Choise of Statistical Processing Methods for the Results of Radcomic Analysis of CT Images of Head and Neck Tumors
- Authors: Pattokhov A.S.1, Khodjibekova Y.M.1, Khodjibekov M.K.2
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Affiliations:
- Tashkent state dental institute
- Tashkent medical academy
- Issue: Vol 68, No 3 (2023)
- Pages: 52-56
- Section: Radiation Diagnostics
- URL: https://bakhtiniada.ru/1024-6177/article/view/363838
- DOI: https://doi.org/10.33266/1024-6177-2023-68-3-52-56
- ID: 363838
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Abstract
Purpose: Selection of the optimal method for statistical processing of the results of texture analysis of conventional CT images in patients with head and neck tumors.
Material and methods: A total of 118 patients aged from 4 to 80 years with a verified diagnosis of 37 benign and 81 malignant head and neck tumors were studied. Texture analysis was performed using LIFEx program, version 7.10, with statistical processing using SPSS, MedCalc, XLSTAT, R.
Results: The 39 texture indicators extracted from CT images were subjected to statistical processing by different methods, including Mann-Whitney U test, correlation matrix, factor analysis, LASSO-regression, ending with the development of a logistic classification model. Of the multiple processing methods, LASSO-regression followed by logistic model was optimal; according to its results, the percentage of correct classification of benign and malignant patient groups was – 81.3 %, area under the ROC curve was 0.902±0.029 (p<0.0001), sensitivity – 82.7 %, specificity – 87.5 %.
Conclusion: Texture analysis of medical images allows non-invasive prediction of benign or malignant nature of the imaged head and neck mass. The choice of the correct method for statistical processing of texture analysis results is critical to assess and classify patients according to the nature of the tumor.
About the authors
A. Sh. Pattokhov
Tashkent state dental institute
Email: marat.khodjibekov@gmail.com
Tashkent, Uzbekistan
Yu. M. Khodjibekova
Tashkent state dental institute
Email: marat.khodjibekov@gmail.com
Tashkent, Uzbekistan
M. Kh. Khodjibekov
Tashkent medical academy
Email: marat.khodjibekov@gmail.com
Tashkent, Uzbekistan
References
- Petralia G., Bonello L., Viotti S., Preda L., d’Andrea G., Bellomi M. CT Perfusion in Oncology: How to Do It. Cancer Imaging. 2010;10;1:8-19. doi: 10.1102/1470-7330.2010.0001.
- Gerashchenko T.S., Denisov E.V., Litvyakov N.V., Zavyalova M.V., Vtorushin S.V., Tsyganov M.M., Perelmuter V.M., Cherdyntseva N.V. Intratumor Heterogeneity: Nature and Biological Significance. Biokhimiya = Biochemistry. 2013;78;11:1531–1549 (In Russ.).
- Lin G., Keshari K.R., Park J.M. Cancer Metabolism and Tumor Heterogeneity: Imaging Perspectives Using MR Imaging and Spectroscopy. Contrast Media Mol Imaging. 2017;2017:6053879. doi: 10.1155/2017/6053879.
- Nioche C., Orlhac F., Boughdad S., Reuzé S., Goya-Outi J., Robert C., Pellot-Barakat C., Soussan M., Frouin F., Buvat I. 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. doi: 10.1158/0008-5472.CAN-18-0125.
- Nailon W.H. Texture Analysis Methods for Medical Image Characterisation. Biomedical Imaging. Ed. Mao Y. London, IntechOpen, 2010. URL: https://www.intechopen.com/chapters/10175. doi: 10.5772/8912.
- Wu J., Aguilera T., Shultz D., Gudur M., Rubin D.L., Loo B.W.Jr., Diehn M., Li R. Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis. Radiology. 2016;281;1:270-278. doi: 10.1148/radiol.2016151829.
- 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;40:271–280. 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;99;4:921-928. doi: 10.1016/j.ijrobp.2017.06.002.
- 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. Am. J. Roentgenol. 2020;15;5:1184-1190. doi: 10.2214/AJR.19.22593.
- Zhang Y., Chen C., Tian Z., Feng R., Cheng Y., Xu J. The Diagnostic Value of MRI-Based Texture Analysis in Discrimination of Tumors Located in Posterior Fossa: a Preliminary Study. Front. Neurosci. 2019;13:1113. doi: 10.3389/fnins.2019.0111.
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