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Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery

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Abstract

Purpose

Gamma Knife radiosurgery (GKRS) is a non-invasive procedure for the treatment of brain metastases. This study sought to determine whether radiomic features of brain metastases derived from pre-GKRS magnetic resonance imaging (MRI) could be used in conjunction with clinical variables to predict the effectiveness of GKRS in achieving local tumor control.

Methods

We retrospectively analyzed 161 patients with non-small cell lung cancer (576 brain metastases) who underwent GKRS for brain metastases. The database included clinical data and pre-GKRS MRI. Brain metastases were demarcated by experienced neurosurgeons, and radiomic features of each brain metastasis were extracted. Consensus clustering was used for feature selection. Cox proportional hazards models and cause-specific proportional hazards models were used to correlate clinical variables and radiomic features with local control of brain metastases after GKRS.

Results

Multivariate Cox proportional hazards model revealed that higher zone percentage (hazard ratio, HR 0.712; P = .022) was independently associated with superior local tumor control. Similarly, multivariate cause-specific proportional hazards model revealed that higher zone percentage (HR 0.699; P = .014) was independently associated with superior local tumor control.

Conclusions

The zone percentage of brain metastases, a radiomic feature derived from pre-GKRS contrast-enhanced T1-weighted MRIs, was found to be an independent prognostic factor of local tumor control following GKRS in patients with non-small cell lung cancer and brain metastases. Radiomic features indicate the biological basis and characteristics of tumors and could potentially be used as surrogate biomarkers for predicting tumor prognosis following GKRS.

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Acknowledgements

The authors would like to thank all colleagues who contributed to this study. We are grateful to our research assistants, Fong-Jiao Lee, Hsueh-Jen Huang, Wen-Chi Ku, Yi-Bei Tseng, and Jr Lan Huang for their data recording and transcription. We thank the editor and series editor for constructive criticisms of an earlier version of this article. This work was financially supported in part by the Ministry of Science and Technology, Taiwan, under the project MOST 108-2221-E-038-019 and the project MOST 108-2634-F-010-002, in part by the Research Grants for Newly Hired Faculty by the Taipei Medical University in Taiwan, under the project TMU 108-AE1-B04, and in part by the Brain Research Center, National Yang-Ming University from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

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Authors and Affiliations

  1. Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan

    Chih-Ying Huang

  2. School of Medicine, National Yang-Ming University, Taipei, Taiwan

    Chih-Ying Huang, Cheng-Chia Lee, Huai-Che Yang, Chung-Jung Lin, Hsiu-Mei Wu, Wen-Yuh Chung, Cheng-Ying Shiau & Wan-Yuo Guo

  3. Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan

    Cheng-Chia Lee, Huai-Che Yang, Wen-Yuh Chung & David Hung-Chi Pan

  4. Brain Research Center, National Yang-Ming University, Taipei, Taiwan

    Cheng-Chia Lee

  5. Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan

    Chung-Jung Lin, Hsiu-Mei Wu & Wan-Yuo Guo

  6. Cancer Center, Taipei Veterans General Hospital, Taipei, Taiwan

    Cheng-Ying Shiau

  7. Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan

    David Hung-Chi Pan

  8. Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan

    Syu-Jyun Peng

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  1. Chih-Ying Huang
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Below is the link to the electronic supplementary material. Supplementary Fig.1 Consensus clustering delta area plot. The relative change in the area under cumulative distribution function curves (y-axis) approximately converges to minimum at 8 or more clusters (x-axis). Therefore, we selected 8 as the number of clusters to use in analyses.

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Huang, CY., Lee, CC., Yang, HC. et al. Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery. J Neurooncol 146, 439–449 (2020). https://doi.org/10.1007/s11060-019-03343-4

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