» Articles » PMID: 29443389

Can We Trust the Calculation of Texture Indices of CT Images? A Phantom Study

Overview
Journal Med Phys
Specialty Biophysics
Date 2018 Feb 15
PMID 29443389
Citations 26
Authors
Affiliations
Soon will be listed here.
Abstract

Purpose: Texture analysis is an emerging tool in the field of medical imaging analysis. However, many issues have been raised in terms of its use in assessing patient images and it is crucial to harmonize and standardize this new imaging measurement tool. This study was designed to evaluate the reliability of texture indices of CT images on a phantom including a reproducibility study, to assess the discriminatory capacity of indices potentially relevant in CT medical images and to determine their redundancy.

Methods: For the reproducibility and discriminatory analysis, eight identical CT acquisitions were performed on a phantom including one homogeneous insert and two close heterogeneous inserts. Texture indices were selected for their high reproducibility and capability of discriminating different textures. For the redundancy analysis, 39 acquisitions of the same phantom were performed using varying acquisition parameters and a correlation matrix was used to explore the 2 × 2 relationships. LIFEx software was used to explore 34 different parameters including first order and texture indices.

Results: Only eight indices of 34 exhibited high reproducibility and discriminated textures from each other. Skewness and kurtosis from histogram were independent from the six other indices but were intercorrelated, the other six indices correlated in diverse degrees (entropy, dissimilarity, and contrast of the co-occurrence matrix, contrast of the Neighborhood Gray Level difference matrix, SZE, ZLNU of the Gray-Level Size Zone Matrix).

Conclusions: Care should be taken when using texture analysis as a tool to characterize CT images because changes in quantitation may be primarily due to internal variability rather than from real physio-pathological effects. Some textural indices appear to be sufficiently reliable and capable to discriminate close textures on CT images.

Citing Articles

Reproducibility and location-stability of radiomic features derived from cone-beam computed tomography: a phantom study.

He X, Chen Z, Gao Y, Wang W, You M Dentomaxillofac Radiol. 2023; 52(8):20230180.

PMID: 37664997 PMC: 10968769. DOI: 10.1259/dmfr.20230180.


Radiomics nomogram based on multi-parametric magnetic resonance imaging for predicting early recurrence in small hepatocellular carcinoma after radiofrequency ablation.

Zhang X, Wang C, Zheng D, Liao Y, Wang X, Huang Z Front Oncol. 2022; 12:1013770.

PMID: 36439458 PMC: 9686343. DOI: 10.3389/fonc.2022.1013770.


Radiomics in bone pathology of the jaws.

Santos G, Silva H, Ossege F, Figueiredo P, Melo N, Stefani C Dentomaxillofac Radiol. 2022; 52(1):20220225.

PMID: 36416666 PMC: 9793454. DOI: 10.1259/dmfr.20220225.


Diagnostic value of routine dental radiographs for predicting the mandibular canal localization validated by cone-beam computed tomogram measurements.

Wiechens B, Brockmeyer P, Sevinc T, Hoene G, Schliephake H, Hahn W Clin Exp Dent Res. 2022; 8(6):1440-1448.

PMID: 35938927 PMC: 9760152. DOI: 10.1002/cre2.639.


A multi-modality physical phantom for mimicking tumor heterogeneity patterns in PET/CT and PET/MRI.

Valladares A, Beyer T, Papp L, Salomon E, Rausch I Med Phys. 2022; 49(9):5819-5829.

PMID: 35838056 PMC: 9543355. DOI: 10.1002/mp.15853.