Objective: to determine the diagnostic significance of computed tomography texture analysis (CTTA) in differentiating head and neck tumors.Material and methods. The study included 118 patients aged from 4 to 80 years with a verified diagnosis of benign and malignant (37 and 81, respectively) head and neck tumors. CTTA was performed using the LIFEx program, version 6.30. Thirty eight (38) texture indices extracted from routine CT images were tested by regression analysis with creation of logistic texture models with associations of four indices as independent predictors.Results. The possibility of using derived models – probability textural indices for benign and malignant tumors differentiation was established: area under ROC-curve (AUC) 0.854 ± 0.035 (p < 0.001); for differentiation of locally spread from locally limited tumors: AUC 0.840 ± 0.049 (p < 0.001); for differentiation of moderately, poorly, and undifferentiated cancer (G2, G3, G4) from well-differentiated (G1) head and neck cancer: AUC 0.826 ± 0.085 (p < 0.001).Conclusion. CT images texture analysis allows to make non-invasive prognosis of benign or malignant nature of a visualized head and neck tumor, as well as to determine the extent and degree of tumor malignancy.

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.