Dynamic and perfusion computed tomography for solitary lung foci

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.

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Texture Analysis of CT Images in Head and Neck Tumors Differentiation

Khodjibekova

Khodjibekov

Akhmedov

et al. 2022

Vestn. rentgenol. radiol.

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Dynamic and perfusion computed tomography for solitary lung foci

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Texture Analysis of CT Images in Head and Neck Tumors Differentiation

Texture Analysis of CT Images in Head and Neck Tumors Differentiation

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