Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas
Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. This study aimed to evaluate the feasibility of texture analysis on preoperative conventional MRI images in predicting early malignant transformation from low- to high-grade glioma and compare its utility to histogram analysis alone.
A total of 68 patients with low-grade glioma (LGG) were included in this study, 15 of which showed malignant transformation. Patients were randomly divided into training (60%) and testing (40%) sets. Texture analyses were performed to obtain the most discriminant factor (MDF) values for both training and testing data. Receiver operating characteristic (ROC) curve analyses were performed on MDF values and 9 histogram parameters in the training data to obtain cutoff values for determining the correct rates of discrimination between two groups in the testing data.
The ROC analyses on MDF values resulted in an area under the curve (AUC) of 0.90 (sensitivity 85%, specificity 84%) for T2w FLAIR, 0.92 (86%, 94%) for ADC, 0.96 (97%, 84%) for T1w, and 0.82 (78%, 75%) for T1w + Gd and correctly discriminated between the two groups in 93%, 100%, 93%, and 92% of cases in testing data, respectively. In the astrocytoma subgroup, AUCs were 0.92 (88%, 83%) for T2w FLAIR and 0.90 (92%, 74%) for T1w + Gd and correctly discriminated two groups in 100% and 92% of cases. The MDF outperformed all 9 of the histogram parameters.
Texture analysis on conventional preoperative MRI images can accurately predict early malignant transformation of LGGs, which may guide therapeutic planning.
• Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment.
• Texture analysis based on conventional preoperative MR images can accurately predict early malignant transformation from low- to high-grade glioma.
• Texture analysis is a clinically feasible technique that may provide an alternative and effective way of determining the likelihood of early malignant transformation and help guide therapeutic decisions.
KeywordsMagnetic resonance imaging Glioma Astrocytoma Computer-assisted image analysis
Area under the curve
Linear discriminant analysis
Most discriminant factor
Receiver operating characteristic
The authors thank Kelly McCabe Gillen, PhD, for her assistance in manuscript editing.
This study has received funding by grants from the National Institutes of Health of United States (R01 NS095562, R01 NS090464) and National Natural Science Foundation of China (No. 81730049, 81801666).
Compliance with ethical standards
The scientific guarantor of this publication is Shun Zhang.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• performed at one institution
- 6.Rotariu D, Gaivas S, Faiyad Z, Haba D, Iliescu B, Poeata I (2010) Malignant transformation of low grade gliomas into glioblastoma a series of 10 cases and review of the literature. Rom Neurosurg 4:403–412Google Scholar
- 17.Béresová M, Larroza A, Arana E, Varga J, Balkay L, Moratal D (2018) 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution. MAGMA 31:285–294Google Scholar
- 22.Aggarwal N, Agrawal R (2012) First and second order statistics features for classification of magnetic resonance brain images. J Signal Inf Process 3:146Google Scholar
- 25.Szczypiński PM, Strzelecki M, Materka A, Klepaczko A (2009) MaZda--a software package for image texture analysis. Comput Methods Prog Biomed 94:66–76Google Scholar
- 28.Vallières M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496Google Scholar
- 33.Ly KI, Gerstner ER (2018) The role of advanced brain tumor imaging in the care of patients with central nervous system malignancies. Curr Treat Options Oncol 19:40Google Scholar