Quantification of tumor response of cystic vestibular schwannoma to Gamma Knife radiosurgery by using artificial intelligence

Chih Ying Huang, Syu Jyun Peng, Hsiu Mei Wu, Huai Che Yang, Ching Jen Chen, Mao Che Wang, Yong Sin Hu, Yu Wei Chen, Chung Jung Lin, Wan Yuo Guo, David Hung Chi Pan, Wen Yuh Chung, Cheng Chia Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

OBJECTIVE Gamma Knife radiosurgery (GKRS) is a common treatment modality for vestibular schwannoma (VS). The ability to predict treatment response is important in patient counseling and decision-making. The authors developed an algorithm that can automatically segment and differentiate cystic and solid tumor components of VS. They also investigated associations between the quantified radiological features of each component and tumor response after GKRS. METHODS This is a retrospective study comprising 323 patients with VS treated with GKRS. After preprocessing and generation of pretreatment T2-weighted (T2W)/T1-weighted with contrast (T1WC) images, the authors segmented VSs into cystic and solid components by using fuzzy C-means clustering. Quantitative radiological features of the entire tumor and its cystic and solid components were extracted. Linear regression models were implemented to correlate clinical variables and radiological features with the specific growth rate (SGR) of VS after GKRS. RESULTS A multivariable linear regression model of radiological features of the entire tumor demonstrated that a higher tumor mean signal intensity (SI) on T2W/T1WC images (p < 0.001) was associated with a lower SGR after GKRS. Similarly, a multivariable linear regression model using radiological features of cystic and solid tumor components demonstrated that a higher solid component mean SI (p = 0.039) and a higher cystic component mean SI (p = 0.004) on T2W/ T1WC images were associated with a lower SGR after GKRS. A larger cystic component proportion (p = 0.085) was associated with a trend toward a lower SGR after GKRS. CONCLUSIONS Radiological features of VSs on pretreatment MRI that were quantified using fuzzy C-means were associated with tumor response after GKRS. Tumors with a higher tumor mean SI, a higher solid component mean SI, and a higher cystic component mean SI on T2W/T1WC images were more likely to regress in volume after GKRS. Those with a larger cystic component proportion also trended toward regression after GKRS. Further refinement of the algorithm may allow direct prediction of tumor response.

Original languageEnglish
Pages (from-to)1298-1306
Number of pages9
JournalJournal of Neurosurgery
Volume136
Issue number5
DOIs
Publication statusPublished - May 2022

Keywords

  • artificial intelligence
  • cyst
  • fuzzy C-means clustering
  • Gamma Knife
  • quantitative radiological feature
  • specific growth rate
  • stereotactic radiosurgery
  • vestibular schwannoma

ASJC Scopus subject areas

  • Surgery
  • Clinical Neurology

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