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Diffusion-weighted imaging and arterial spin labeling radiomics features may improve differentiation between radiation-induced brain injury and glioma recurrence

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To determine whether radiomics features derived from diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) can improve the differentiation between radiation-induced brain injury (RIBI) and tumor recurrence (TR) in glioma patients.

Methods

A total of 4199 radiomics features were extracted from conventional MRI, apparent diffusion coefficient (ADC), and cerebral blood flow (CBF) maps, obtained from 96 pathologically confirmed WHO grade 2~4 gliomas with enhancement after standard treatment. The intraclass correlation coefficient (ICC) was used to test segmentation stability between two doctors. Radiomics features were selected using the Mann–Whitney U test, LASSO regression, and RFE algorithms. Four machine learning classifiers were adopted to establish radiomics models. The diagnostic performance of multiparameter, conventional, and single-parameter MRI radiomics models was compared using the area under the curve (AUC). The models were evaluated in the subsequent independent validation set (n = 30).

Results

Eight important radiomics features (3 from conventional MRI, 1 from ADC, and 4 from CBF) were selected. Support vector machine (SVM) was chosen as the optimal classifier. The diagnostic performance of the multiparameter MRI radiomics model (AUC 0.96) was higher than that of the conventional MRI (AUC 0.88), ADC (AUC 0.91), and CBF (AUC 0.95) radiomics models. For subgroup analysis, the multiparameter MRI radiomics model showed similar performance, with AUCs of 0.98 in WHO grade 2~3 and 0.96 in WHO grade 4.

Conclusion

The incorporation of noninvasive DWI and ASL into the MRI radiomics model improved the diagnostic performance in differentiating RIBI from TR; ASL, especially, played a significant role.

Key Points

• The multiparameter MRI radiomics model was superior to the conventional MRI radiomics model in differentiating glioma recurrence from radiation-induced brain injury.

• Diffusion and perfusion MRI could improve the ability of the radiomics model in predicting the progression in patients with glioma.

• Arterial spin labeling played an important role in predicting glioma progression using radiomics models.

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Abbreviations

ADC:

Apparent diffusion coefficient

ASL:

Arterial spin labeling

AUC:

Area under the receiver operating characteristics curve

BBB:

Blood–brain barrier

CBF:

Cerebral blood flow

CE:

Contrast-enhanced

DSC:

Dynamic susceptibility contrast-enhanced

DWI:

Diffusion-weighted imaging

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size zone matrix

ICC:

Intraclass correlation coefficient

IDH:

Isocitrate dehydrogenase

KNN:

K-nearest neighbor

LASSO:

Least absolute shrinkage and selection operator

LR:

Logistic regression

MRS:

Magnetic resonance spectroscopy

NB:

Naive Bayes

PsP:

Pseudoprogression

RFE:

Recursive feature elimination

RIBI:

Radiation-induced brain injury

RN:

Radiation necrosis

ROC:

Receiver operating characteristic curves

ROI :

Regions of interest

SVM:

Support vector machine

TMZ:

Temozolomide

TR:

Tumor recurrence

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Funding

This work was supported by the National Key R&D Program of China (2019YFB1311600) and the National Natural Science Foundation of China (62072358).

Author information

Author notes

  1. Jun Zhang, Yue Wu, Liang Zhang and Lin Ma contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The First Medical Center, Chinese PLA General Hospital, 28 Fu-Xing Road, Haidian District, Beijing, 100853, China

    Jun Zhang, Yulin Wang, Xinyue Zhang & Lin Ma

  2. Department of Nuclear Medicine, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, China

    Jun Zhang

  3. School of Computer Science and Technology, Xidian University, Xi’an, 710065, Shanxi, China

    Yue Wu, Guangming Zhu & Liang Zhang

  4. Department of Neurosurgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China

    Yifei Lei

  5. Department of Medical Imaging, Second Affiliated Hospital of Xi’An Jiaotong University, Xi’an, China

    Cuiping Mao

Authors
  1. Jun Zhang
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Corresponding authors

Correspondence to Liang Zhang or Lin Ma.

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The scientific guarantor of this publication is Lin Ma, MD.

Conflict of interests

The authors of this manuscript declare no relationship with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise (Guangming Zhu, 8 years of experience).

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Written informed consent was waived by the Institutional Review Board.

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• Retrospective

• Cross-sectional study

• Performed at one institution

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Jun Zhang and Yue Wu are co-first authors.

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Zhang, J., Wu, Y., Wang, Y. et al. Diffusion-weighted imaging and arterial spin labeling radiomics features may improve differentiation between radiation-induced brain injury and glioma recurrence. Eur Radiol 33, 3332–3342 (2023). https://doi.org/10.1007/s00330-022-09365-3

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