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Elucidation of Differential Nano-Textural Attributes for Normal Oral Mucosa and Pre-Cancer

Published online by Cambridge University Press:  17 September 2019

Debaleena Nawn ,
Saunak Chatterjee ,
Anji Anura ,
Swarnendu Bag ,
Debjani Chakraborty ,
Mousumi Pal ,
Ranjan Rashmi Paul  and
Jyotirmoy Chatterjee
Debaleena Nawn*
Affiliation:
Advanced Technology Development Centre, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
Saunak Chatterjee
Affiliation:
School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
Anji Anura
Affiliation:
School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
Swarnendu Bag
Affiliation:
Tata Medical Center, Kolkata 700160, West Bengal, India
Debjani Chakraborty
Affiliation:
Department of Mathematics, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
Mousumi Pal
Affiliation:
Guru Nanak Institute of Dental Sciences and Research, Kolkata 700114, West Bengal, India
Ranjan Rashmi Paul
Affiliation:
Guru Nanak Institute of Dental Sciences and Research, Kolkata 700114, West Bengal, India
Jyotirmoy Chatterjee
Affiliation:
School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
*
*Author for correspondence: Debaleena Nawn, E-mail: debaleena.nawn@gmail.com
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Abstract

Computational analysis on altered micro-nano-textural attributes of the oral mucosa may provide precise diagnostic information about oral potentially malignant disorders (OPMDs) instead of an existing handful of qualitative reports. This study evaluated micro-nano-textural features of oral epithelium from scanning electron microscopic (SEM) images and the sub-epithelial connective tissue from light microscopic (LM) and atomic force microscopic (AFM) images for normal and OPMD (namely oral sub-mucous fibrosis, i.e., OSF). Objective textural descriptors, namely discrete wavelet transform, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP), were extracted and fed to standard classifiers. Best classification accuracy of 87.28 and 93.21%; sensitivity of 93 and 96%; specificity of 80 and 91% were achieved, respectively, for SEM and AFM. In the study groups, SEM analysis showed a significant (p < 0.01) variation for all the considered textural descriptors, while for AFM, a remarkable alteration (p < 0.01) was only found in GLCM and LBP. Interestingly, sub-epithelial collagen nanoscale and microscale textural information from AFM and LM images, respectively, were complementary, namely microlevel contrast was more in normal (0.251) than OSF (0.193), while nanolevel contrast was more in OSF (0.283) than normal (0.204). This work, thus, illustrated differential micro-nano-textural attributes for oral epithelium and sub-epithelium to distinguish OPMD precisely and may be contributory in early cancer diagnostics.

Keywords

atomic force microscopyclassification accuracymicro-nano-textural attributesoral sub-mucous fibrosisscanning electron microscopy
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 25 , Issue 5 , October 2019 , pp. 1224 - 1233
Copyright
Copyright © Microscopy Society of America 2019 

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References

Al-Bahlani, S, Al-Dhahli, B, Al-Adawi, K, Al-Nabhani, A & Al-Kindi, M (2017). Platinum-based drugs differentially affect the ultrastructure of breast cancer cell types. BioMed Res Int 2017, Article ID 3178794.Google Scholar
Ali, H, Sharif, M, Yasmin, M & Rehmani, MH (2017). Computer-based classification of chromoendoscopy images using homogeneous texture descriptors. Comp Biol Med 88, 84–92.Google Scholar
Al-Tikriti, MS & Henry, RW (2015). Ultrastructure of developing feline nonciliated bronchiolar epithelial cells. Ultrastruct Pathol 39, 245–254.Google Scholar
Anura, A, Das, D, Pal, M, Paul, RR, Das, S & Chatterjee, J (2017). Nanomechanical signatures of oral submucous fibrosis in sub-epithelial connective tissue. J Mech Behav Biomed Mater 65, 705–715.Google Scholar
Asikainen, PJ, Dekker, H, Sirviö, E, Mikkonen, J, Schulten, EA, Bloemena, E, Koistinen, A, ten Bruggenkate, CM & Kullaa, AM (2017). Radiation induced changes in the microstructure of epithelial cells of the oral mucosa: A comparative light and electron microscopic study. J Oral Pathol Med 46, 1004–1010.Google Scholar
Bag, S, Pal, M, Chaudhary, A, Das, RK, Paul, RR, Sengupta, S & Chatterjee, J (2015). Connecting cyto-nano-architectural attributes and epithelial molecular expression in oral submucous fibrosis progression to cancer. J Clin Pathol 68, 605–613.Google Scholar
Bánóczy, J (2012). Oral Leukoplakia, vol. 8. Springer Science & Business Media.Google Scholar
Bari, S, Metgud, R, Vyas, Z & Tak, A (2017). An update on studies on etiological factors, disease progression, and malignant transformation in oral submucous fibrosis. J Cancer Res Ther 13, 399.Google Scholar
Beura, S, Majhi, B & Dash, R (2015). Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing 154, 1–14.Google Scholar
Brauchle, E, Kasper, J, Daum, R, Schierbaum, N, Falch, C, Kirschniak, A, Schäffer, TE & Schenke-Layland, K (2018). Biomechanical and biomolecular characterization of extracellular matrix structures in human colon carcinomas. Matrix Biol 68, 180–193.Google Scholar
Chen, B, Chen, ZW, Wang, GJ & Xie, WP (2014). Damage detection on sudden stiffness reduction based on discrete wavelet transform. Sci World J 2014, Article ID 807620, 16 pages.Google Scholar
Chun-Lin, L (2010). A Tutorial of the Wavelet Transform. Taiwan: NTUEE.Google Scholar
Damerjian, V, Tankyevych, O, Guellich, A, Damy, T & Petit, E (2016). Ultrasound image texture characterization with Gabor wavelets for cardiac hypertrophy differentiation. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 49–52. IEEE, Prague, Czech Republic.Google Scholar
Das, S & Jena, UR (2016). Texture classification using combination of LBP and GLRLM features along with KNN and multiclass SVM classification. In 2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS), pp. 115–119. IEEE, Mathura, India.Google Scholar
Deisboeck, TS, Wang, Z, Macklin, P & Cristini, V (2011). Multiscale cancer modeling. Annu Rev Biomed Eng 13, 127–155.Google Scholar
Deng, X, Xiong, F, Li, X, Xiang, B, Li, Z, Wu, X, Guo, C, Li, X, Li, Y, Li, G & Xiong, W (2018). Application of atomic force microscopy in cancer research. J Nanobiotechnol 16, 102.Google Scholar
Dionne, KR, Warnakulasuriya, S, BintiZain, R & Cheong, SC (2015). Potentially malignant disorders of the oral cavity: Current practice and future directions in the clinic and laboratory. Int J Cancer 136, 503–515.Google Scholar
Ekanayaka, RP & Tilakaratne, WM (2016). Oral submucous fibrosis: Review on mechanisms of malignant transformation. Oral Surg Oral Med Oral Pathol Oral Radiol 122, 192–199.Google Scholar
Fernandez-Lozano, C, Seoane, JA, Gestal, M, Gaunt, TR, Dorado, J & Campbell, C (2015). Texture classification using feature selection and kernel-based techniques. Soft Comput 19, 2469–2480.Google Scholar
Ganganna, K, Shetty, P & Shroff, SE (2012). Collagen in histologic stages of oral submucous fibrosis: A polarizing microscopic study. J Oral Maxillofac Pathol 16, 162.Google Scholar
Gautier, HO, Thompson, AJ, Achouri, S, Koser, DE, Holtzmann, K, Moeendarbary, E & Franze, K (2015). Atomic force microscopy-based force measurements on animal cells and tissues. In Methods in Cell Biology, vol. 125, pp. 211–235. Academic Press.Google Scholar
Graps, A (1995). An introduction to wavelets. IEEE Comput Sci Eng 2, 50–61.Google Scholar
Gu, X, Cheng, M & Herrera, GA (2018). Kidney carcinoid tumor: Histological, immunohistochemical and ultrastructural features. Ultrastruct Pathol 42, 18–22.Google Scholar
Hogeweg, L, Sánchez, CI, Maduskar, P, Philipsen, R, Story, A, Dawson, R, Theron, G, Dheda, K, Peters-Bax, L & Van Ginneken, B (2015). Automatic detection of tuberculosis in chest radiographs using a combination of textural, focal, and shape abnormality analysis. IEEE Trans Med Imaging 34, 2429–2442.Google Scholar
Liu, Y, Zhu, X, Huang, Z, Cai, J, Chen, R, Xiong, S, Chen, G & Zeng, H (2015). Texture analysis of collagen second-harmonic generation images based on local difference local binary pattern and wavelets differentiates human skin abnormal scars from normal scars. J Biomed Opt 20, 016021.Google Scholar
Lu, P, Weaver, VM & Werb, Z (2012). The extracellular matrix: A dynamic niche in cancer progression. J Cell Biol 196, 395–406.Google Scholar
Mala, K, Sadasivam, V & Alagappan, S (2015). Neural network based texture analysis of CT images for fatty and cirrhosis liver classification. Appl Soft Comput 32, 80–86.Google Scholar
Midya, A & Chakraborty, J (2015). Classification of benign and malignant masses in mammograms using multi-resolution analysis of oriented patterns. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 411–414. IEEE, New York, NY, USA.Google Scholar
Mohanty, AK, Senapati, MR, Beberta, S & Lenka, SK (2013). Texture-based features for classification of mammograms using decision tree. Neural Comput Appl 23, 1011–1017.Google Scholar
Mostaço-Guidolin, LB, Ko, ACT, Wang, F, Xiang, B, Hewko, M, Tian, G, Major, A, Shiomi, M & Sowa, MG (2013). Collagen morphology and texture analysis: From statistics to classification. Sci Rep 3, 2190.Google Scholar
Nanou, A, Crespo, M, Flohr, P, De Bono, J & Terstappen, L (2018). Scanning electron microscopy of circulating tumor cells and tumor-derived extracellular vesicles. Cancers 10, 416.Google Scholar
Nematbakhsh, Y, Pang, KT & Lim, CT (2017). Correlating the viscoelasticity of breast cancer cells with their malignancy. Converg Sci Phys Oncol 3, 034003.Google Scholar
Ojala, T, Pietikainen, M & Maenpaa, T (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24, 971–987.Google Scholar
Palazoglu, A, Stroeve, P & Romagnoli, JA (2010). Wavelet analysis of images from scanning probe and electron microscopy. Microscopy 2, 1251–1262.Google Scholar
Pinkert, MA, Salkowski, LR, Keely, PJ, Hall, TJ, Block, WF & Eliceiri, KW (2018). Review of quantitative multiscale imaging of breast cancer. J Med Imaging 5, 010901.Google Scholar
Plodinec, M, Loparic, M, Monnier, CA, Obermann, EC, Zanetti-Dallenbach, R, Oertle, P, Hyotyla, JT, Aebi, U, Bentires-Alj, M, Lim, RY & Schoenenberger, CA (2012). The nanomechanical signature of breast cancer. Nat Nanotechnol 7, 757.Google Scholar
Raman, SP, Chen, Y, Schroeder, JL, Huang, P & Fishman, EK (2014). CT texture analysis of renal masses: Pilot study using random forest classification for prediction of pathology. Acad Radiol 21, 1587–1596.Google Scholar
Ren, J, Jiang, X, Yuan, J & Wang, G (2014). Optimizing LBP structure for visual recognition using binary quadratic programming. IEEE Signal Process Lett 21, 1346–1350.Google Scholar
Somwanshi, DK, Yadav, AK & Roy, R (2017). Medical images texture analysis: A review. In 2017 International Conference on Computer, Communications and Electronics (Comptelix), pp. 436–441. IEEE, Jaipur, India.Google Scholar
Stylianou, A, Lekka, M & Stylianopoulos, T (2018). AFM assessing of nanomechanical fingerprints for cancer early diagnosis and classification: From single cell to tissue level. Nanoscale 10, 20930–20945.Google Scholar
Subramanian, H, Roy, HK, Pradhan, P, Goldberg, MJ, Muldoon, J, Brand, RE, Sturgis, C, Hensing, T, Ray, D, Bogojevic, A & Mohammed, J (2009). Nanoscale cellular changes in field carcinogenesis detected by partial wave spectroscopy. Cancer Res 69, 5357–5363.Google Scholar
Susan, S & Hanmandlu, M (2013). Difference theoretic feature set for scale-, illumination- and rotation-invariant texture classification. IET Image Process 7, 725–732.Google Scholar
Tanaka, E, Noguchi, T, Nagai, K, Akashi, Y, Kawahara, K & Shimada, T (2012). Morphology of the epithelium of the lower rectum and the anal canal in the adult human. Med Mol Morphol 45, 72–79.Google Scholar
Taufalele, PV, VanderBurgh, JA, Muñoz, A, Zanotelli, MR & Reinhart-King, CA (2019). Fiber alignment drives changes in architectural and mechanical features in collagen matrices. PLoS One 14, e0216537.Google Scholar
Thakur, M & Hazare, V (2011). Scanning electron microscopic study of surface epithelial cells in erosive and nonerosive oral lichen planus. J Contemp Dent Prac 12, 463–468.Google Scholar
Tian, M, Li, Y, Liu, W, Jin, L, Jiang, X, Wang, X, Ding, Z, Peng, Y, Zhou, J, Fan, J & Cao, Y (2015). The nanomechanical signature of liver cancer tissues and its molecular origin. Nanoscale 7, 12998–13010.Google Scholar
Tilakaratne, WM, Klinikowski, MF, Saku, T, Peters, TJ & Warnakulasuriya, S (2006). Oral submucous fibrosis: Review on aetiology and pathogenesis. Oral Oncol 42, 561–568.Google Scholar
Uttam, S, Pham, HV, LaFace, J, Leibowitz, B, Yu, J, Brand, RE, Hartman, DJ & Liu, Y (2015). Early prediction of cancer progression by depth-resolved nanoscale maps of nuclear architecture from unstained tissue specimens. Cancer Res 75, 4718–4727.Google Scholar
Vetterli, M & Herley, C (1992). Wavelets and filter banks: Theory and design. IEEE Trans Signal Process 40, 2207–2232.Google Scholar
Vrbik, I, Van Nest, SJ, Meksiarun, P, Loeppky, J, Brolo, A, Lum, JJ & Jirasek, A (2019). Haralick texture feature analysis for quantifying radiation response heterogeneity in murine models observed using Raman spectroscopic mapping. PLoS One 14, e0212225.Google Scholar
Workman, MJ, Serov, A, Halevi, B, Atanassov, P & Artyushkova, K (2015). Application of the discrete wavelet transform to SEM and AFM micrographs for quantitative analysis of complex surfaces. Langmuir 31, 4924–4933.Google Scholar
Yoshimura, K, Dissanayake, UB, Nanayakkara, D, Kageyama, I & Kobayashi, K (2005). Morphological changes in oral mucosae and their connective tissue cores regarding oral submucous fibrosis. Arch Histol Cytol 68, 185–192.Google Scholar
Zangooei, MH & Habibi, J (2017). Hybrid multiscale modeling and prediction of cancer cell behavior. PLoS One 12, e0183810.Google Scholar
Zhang, X, Cui, J, Wang, W & Lin, C (2017). A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm. Sensors 17, 1474.Google Scholar
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