Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study

Catherine St. Pierre M.D.; Wendy-Julie Madore; Étienne De Montigny; Dominique Trudel M.D.; Caroline Boudoux; Nicolas Godbout; Anne-Marie Mes-Masson; Kurosh Rahimi M.D.; Frédéric Leblond

doi:10.1117/1.JMI.4.4.041306

12 October 2017 Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study

Catherine St. Pierre M.D., Wendy-Julie Madore, Étienne De Montigny, Dominique Trudel M.D., Caroline Boudoux, Nicolas Godbout, Anne-Marie Mes-Masson, Kurosh Rahimi M.D., Frédéric Leblond

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 4, 041306 (October 2017). https://doi.org/10.1117/1.JMI.4.4.041306

Abstract

Optical coherence tomography (OCT) yields microscopic volumetric images representing tissue structures based on the contrast provided by elastic light scattering. Multipatient studies using OCT for detection of tissue abnormalities can lead to large datasets making quantitative and unbiased assessment of classification algorithms performance difficult without the availability of automated analytical schemes. We present a mathematical descriptor reducing the dimensionality of a classifier’s input data, while preserving essential volumetric features from reconstructed three-dimensional optical volumes. This descriptor is used as the input of classification algorithms allowing a detailed exploration of the features space leading to optimal and reliable classification models based on support vector machine techniques. Using imaging dataset of paraffin-embedded tissue samples from 38 ovarian cancer patients, we report accuracies for cancer detection <90% for binary classification between healthy fallopian tube and ovarian samples containing cancer cells. Furthermore, multiples classes of statistical models are presented demonstrating <70% accuracy for the detection of high-grade serous, endometroid, and clear cells cancers. The classification approach reduces the computational complexity and needed resources to achieve highly accurate classification, making it possible to contemplate other applications, including intraoperative surgical guidance, as well as other depth sectioning techniques for fresh tissue imaging.

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Catherine St. Pierre M.D., Wendy-Julie Madore, Étienne De Montigny, Dominique Trudel M.D., Caroline Boudoux, Nicolas Godbout, Anne-Marie Mes-Masson, Kurosh Rahimi M.D., and Frédéric Leblond "Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study," Journal of Medical Imaging 4(4), 041306 (12 October 2017). https://doi.org/10.1117/1.JMI.4.4.041306

Received: 15 May 2017; Accepted: 14 September 2017; Published: 12 October 2017

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