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Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas

  • Shun Zhang
  • Gloria Chia-Yi Chiang
  • Rajiv S. Magge
  • Howard Alan Fine
  • Rohan Ramakrishna
  • Eileen Wang Chang
  • Tejas Pulisetty
  • Yi Wang
  • Wenzhen ZhuEmail author
  • Ilhami KovanlikayaEmail author
Neuro

Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusion

Texture analysis on conventional preoperative MRI images can accurately predict early malignant transformation of LGGs, which may guide therapeutic planning.

Key Points

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.

Keywords

Magnetic resonance imaging Glioma Astrocytoma Computer-assisted image analysis 

Abbreviations

AUC

Area under the curve

LDA

Linear discriminant analysis

LGG

Low-grade glioma

MDF

Most discriminant factor

ROC

Receiver operating characteristic

Notes

Acknowledgements

The authors thank Kelly McCabe Gillen, PhD, for her assistance in manuscript editing.

Funding

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

Guarantor

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.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• observational

• performed at one institution

Supplementary material

330_2018_5921_MOESM1_ESM.docx (1.2 mb)
ESM 1 (DOCX 1194 kb)

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Shun Zhang
    • 1
    • 2
  • Gloria Chia-Yi Chiang
    • 2
  • Rajiv S. Magge
    • 3
  • Howard Alan Fine
    • 3
  • Rohan Ramakrishna
    • 4
  • Eileen Wang Chang
    • 2
  • Tejas Pulisetty
    • 5
  • Yi Wang
    • 2
    • 6
  • Wenzhen Zhu
    • 1
    Email author
  • Ilhami Kovanlikaya
    • 2
    Email author
  1. 1.Department of Radiology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
  2. 2.Department of RadiologyWeill Cornell MedicineNew YorkUSA
  3. 3.Department of NeurologyWeill Cornell MedicineNew YorkUSA
  4. 4.Department of Neurological SurgeryWeill Cornell MedicineNew YorkUSA
  5. 5.Department of RadiologySaint Louis UniversitySaint LouisUSA
  6. 6.Department of Biomedical EngineeringCornell UniversityIthacaUSA

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