Top

Published in:

01-03-2022 | Original Article

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

Authors: Zihan Yang, Jianwei Shang, Chenlu Liu, Jun Zhang, Yanmei Liang

Published in: Lasers in Medical Science | Issue 2/2022

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.

Skalova A, Stenman G, Simpson RHW et al (2018) The role of molecular testing in the differential diagnosis of salivary gland carcinomas. Am J Surg Pathol 42(2):e11–e27. https://doi.org/10.1097/PAS.0000000000000980CrossRefPubMed

Palasz P, Adamski L, Gorska-Chrzastek M, Starzynska A, Studniarek M (2017) Contemporary diagnostic imaging of oral squamous cell carcinoma - a review of literature. Pol J Radiol 7(82):193–202. https://doi.org/10.12659/PJR.900892CrossRef

Li Q, Zhang X, Liu X et al (2012) Long-term treatment outcome of minor salivary gland carcinoma of the hard palate. Oral Oncol 48(5):456–462. https://doi.org/10.1016/j.oraloncology.2011.12.005CrossRefPubMed

Heller KS, Dubner S, Chess Q, Attie JN (1993) Value of fine needle aspiration biopsy of salivary gland mass in clinical decision making. Am J Surg 164(6):667–670. https://doi.org/10.1016/S0002-9610(05)80731-7CrossRef

Inohara H, Akahani S, Yamamoto Y et al (2008) The role of fine-needle aspiration cytology and magnetic resonance imaging in the management of parotid mass lesions. Acta Otolaryngol 128:1152–1158. https://doi.org/10.1080/00016480701827533CrossRefPubMed

Limongelli L, Capodiferro S, Tempesta A et al (2020) Early tongue carcinomas (clinical stage I and II): echo-guided three-dimensional diode laser mini-invasive surgery with evaluation of histological prognostic parameters. A study of 85 cases with prolonged follow-up. Lasers Med Sci 35(3):751–758. https://doi.org/10.1007/s10103-019-02932-zCrossRefPubMed

Kumar P, Kanaujia SK, Singh A, Pradhan A (2019) In vivo detection of oral precancer using a fluorescence-based, in-house-fabricated device: a Mahalanobis distance-based classification. Lasers Med Sci 34:1243–1251. https://doi.org/10.1007/s10103-019-02720-9CrossRefPubMed

Anschau F, Webster J, Capra MEZ, de Silva ALFD, Stein AT (2019) Efficacy of low-level laser for treatment of cancer oral mucositis: a systematic review and meta-analysis. Lasers Med Sci 34:1053–1062. https://doi.org/10.1007/s10103-019-02722-7CrossRefPubMed

Koch M, Schapher M, Goncalves M, Iro H, Mantsopoulos K (2020) Simultaneous application of ultrasound and sialendoscopy: experience in the management of stenosis and other non-sialolithiasis-related salivary gland disorders. Eur Rev Med Pharmaco 24(5):2196–2204. https://doi.org/10.26355/eurrev_202003_20485CrossRef

10.

Razek A (2019) Multi-parametric MR imaging using pseudo-continuous arterial-spin labeling and diffusion-weighted MR imaging in differentiating subtypes of parotid tumors. Magn Reson Imaging 63:55–59. https://doi.org/10.1016/j.mri.2019.08.005CrossRefPubMed

11.

Wu W, Wang C, Li D, Luo J, Ye J, Xu S (2019) Multiphase contrast-enhanced computed tomography imaging features of salivary duct carcinoma: differentiation from other salivary gland malignancies. Oral Surg Oral Med Oral Pathol Oral Radiol 128(5):543–551. https://doi.org/10.1016/j.oooo.2019.05.011CrossRefPubMed

12.

Fakhry N, Antonini F, Michel J et al (2012) Fine-needle aspiration cytology in the management of parotid masses: evaluation of 249 patients. Eur Ann Otorhinolaryngol Head Neck Dis 129(3):131–135. https://doi.org/10.1016/j.anorl.2011.10.008CrossRefPubMed

13.

Alnawaiseh M, Schubert F, Heiduschka P, Eter N (2019) Optical coherence tomography angiography in patients with retinitis pigmentosa. Retina 39(1):210–217. https://doi.org/10.1097/IAE.000000000000190CrossRefPubMed

14.

Turani Z, Fatemizadeh E, Blumetti T et al (2019) Optical radiomic signatures derived from optical coherence tomography images to improve identification of melanoma. Cancer Res 79(8):2021–2030. https://doi.org/10.1158/0008-5472.CAN-18-2791CrossRefPubMedPubMedCentral

15.

Jerjes W, Hamdoon Z, Hopper C (2019) Structural validation of facial skin using optical coherence tomography: a descriptive study. Skin Res Technol 00:1–10. https://doi.org/10.1111/srt.12791CrossRef

16.

Yonetsu T, Bouma BE, Kato K, Fujimoto JG, Jang IK (2013) Optical coherence tomography - 15 years in cardiology. Circ J 77(8):1933–1940. https://doi.org/10.1253/circj.cj-13-0643.1CrossRefPubMed

17.

Gan Y, Tsay D, Amir SB, Marboe CC, Hendon CP (2016) Automated classification of optical coherence tomography images of human atrial tissue. J Biomed Opt 21(10):101407CrossRef

18.

Kut C, Chaichana KL, Xi J et al (2015) Detection of human brain cancer infiltration ex vivo and in vivo using quantitative optical coherence tomography. Sci Transl Med 7(292):292ra100. https://doi.org/10.1126/scitranslmed.3010611CrossRefPubMedPubMedCentral

19.

Erickson-Bhatt SJ, Mesa KJ, Marjanovic M, Chaney EJ, Ahmad A, Huang P-C, Liu ZG, Cunningham K, Boppart SA (2018) Intraoperative optical coherence tomography of the human thyroid: feasibility for surgical assessment. Transl Res 195:13–24. https://doi.org/10.1016/j.trsl.2017.12.001CrossRefPubMed

20.

Yang Z, Shang J, Liu C, Zhang J, Liang Y (2020) Identification of oral cancer in OCT images based on an optical attenuation model. Lasers Med Sci 35:1999–2007. https://doi.org/10.1007/s10103-020-03025-yCrossRefPubMed

21.

Hamdoon Z, Jerjes W, Upile T, McKenzie G, Jay A, Hopper C (2013) Optical coherence tomography in the assessment of suspicious oral lesions: an immediate ex vivo study. Photodiagn Photodyn 10(1):17–27. https://doi.org/10.1016/j.pdpdt.2012.07.005CrossRef

22.

Walther J, Golde J, Kirsten L et al (2017) In vivo imaging of human oral hard and soft tissues by polarization-sensitive optical coherence tomography. J Biomed Opt 22(12):121717. https://doi.org/10.1117/1.JBO.22.12.121717CrossRef

23.

Wang J, Zheng W, Lin K, Huang Z (2018) Characterizing biochemical and morphological variations of clinically relevant anatomical locations of oral tissue in vivo with hybrid Raman spectroscopy and optical coherence tomography technique. J Biophotonics 11(3):e201700113. https://doi.org/10.1002/jbio.201700113CrossRef

24.

Yang Z, Shang J, Liu C, Zhang J, Hou F, Liang Y (2020) Intraoperative imaging of oral-maxillofacial lesions using optical coherence tomography. J Innov Opt Heal Sci 13(2):2050010. https://doi.org/10.1142/s1793545820500108CrossRef

25.

Li K, Yang Z, Liang W, Shang J, Liang Y, Wan S (2020) Low-cost, ultracompact handheld optical coherence tomography probe for in vivo oral maxillofacial tissue imaging. J Biomed Opt 25(4):046003. https://doi.org/10.1117/1.JBO.25.4.046003CrossRefPubMedCentral

26.

Koprowski R, Teper S, Wróbel Z, Wylegala E (2013) Automatic analysis of selected choroidal diseases in OCT images of the eye fundus. Biomed Eng OnLine 12(1):117. https://doi.org/10.1186/1475-925X-12-117CrossRefPubMedPubMedCentral

27.

Fu D, Tong H, Zheng S, Luo L, Gao F, Minar J (2016) Retinal status analysis method based on feature extraction and quantitative grading in OCT images. Biomed Eng Online 15:87. https://doi.org/10.1186/s12938-016-0206-xCrossRefPubMedPubMedCentral

28.

Alsaih K, Lemaitre G, Rastgoo M, Massich J, Sidibe D, Meriaudeau F (2017) Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. Biomed Eng Online 16:68. https://doi.org/10.1186/s12938-017-0352-9CrossRefPubMedPubMedCentral

29.

Wan S, Lee HC, Huang X et al (2017) Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy. Med Image Anal 38:104–116. https://doi.org/10.1016/j.media.2017.03.002CrossRefPubMedPubMedCentral

30.

Adabi S, Hosseinzadeh M, Noei S et al (2017) Universal in vivo textural model for human skin based on optical coherence tomograms. Sci Rep 7(1):17912. https://doi.org/10.1038/s41598-017-17398-8CrossRefPubMedPubMedCentral

31.

Lenz M, Krug R, Dillmann C et al (2018) Automated differentiation between meningioma and healthy brain tissue based on optical coherence tomography ex vivo images using texture features. J Biomed Opt 23(7):071205. https://doi.org/10.1117/1.JBO.23.7.071205CrossRef

32.

Hou F, Yu Y, Liang Y (2017) Automatic identification of parathyroid in optical coherence tomography images. Lasers Surg Med 49(3):305–311. https://doi.org/10.1002/lsm.22622CrossRefPubMed

33.

Bian D, Tao K, Yuan Z, Kuang H, Liu Z, Liang Y (2020) Identification of atherosclerotic plaques in intravascular optical coherence tomography images based on textures and artificial neural network. Laser Phys 30(3):035602. https://doi.org/10.1088/1555-6611/ab7239CrossRef

34.

Ou X, Pan W, Xiao P (2014) In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). Int J Pharm 460:28–32. https://doi.org/10.1016/j.ijpharm.2013.10.024CrossRefPubMed

35.

Lingley-Papadopoulos CA, Loew MH, Manyak MJ, Zara JM (2008) Computer recognition of cancer in the urinary bladder using optical coherence tomography and texture analysis. J Biomed Opt 13(2):024003. https://doi.org/10.1117/1.2904987CrossRefPubMed

36.

Govindaraj P, Sudhakar MS (2017) Shape characterization using laws of texture energy measures facilitating retrieval. Imaging Sci J 66:98–105. https://doi.org/10.1080/13682199.2017.1380356CrossRef

37.

Qi X, Sivak MV Jr, Isenberg G, Willis JE, Rollins AM (2006) Computer-aided diagnosis of dysplasia in Barrett’s esophagus using multiple endoscopic OCT images. J Biomed Opt 11(4):044010. https://doi.org/10.1117/1.2337314CrossRefPubMed

38.

Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. Computer vision - ECCV pp 469–481. https://doi.org/10.1007/978-3-540-24670-1_36

39.

Keerthi Vasan K, Surendiran B (2016) Dimensionality reduction using principal component analysis for network intrusion detection. Perspectives in Science 8:510–512. https://doi.org/10.1016/j.pisc.2016.05.010CrossRef

Title: Classification of oral salivary gland tumors based on texture features in optical coherence tomography images
Authors: Zihan Yang
Jianwei Shang
Chenlu Liu
Jun Zhang
Yanmei Liang
Publication date: 01-03-2022
Publisher: Springer London
Published in: Lasers in Medical Science / Issue 2/2022
Print ISSN: 0268-8921
Electronic ISSN: 1435-604X
DOI: https://doi.org/10.1007/s10103-021-03365-3

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

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

Abstract

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 2/2022

Efficacy and safety of laser combination therapy and laser alone therapy for keloid: a systematic review and meta-analysis

Photobiomodulation impacts the levels of inflammatory mediators during orthodontic tooth movement? A systematic review with meta-analysis

Treatment of partial injury of the calcaneus tendon with heterologous fibrin biopolymer and/or photobiomodulation in rats

Photobiomodulation enhances facial nerve regeneration via activation of PI3K/Akt signaling pathway–mediated antioxidant response

Efficacy of 5-aminolevulinic acid-based photodynamic therapy in different subtypes of canine mammary gland cancer cells

Effect of photobiomodulation therapy on trunk flexor performance after incisional hernia repair: a randomized controlled trial