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Classification of oral salivary gland tumors based on texture features in optical coherence tomography images

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Abstract

Currently, the diagnoses of oral diseases primarily depend on the visual recognition of experienced clinicians. It has been proven that automatic recognition based on images can support clinical decision-making by extracting and analyzing objective hidden information. In recent years, optical coherence tomography (OCT) has become a powerful optical imaging technique with the advantages of high resolution and non-invasion. In our study, a dataset composed of four kinds of oral salivary gland tumors (SGTs) was obtained from a homemade swept-source OCT, including two benign and two malignant tumors. Seventy-six texture features were extracted from OCT images to create computational models of diseases. It was demonstrated that the artificial neural network (ANN) based on principal component analysis (PCA) can obtain high diagnostic sensitivity and specificity (higher than 99%) for these four kinds of tumors. The classification accuracy of each tumor is larger than 99%. In addition, the performances of two classifiers (ANN and support vector machine) were quantitatively evaluated based on SGTs. It was proven that the texture features in OCT images provided objective information to classify oral tumors.

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Acknowledgements

This study has been funded by the National Natural Science Foundation of China (61875092), Science and Technology Support Program of Tianjin (17YFZCSY00740), and the Beijing-Tianjin-Hebei Basic Research Cooperation Special Program (19JCZDJC65300).

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Authors and Affiliations

  1. Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology, Institute of Modern Optics, Nankai University, 38 Tongyan Road, Tianjin, 300350, China

    Zihan Yang & Yanmei Liang

  2. Department of Oral Pathology, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin, 300041, China

    Jianwei Shang

  3. Department of Oral Medicine, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin, 300041, China

    Chenlu Liu

  4. Department of Oral-Maxillofacial Surgery, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin, 300041, China

    Jun Zhang

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  1. Zihan Yang
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  4. Jun Zhang
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  5. Yanmei Liang
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Correspondence to Yanmei Liang.

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All procedures performed in this study involving human participants were in accordance with the ethical standards of the Ethics Committee of Tianjin Stomatological Hospital.

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Yang, Z., Shang, J., Liu, C. et al. Classification of oral salivary gland tumors based on texture features in optical coherence tomography images. Lasers Med Sci 37, 1139–1146 (2022). https://doi.org/10.1007/s10103-021-03365-3

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  • DOI: https://doi.org/10.1007/s10103-021-03365-3

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