Morphologic 3D scanning of fallopian tubes to assist ovarian cancer diagnosis

Madore, Wendy‐Julie; Montigny, Etienne De; Deschênes, Andréanne; Benboujja, Fouzi; Leduc, M; Mes-Masson, Anne-Marie; Provencher, Diane; Rahimi, Kurosh; Boudoux, Caroline; Godbout, Nicolas

doi:10.1117/12.2211204

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…A subset of the paraffin-embedded tissue samples dataset reported previously in Ref. 34 was selected from the three most common ovarian tumor types: high-grade serous tumors (HGS: 12 blocks), endometrioid tumors (En: 7 blocks), and clear cell tumors (CC: 7 blocks). Samples from healthy fallopian Table 1 Numbers of patients and OCT regions for each class constituting the dataset.…”

Section: Patients and Optical Coherence Tomography Datasetmentioning

confidence: 99%

“…Small OCT probes have been developed specifically tailored to fallopian tube analysis. 34,43 For ovaries analysis, as well as the exterior of the fimbriated end of the tubes, a widefield scanning head, similar to the one used in this study, may be more appropriate. In the surgical workflow, a handheld probe may be more appropriate, but for a screening tool, a flexible mm-sized probe should be envisioned.…”

Section: Four-class Problemmentioning

confidence: 99%

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

et al. 2017

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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 [Formula: see text] for binary classification between healthy fallopian tube and ovarian samples containing cancer cells. Furthermore, multiples classes of statistical models are presented demonstrating [Formula: see text] 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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Section: Patients and Optical Coherence Tomography Datasetmentioning

confidence: 99%

Section: Four-class Problemmentioning

confidence: 99%

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

et al. 2017

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“…The fiber probe design was validated and assembled based on the specifications obtained from optical simulations (OSLO, Lambda Research, Massachusetts). The probe optical characteristics obtained from the simulations were then measured and compared to the previous images acquired on animal models 52 and ex vivo human fallopian tubes. In agreement with CHUM protocol #14.159, imaging was performed on five patients undergoing a complete hysterectomy with bilateral salpingo-oophorectomy next to the operating room theater.…”

Section: Intraluminal Imagingmentioning

confidence: 99%

Morphologic three-dimensional scanning of fallopian tubes to assist ovarian cancer diagnosis

et al. 2017

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The majority of high-grade serous ovarian cancers is now believed to originate in the fallopian tubes. Therefore, current practices include the pathological examination of excised fallopian tubes. Detection of tumors in the fallopian tubes using current clinical approaches remains difficult but is of critical importance to achieve accurate staging and diagnosis. Here, we present an intraoperative imaging system for the detection of human fallopian tube lesions. The system is based on optical coherence tomography (OCT) to access subepithelial tissue architecture. To demonstrate that OCT could identify lesions, we analyzed 180 OCT volumes taken from five different ovarian lesions and from healthy fallopian tubes, and compared them to standard pathological review. We demonstrated that qualitative features could be matched to pathological conditions. We then determined the feasibility of intraluminal imaging of intact human fallopian tubes by building a dedicated endoscopic single-fiber OCT probe to access the mucosal layer inside freshly excised specimens from five patients undergoing prophylactic surgeries. The probe insertion into the lumen acquired images over the entire length of the tubes without damaging the mucosa, providing the first OCT images of intact human fallopian tubes.

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Morphologic 3D scanning of fallopian tubes to assist ovarian cancer diagnosis

Cited by 2 publications

References 12 publications

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

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

Morphologic three-dimensional scanning of fallopian tubes to assist ovarian cancer diagnosis

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