Automated Identification of Basal Cell Carcinoma by Polarization-sensitive Optical Coherence Tomography

Overview

Journal Biomed Opt Express

Specialty Radiology

Date 2014 Nov 1

PMID 25360384

Citations 14

Authors

Lian Duan

Tahereh Marvdashti

Alex Lee

Jean Y Tang

Audrey K Ellerbee

Affiliations

Soon will be listed here.

Abstract

We report an automated classifier to detect the presence of basal cell carcinoma in images of mouse skin tissue samples acquired by polarization-sensitive optical coherence tomography (PS-OCT). The sensitivity and specificity of the classifier based on combined information of the scattering intensity and birefringence properties of the samples are significantly higher than when intensity or birefringence information are used alone. The combined information offers a sensitivity of 94.4% and specificity of 92.5%, compared to 78.2% and 82.2% for intensity-only information and 85.5% and 87.9% for birefringence-only information. These results demonstrate that analysis of the combination of complementary optical information obtained by PS-OCT has great potential for accurate skin cancer diagnosis.

Citing Articles

Polarization-sensitive optical coherence tomography for renal tumor detection in human kidneys.

Yan F, Wang C, Yan Y, Zhang Q, Yu Z, Patel S Opt Lasers Eng. 2023; 173.

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Semisupervised representative learning for measuring epidermal thickness in human subjects in optical coherence tomography by leveraging datasets from rodent models.

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Birefringent tissue-mimicking phantom for polarization-sensitive optical coherence tomography imaging.

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Investigating the depolarization property of skin tissue by degree of polarization uniformity contrast using polarization-sensitive optical coherence tomography.

Zhou X, Maloufi S, Louie D, Zhang N, Liu Q, Lee T Biomed Opt Express. 2021; 12(8):5073-5088.

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Optical coherence angiography for pre-treatment assessment and treatment monitoring following photodynamic therapy: a basal cell carcinoma patient study.

Gubarkova E, Feldchtein F, Zagaynova E, Gamayunov S, Sirotkina M, Sedova E Sci Rep. 2019; 9(1):18670.

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