Oral Squamous Cell Carcinoma (OSCC) and precancerous lesion Oral Leukoplakia (OL) are usually diagnosed manually by microscopic study of biopsy slides. Such manual microscopic evaluation and classification of histopathological images are subjective, time-consuming, and complex among diagnosing experts due to the influence of multiple factors. Early and precise diagnosis of tumor cells is a prerequisite for reducing oral cancer mortality rate. Hence, the combined influence of multiple factors on tumor prognosis must be statistically studied to reveal the correct inferences and knowledge from clinical data to train deep learning models efficiently. In our retrospective study, clinical experiments were performed using Galectin-3 biomarkers, and the factors were graded by experts to obtain a historical clinical dataset. Multivariate Analysis of Variance (MANOVA) statistical data analysis was performed and implemented using Python code on the experimental dataset, and its inferences on specific questions were found helpful to the tumor grading experts.

oral squamous cell carcinoma, oral leukoplakia, Multivariate Analysis of Variance (MANOVA).

Received: April 15, 2023; Accepted: July 3, 2023; Published: December 22, 2023

How to cite this article: Calwin S. Parthibaraj, Freeda M. Selvaraj and Anna P. Joseph, Tumor cell classification: an application of multivariate data processing method, JP Journal of Biostatistics 24(1) (2024), 87-104. http://dx.doi.org/10.17654/0973514324007

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

References:
[1] S. Warnakulasuriya, Living with oral cancer: epidemiology with particular reference to prevalence and life-style changes that influence survival, Oral Oncology 46(6) (2010), 407-410.
[2] N. Aggarwal and S. Bhateja, Leukoplakia - potentially malignant disorder of oral cavity - a review, Biomed. J. Sci. Tech. Res. 4(5) (2018), 4219-4226.
[3] M. Wunschel, M. Neumeier, K. Utpatel, T. E. Reichert, T. Ettl and G. Spanier, Staging more important than grading? Evaluation of malignancy grading, depth of invasion, and resection margins in oral squamous cell carcinoma, Clinical Oral Investigations 25(3) (2020), 1169-1182.
[4] A. K. El-Naggar, J. K. Chan, J. R. Grandis, T. Takata and P. J. Slootweg, International Agency for Research on Cancer (IARC) Press, WHO Classification of Head and Neck Tumors, WHO/IARC Classification of Tumours, 4th ed., Vol. 9, 2017.
[5] S. Prabhu, K. Prasad, A. Robels-Kelly and X. Lu, AI-based carcinoma detection and classification using histopathological images: a systematic review, Computers in Biology and Medicine 142 (2022), 105209.
[6] S. Vittal and G. Karthikeyan, Modeling association detection in order to discover compounds to inhibit oral cancer, Journal of Biomedical Informatics 84 (2018), 159-163.
[7] M. Yağanoğlu, Hepatitis C virus data analysis and prediction using machine learning, Data & Knowledge Engineering 142 (2022), 102087.
[8] A. Sharma and K. K. Paliwal, Cancer classification by gradient LDA technique using microarray gene expression data, Data & Knowledge Engineering 66 (2008), 338-347.
[9] A.-F. Safi, K. Grochau, U. Drebber, V. Schick, O. Thiele, T. Backhaus, H.-J. Nickenig, J. E. Zöller and M. Kreppel, A novel histopathological scoring system for patients with oral squamous cell carcinoma, Clinical Oral Investigations 23(10) (2019), 3759-3765.
[10] Y.-K. Huang, B.-Y. Peng, C.-Y. Wu, C.-T. Su, H.-C. Wang and H.-C. Lai, DNA methylation of PAX1 as a biomarker for oral squamous cell carcinoma, Clinical Oral Investigations 18(3) (2013), 801-808.
[11] D. Borsetto, J. A. Higginson, A. Aslam, L. Al‐Qamachi, J. Dhanda, G. Marioni, S. Franchella, A. Frigo, P. Praveen, T. Martin, S. Parmar and P. Nankivell, Factors affecting prognosis in locoregional recurrence of oral squamous cell carcinoma, Journal of Oral Pathology and Medicine 48(3) (2019), 206-213.
[12] I. H. Ibrahim, F. A. Khalil, G. A. El-Kelany and S. M. Ahmed, ANOVA and MANOVA analysis in bioequivalence study, JP Journal of Biostatistics 14(1) (2017), 61-74.
[13] S. Padam, Sample size for experimental studies, Journal of Clinical and Preventive Cardiology 1 (2012), 88-93.
[14] A. V. Parwani, The use of a combination of galectin-3 and thyroid peroxidase for the diagnosis and prognosis of thyroid cancer, Yearbook of Pathology and Laboratory Medicine 2006 (2006), 169-170.
[15] L. Unverdorben, T. Haufe, L. Santoso, S. Hofmann, U. Jeschke and S. Hutter, Prototype and chimera - type galectins in placentas with spontaneous and recurrent miscarriages, International Journal of Molecular Sciences 17(5) (2016), 644.
[16] G. Schwarz, M. Remmelink, C. Decaestecker, I. Gielen, V. Budel, M. Burchert, F. Darro, A. Danguy, H.-J. Gabius, I. Salmon and R. Kiss, Galectin fingerprinting in tumor diagnosis: differential expression of galectin-3 and galectin-3 binding sites, but not galectin-1, in benign vs malignant uterine smooth muscle tumors, American Journal of Clinical Pathology 111(5) (1999), 623-631.
[17] T. M. Kolben et al., Expression of Sialyl Lewis a, Sialyl Lewis x, Lewis y, Gal-3, Gal-7, STMN1 and p16 in cervical dysplasia, Future Oncology 13(2) (2017), 145-157.
[18] J. Jiang, M. S. Jin, F. Kong, D. Cao, H. X. Ma, Z. Jia, Y. P. Wang, J. Suo and X. Cao, Decreased galectin-9 and increased Tim 3 expression are related to poor prognosis in gastric cancer, PLoS ONE 8(12) (2013), e81799.