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Multiparametric MRI-based fusion radiomics for predicting telomerase reverse transcriptase (TERT) promoter mutations and progression-free survival in glioblastoma: a multicentre study

  • Advanced Neuroimaging
  • Published:
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Abstract

Purpose

This study evaluated the performance of multiparametric magnetic resonance imaging (MRI)–based fusion radiomics models (MMFRs) to predict telomerase reverse transcriptase (TERT) promoter mutation status and progression-free survival (PFS) in glioblastoma patients.

Methods

We retrospectively analysed 208 glioblastoma patients from two hospitals. Quantitative imaging features were extracted from each patient’s T1-weighted, T1-weighted contrast-enhanced, and T2-weighted preoperative images. Using a coarse-to-fine feature selection strategy, four radiomics signature models were constructed based on the three MRI sequences and their combination for TERT promoter mutation status and PFS; model performance was subsequently evaluated. Subgroup analyses were performed by the radiomics signature of TERT promoter mutation status and PFS to distinguish patients who could benefit from prolonged temozolomide chemotherapy cycles.

Results

TERT promoter mutation status was best predicted by MMFR, with an area under the curve (AUC) of 0.816 and 0.812 for the training and internal validation sets, respectively. The external test set also achieved stable and optimal prediction results (AUC, 0.823). MMFR better predicted patient PFS compared with the single-sequence radiomics signature in the test set (C-index, 0.643 vs 0.561 vs 0.620 vs 0.628). Subgroup analyses showed that more than six cycles of postoperative temozolomide chemotherapy were associated with improved PFS for patients in class 2 (high TERT promoter mutation and high survival rates; HR, 0.222; 95% CI, 0.054 − 0.923; p = 0.025).

Conclusion

MMFR is an effective method to predict TERT promoter mutations and PFS in patients with glioblastoma. Moreover, subgroup analysis could differentiate patients who may benefit from prolonged TMZ chemotherapy cycles.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank the Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application for its technical support.

Funding

This work was supported by a grant from the National Natural Science Foundation of China (Grant Number: 82071871); Youth Exploration Fund of Shenzhen Health Economics Society, China (Grant Number: 202211); Science and Technology Program of Guangzhou (202102080257); and Medical Science and Technology Research Foundation of Guangdong province (A2021405).

Author information

Author notes

  1. Hongbo Zhang and Hanwen Zhang contributed equally to this work.

  2. Yi Lei and Biao Huang contributed equally to this work.

Authors and Affiliations

  1. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China

    Hongbo Zhang, Yuze Zhang & Biao Huang

  2. Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, #106 Zhongshan 2Nd Road, Guangzhou, 510080, China

    Hongbo Zhang, Yuze Zhang, Lei Wu, Wanqun Yang & Biao Huang

  3. Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 517108, China

    Hongbo Zhang & Beibei Zhou

  4. Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, #3002 SunGangXi Road, Shenzhen, 518035, China

    Hanwen Zhang & Yi Lei

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Correspondence to Yi Lei or Biao Huang.

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The Institutional Review Board approved the design of this retrospective study (ID: KY-Z-2020–139-02). This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. All authors have participated sufficiently to take public responsibility for the content of the manuscript and have approved its submission to the journal. We have read and understood your journal’s policies, and we believe that neither the manuscript nor the study violates any of these.

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The Institutional Review Board has approved the study design and waived the requirement for informed patient consent due to the retrospective nature of the study.

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Zhang, H., Zhang, H., Zhang, Y. et al. Multiparametric MRI-based fusion radiomics for predicting telomerase reverse transcriptase (TERT) promoter mutations and progression-free survival in glioblastoma: a multicentre study. Neuroradiology 66, 81–92 (2024). https://doi.org/10.1007/s00234-023-03245-3

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