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Article · Medical Radiology and radiation safety · May 1, 2023

Choise of Statistical Processing Methods for the Results of Radcomic Analysis of CT Images of Head and Neck Tumors

Aziz Pattokhov, Yu. M. Khodjibekova, Marat Khodjibekov

Marat Khodjibekov
Read on dx.doi.orgPDFDOI 10.33266/1024-6177-2023-68-3-52-56

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.

Radiomics and Machine Learning in Medical ImagingAI in cancer detectionImage Retrieval and Classification Techniquestexture analysishead and neck cancercomputed tomography

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