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Multimodality MRI-based radiomics approach to predict the posttreatment response of lung cancer brain metastases to gamma knife radiosurgery

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop and validate a multimodality MRI-based radiomics approach to predicting the posttreatment response of lung cancer brain metastases (LCBM) to gamma knife radiosurgery (GKRS).

Methods

We retrospectively analyzed 213 lesions from 137 patients with LCBM who received GKRS between January 2017 and November 2020. The data were divided into a primary cohort (102 patients with 173 lesions) and an independent validation cohort (35 patients with 40 lesions) according to the time of treatment. Benefit result was defined using pretreatment and 3-month follow-up MRI images based on the Response Assessment in Neuro-Oncology Brain Metastases criteria. Valuable radiomics features were extracted from pretreatment multimodality MRI images using random forests. Prediction performance among the radiomics features of tumor core (RFTC) and radiomics features of peritumoral edema (RFPE) together was evaluated separately. Then, the random forest radiomics score and nomogram were developed through the primary cohort and evaluated through an independent validation cohort. Prediction performance was evaluated by ROC curve, calibration curve, and decision curve.

Results

Gender (p = 0.018), histological subtype (p = 0.009), epidermal growth factor receptor mutation (p = 0.034), and targeted drug treatment (p = 0.021) were significantly associated with posttreatment response. Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). Finally, the radiomics nomogram had an AUC of 0.930, a C-index of 0.930 (specificity of 83.1%, sensitivity of 87.3%) in primary cohort, and an AUC of 0.852, a C-index of 0.848 (specificity of 84.2%, sensitivity of 76.2%) in validation cohort.

Conclusions

Multimodality MRI-based radiomics models can predict the posttreatment response of LCBM to GKRS.

Key Points

• Among the selected radiomics features, texture features basically contributed the dominant force in prediction tasks (80%), especially gray-level co-occurrence matrix features (40%).

• Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001).

• The multimodality MRI-based radiomics nomogram showed high accuracy for distinguishing the posttreatment response of LCBM to GKRS (AUC = 0.930, in primary cohort; AUC = 0.852, in validation cohort).

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Abbreviations

CBV:

Cerebral blood volume

EGFR:

Epidermal growth factor receptor

GKRS:

Gamma knife radiosurgery

LCBM:

Lung cancer brain metastases

RF_Score:

Random forest radiomics score

RFPE:

Radiomics features of peritumoral edema

RFTC:

Radiomics features of tumor core

T1-MPRAGE:

T1 magnetization-prepared rapid gradient-echo

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Funding

This study has received funding by the National Natural Science Foundation of China (No. 61971271), the Natural Science Foundation of Shandong Province (No. ZR2019QF007), Primary Research and Development Plan of Shandong Province (2017CXCG1209, 2018GGX101018, 2019QYTPY020), and the Taishan Scholars Program (No. tsqn 20161023, 20161070).

Author information

Author notes

  1. Zekun Jiang and Bao Wang contributed equally to this work.

Authors and Affiliations

  1. Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, East Wenhua Road 88, Jinan, 250014, Shandong, China

    Zekun Jiang & Dengwang Li

  2. Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, China

    Bao Wang

  3. Department of Experiment, Tumor Hospital Affiliated to Guangxi Medical University, Nanning, Guangxi, China

    Xiao Han

  4. Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jingwu Road 324, Jinan, 250021, Shandong, China

    Peng Zhao, Meng Gao, Chuanjin Lan & Yingchao Liu

  5. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China

    Yi Zhang & Ping Wei

Authors
  1. Zekun Jiang
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Corresponding authors

Correspondence to Yingchao Liu or Dengwang Li.

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The scientific guarantor of this publication is Yingchao Liu.

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The authors declare no competing interests.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained (NSFC NO: 2019–272).

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• retrospective

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• performed at one institution

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Jiang, Z., Wang, B., Han, X. et al. Multimodality MRI-based radiomics approach to predict the posttreatment response of lung cancer brain metastases to gamma knife radiosurgery. Eur Radiol 32, 2266–2276 (2022). https://doi.org/10.1007/s00330-021-08368-w

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