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

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  • 1 Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital;
  • | 2 Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University;
  • | 3 School of Medicine, National Yang Ming Chiao Tung University, Taipei;
  • | 4 Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan;
  • | 5 Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia;
  • | 6 Department of Otolaryngology–Head and Neck Surgery, Taipei Veterans General Hospital;
  • | 7 Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University; and
  • | 8 Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
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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.

ABBREVIATIONS

CET1WI = contrast-enhanced T1-weighted imaging; GKRS = Gamma Knife radiosurgery; ROI = region of interest; SGR = specific growth rate; SI = signal intensity; T2WI = T2-weighted imaging; T2W/T1WC = T2-weighted/T1-weighted with contrast; VS = vestibular schwannoma; WM = white matter.

Supplementary Materials

    • Supplemental Methods (PDF 440 KB)

Illustration from Serrato-Avila (pp 1410–1423). Copyright Johns Hopkins University, Art as Applied to Medicine. Published with permission.

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