Abstract
Intra-operative (this work was partially supported by Disruptive Technologies Innovation Fund, Ireland, project code DTIF2018 240 CA) identification of malignant versus benign or healthy tissue is a major challenge in fluorescence guided cancer surgery. We propose a perfusion quantification method for computer-aided interpretation of subtle differences in dynamic perfusion patterns which can be used to distinguish between normal tissue and benign or malignant tumors intra-operatively by using multispectral endoscopic videos. The method exploits the fact that vasculature arising from cancer angiogenesis gives tumors differing perfusion patterns from the surrounding normal tissues. Experimental evaluation of our method on a cohort of colorectal cancer surgery endoscopic videos suggests that it discriminates between healthy, cancerous and benign tissues with 95% accuracy.
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Notes
- 1.
Perfusion is the passage of fluid through the circulatory or lymphatic system to a capillary bed in tissue.
References
Benson, R.C., Kues, H.A.: Fluorescence properties of indocyanine green as related to angiography. Phys. Med. Biol. 23(1), 159–163 (1978). https://doi.org/10.1088/0031-9155/23/1/017
Boni, L., David, G., Dionigi, G., Rausei, S., Cassinotti, E., Fingerhut, A.: Indocyanine green-enhanced fluorescence to assess bowel perfusion during laparoscopic colorectal resection. Surg. Endosc. 30(7), 2736–2742 (2015). https://doi.org/10.1007/s00464-015-4540-z
Choi, M., Choi, K., et al.: Dynamic fluorescence imaging for multiparametric measurement of tumor vasculature. J. Biomed. Optics 16(4), 046008 (2011). https://doi.org/10.1117/1.3562956
De Palma, M., Biziato, D., Petrova, T.V.: Microenvironmental regulation of tumour angiogenesis. Nat. Rev. Cancer 17(8), 457 (2017). https://doi.org/10.1038/nrc.2017.51
Diana, M., et al.: Enhanced-reality video fluorescence: a real-time assessment of intestinal viability. Ann. Surg. 259(4), 700–707 (2014). https://doi.org/10.1097/SLA.0b013e31828d4ab3
Gurfinkel, M., et al.: Pharmacokinetics of icg and hpph-car for the detection of normal and tumor tissue using fluorescence, near-infrared reflectance imaging: a case study. Photochem. Photobiol. 72(1), 94–102 (2000). https://doi.org/10.1562/0031-8655(2000)072
Holt, D., et al.: Intraoperative near-infrared imaging can distinguish cancer from normal tissue but not inflammation. PLOS ONE. 9(7), e103342 (2014). https://doi.org/10.1371/journal.pone.0103342
Huh, Y.J., et al.: Efficacy of assessing intraoperative bowel perfusion with near-infrared camera in laparoscopic gastric cancer surgery. J. Laparoendosc. Adv. Surg. Tech. 29(4), 476–483 (2019). https://doi.org/10.1089/lap.2018.0263
Jayender, J., et al.: Statistical learning algorithm for in situ and invasive breast carcinoma segmentation. Comput. Med. Imaging Graph 37(4), 281–292 (2013). https://doi.org/10.1016/j.compmedimag.2013.04.003
Jones, E., et al.: SciPy: open source scientific tools for Python, (2001) http://www.scipy.org/
McKinney, S.M., et al.: International evaluation of an ai system for breast cancer screening. Nat. 577(7788), 89–94 (2020). https://doi.org/10.1038/s41586-019-1799-6
Nishida, N., et al.: Angiogenesis in cancer. Vasc. Health Risk Manage. 2(3), 213 (2006). https://doi.org/10.2147/vhrm.2006.2.3.213
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011). https://doi.org/10.5555/1953048.2078195
Phillips, C.L., et al.: Feedback Control Systems. Prentice Hall, 4 ed, (2000)
Schaafsma, B.E., et al.: The clinical use of indocyanine green as a near-infrared fluorescent contrast agent for image-guided oncologic surgery. J.Surg. Oncol. 104(3), 323–332 (2011). https://doi.org/10.1002/jso.21943
Selka, F., et al.: Fluorescence-based enhanced reality for colorectal endoscopic surgery. In: Ourselin, S., Modat, M. (eds.) WBIR 2014. LNCS, vol. 8545, pp. 114–123. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08554-8_12
Shapcott, C.M., Rajpoot, N., Hewitt, K.: Deep learning with sampling for colon cancer histology images. Front. Bioeng. Biotech. 7, 52 (2019). https://doi.org/10.3389/fbioe.2019.00052
Son, G.M., Kwon, M.S., Kim, Y., Kim, J., Kim, S.H., Lee, J.W.: Quantitative analysis of colon perfusion pattern using indocyanine green (ICG) angiography in laparoscopic colorectal surgery. Surg. Endosc. 33(5), 1640–1649 (2018). https://doi.org/10.1007/s00464-018-6439-y
Veys, I., et al.: Icg-fluorescence imaging for detection of peritoneal metastases and residual tumoral scars in locally advanced ovarian cancer: a pilot study. J. Surg. Oncol. 117(2), 228–235 (2018). https://doi.org/10.1002/jso.24807
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Zhuk, S. et al. (2020). Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_68
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