Evaluation of the diagnostic value of texture analysis of CT images in differentiating benign and malignant tumors of the jaws

Abdullaeva L.Sh.; Khodjibekov M.Kh.; Khodjibekova Y.M.; Polatova D.Sh.; Pattakhov A.Sh.

doi:10.57231/j.idmfs.2024.3.3.005

Evaluation of the diagnostic value of texture analysis of CT images in differentiating benign and malignant tumors of the jaws

Authors

Abdullaeva L.Sh.
Khodjibekov M.Kh.
Khodjibekova Y.M.
Polatova D.Sh.
Pattakhov A.Sh.

DOI:

https://doi.org/10.57231/j.idmfs.2024.3.3.005

Keywords:

spatial heterogeneity, texture analysis, computed tomography, jaw tumors

Abstract

Purpose of the study: to determine the diagnostic value of using radiomics in differentiating benign and malignant formations of the jaws on MSCT images.
Material and methods: A radiomics analysis of CT images was retrospectively performed in 65 patients with histologically and clinically verified tumors of the jaws (28 benign and 37 malignant tumors). For texture analysis, the LIFEx 7.10 program (C. Nioche, F. Orlhac etc.) was used.
Results: Using the Kruskal-Wallis test, 1 of 39 texture parameters - DISCRETIZED_AUC_CSH - showed statistically significant differences between benign and malignant tumors of the jaws (p<0.05). A predictive model was built from the selected texture feature using logistic regression. PredDis= 1/ (1 + exp (-1.02914-0.0044970* DISCRETIZED_AUC_CSH)). The regression values calculated from the model were normalized in the range from 0 to 1 using logit transformation and were used as texture heterogeneity indices.
Conclusion: Texture analysis of CT images allows noninvasive prediction of benign or malignant nature of tumors of the upper and lower jaws. The texture heterogeneity index calculated from the logistic texture model has the highest prognostic accuracy. Texture analysis transforms standard computed tomography into a multiparametric study, complementing the qualitative assessment of anatomical details of the visualized formation with a quantitative functional indicator characterizing intratumor spatial heterogeneity.

References

Геращенко Т.С., Денисов Е.В., Литвяков Н.В., Завьялова М.В., Вторушин С.В., Цыганов М.М., Перельмутер В.М., Чердынцева Н.В. Внутриопухолевая гетерогенность: природа и биологическое значение. БИОХИМИЯ, 2013; 78(11): 1531-1549. [Gerashchenko T.S., Denisov E.V., Litvyakov N.V., Zavyalova M.V., Vtorushin S.V., Tsyganov M.M., Perelmuter V.M., Cherdyntseva N.V. Intratumoral heterogeneity: nature and biological significance. BIOCHEMISTRY, 2013; 78(11): 1531-1549.].

Тимофеев, А.А. Руководство по челюстно-лицевой хирургии и хирургической стоматологии. Киев, 2012: 1048. [Timofeev, A.A. Guide to maxillofacial surgery and surgical dentistry]. Kiev, 2012: 1048.].

Lin G, Keshari KR, Park JM. Cancer Metabolism and Tumor Heterogeneity: Imaging Perspectives Using MR Imaging and Spectroscopy. Contrast Media Mol Imaging. 2017; Oct 9; doi:10.1155/2017/6053879.

C Nioche, F Orlhac, S Boughdad, S Reuzé, J Goya-Outi, C Robert, C Pellot-Barakat, M Soussan, F Frouin, and I Buvat. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Research 2018; 78(16):4786-4789, PMID: 29959149, doi: 10.1158/0008-5472.CAN-18-0125.

Nailon, W. H. Texture Analysis Methods for Medical Image Characterisation. In: Mao, Y., editor. Biomedical Imaging [Internet]. London: IntechOpen; 2010 [cited 2022 Feb 23]. Available from: https://www.intechopen.com/chapters/10175. doi: 10.5772/8912.

Romeo V, Cuocolo R, Ricciardi C, Ugga L, Cocozza S, Verde F, et al. Prediction of tumor grade and nodal status in oropharyngeal and oral cavity squamous-cell carcinoma using a radiomic approach. Anticancer Res 2020; 40:271–80, PMID: 31892576 DOI: 10.21873/anticanres.13949.

Bogowicz M, Riesterer O, Ikenberg K, Stieb S, Moch H, Studer G, Guckenberger M, Tanadini-Lang S. Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma. Int J Radiat Oncol Biol Phys. 2017 Nov 15;99(4):921-928, PMID: 28807534 DOI: 10.1016/j.ijrobp.2017.06.002.

H. Kuno, M.M. Qureshi, M.N. Chapman, B. Li, V.C. Andreu-Arasa, K. Onoue, M.T. Truong and O. Sakai. CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy. American Journal of Neuroradiology December 2017, 38 (12) 2334-2340, PMID: 29025727 PMCID: PMC7963748 DOI: 10.3174/ajnr. A5407.

Ger RB, Zhou S, Elgohari B, Elhalawani H, Mackin DM, Meier JG, Nguyen CM, Anderson BM, Gay C, Ning J, Fuller CD, Li H, Howell RM, Layman RR, Mawlawi O, Stafford RJ, Aerts H, Court LE. Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients. PLoS One. 2019 Sep 19;14(9): e0222509, PMID: 31536526 PMCID: PMC6752873 DOI: 10.1371/journal.pone.0222509.

Meyer HJ, Hamerla G, Höhn AK, Surov A. CT Texture Analysis-Correlations with Histopathology Parameters in Head and Neck Squamous Cell Carcinomas. Front Oncol. 2019 May 28; 9:444, PMCID: PMC6546809, PMID: 31192138, doi: 10.3389/fonc.2019.00444.

Ren J, Qi M, Yuan Y, Duan S, Tao X. Machine Learning-Based MRI Texture Analysis to Predict the Histologic Grade of Oral Squamous Cell Carcinoma. AJR Am J Roentgenol. 2020 Nov;215(5):1184-1190, PMID: 32930606 doi: 10.2214/AJR.19.22593.

Zhang, Y., Chen, C., Tian, Z., Feng, R., Cheng, Y., and Xu, J. The Diagnostic Value of MRI-Based Texture Analysis in Discrimination of Tumors Located in Posterior Fossa: a Preliminary Study. (2019). Front. Neurosci. Vol. 13:1113, PMID: 31708724 PMCID: PMC6819318 doi: 10.3389/fnins.2019.01113.