Automated identification of basal cell carcinoma by polarization-sensitive optical coherence tomography.

Lian Duan,Alex Lee,Jean Y Tang,Audrey K Ellerbee,Tahereh Marvdashti

doi:10.1364/boe.5.003717

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.

Highlights

Skin cancer, the most common form of cancer in the Western world, accounts for billions in annual healthcare costs [1,2,3]
Using the combined information from intensity and phase retardation, our automated classifier significantly outperforms previous works examining skin cancer based on Optical coherence tomography (OCT) alone and achieves a sensitivity and specificity of 94.4% and 92.5%, respectively
The calculated specificity and sensitivity are 92.5% and 94.4%, respectively, yielding an overall accuracy of 93.5% (Table 2). These values are significantly higher than those obtained using intensity or birefringence information alone: the specificity and sensitivity of a version of our SVM-based classifier based solely on intensity parameters were respectively 82.2% and 78.2%, which is comparable to that achieved using conventional OCT reported in literature; a classifier we generated based on phase retardation parameters alone did slightly better, at 85.0% and 87.9%

Summary

Introduction

The most common form of cancer in the Western world, accounts for billions in annual healthcare costs [1,2,3]. Several studies have suggested that OCT can perform non-invasive diagnosis of skin cancer, because it is able to visualize sub-dermal features associated with the first appearance of skin cancer [6,7,8,9,10,11]. Jørgensen et al demonstrated a machine learning-based method to diagnose and classify skin cancer with OCT [6]. While their OCT system revealed structural information derived from differences in scattering intensity for cancerous versus healthy tissue, OCT-based scattering contrast in their and others’ work had limited power to discriminate skin cancer, leading to a sensitivity and specificity below 80% [10, 12,13,14,15]

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Conclusion