(2):482–492

    Article  PubMed  Google Scholar 

  13. Phelps DL, Balog J, Gildea LF, Bodai Z, Savage A, El-Bahrawy MA, Speller AV, Rosini F, Kudo H, McKenzie JS, Brown R, Takáts Z, Ghaem-Maghami S (2018) The surgical intelligent knife distinguishes normal, borderline and malignant gynaecological tissues using rapid evaporative ionisation mass spectrometry (REIMS). Br J Cancer 118(10):1349–1358

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Pinto M, Zorn KC, Tremblay JP, Desroches J, Dallaire F, Aubertin K, Marple E, Kent C, Leblond F, Trudel D, Lesage F (2019) Integration of a Raman spectroscopy system to a robotic-assisted surgical system for real-time tissue characterization during radical prostatectomy procedures. J Biomed Opt 24(2):025001–025001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Qi Y, Chen EX, Hu D, Yang Y, Wu Z, Zheng M, Sadi MA, Jiang Y, Zhang K, Chen Z, Chen YP (2023) Applications of Raman spectroscopy in clinical medicine. Preprint arXiv:2304.07740

  16. Santilli AM, Jamzad A, Janssen NN, Kaufmann M, Connolly L, Vanderbeck K, Wang A, McKay D, Rudan JF, Fichtinger G, Mousavi P (2020) Perioperative margin detection in basal cell carcinoma using a deep learning framework. Int J Comput Assisted Radiol Surg 15(5):887–896

    Article  Google Scholar 

  17. Schleusener J, Lademann J, Darvin ME (2017) Depth-dependent autofluorescence photobleaching using 325, 473, 633, and 785nm of porcine ear skin ex-vivo. J Biomed Opt 22(9):091503

    Article  Google Scholar 

  18. Sheehy G, Picot F, Dallaire F, Ember K, Nguyen T, Petrecca K, Trudel D, Leblond F (2023) Open-sourced Raman spectroscopy data processing package implementing a novel baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids. J Biomed Opt 28(February):1–20

    Google Scholar 

  19. Sun Y, Kadoury S, Li Y, John M, Resnick J, Plambeck G, Liao R, Sauer F, Xu C (2007) Image guidance of intracardiac ultrasound with fusion of pre-operative images. In: Medical image computing and computer-assisted intervention–MICCAI 2007: 10th international conference, Brisbane, Proceedings, Part I 10, Springer, pp 60–67

  20. van Breugel SJ, Low I, Christie ML, Pokorny MR, Nagarajan R, Holtkamp HU, Srinivasa K, Amirapu S, Nieuwoudt MK, Simpson MC, Zargar-Shoshtari K, Aguergaray C (2023) Raman spectroscopy system for real-time diagnosis of clinically significant prostate cancer tissue. J Biophotonics 16(5):e202200334

  21. Wang X, Tian S, Yu L, Lv X, Zhang Z (2020) Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network. Lasers Med Sci 35

  22. Zhang L, Li C, Peng D, Yi X, He S, Liu F, Zheng X, Huang WE, Zhao L, Huang X (2022) Raman spectroscopy and machine learning for the classification of breast cancers. Spectrochim Acta Part A Mol Biomol Spectrosc 264

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Acknowledgements

This research was undertaken thanks, in part, to funding from the Canada First Research Excellence Fund through the TransMedTech Institute.

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Authors and Affiliations

  1. Polytechnique Montréal, Montréal, QC, Canada

    David Grajales, William T. Le, Trang Tran, Sandryne David, Frédérick Dallaire, Katherine Ember, Frédéric Leblond & Samuel Kadoury

  2. Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montréal, QC, Canada

    David Grajales, William T. Le, Trang Tran, Sandryne David, Frédérick Dallaire, Katherine Ember, Frédéric Leblond, Cynthia Ménard & Samuel Kadoury

  3. Institut du Cancer de Montréal, Montréal, QC, Canada

    Frédéric Leblond

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  1. David Grajales
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Correspondence to David Grajales.

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Conflict of interest

F. Leblond is co-founder of ODS Medical (now Reveal Surgical) formed in 2015 to commercialize a Raman spectroscopy system for neurosurgical and prostate surgery applications. The other authors declare no conflicts of interest.

Ethics approval

All procedures performed in studies involving human participants, from which data were used in the present research, were in accordance with the ethical standards of the institutional and/or national research committee: (ODS Sentry System-1000/2019-5313), (HS#: STUDY-20-01371), (EAN 2021-5997), (NCT03378856).

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Grajales, D., Le, W.T., Tran, T. et al. Robot-assisted biopsy sampling for online Raman spectroscopy cancer confirmation in the operating room. Int J CARS 19, 1103–1111 (2024). https://doi.org/10.1007/s11548-024-03100-7

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  • DOI: https://doi.org/10.1007/s11548-024-03100-7

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