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MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery

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Abstract

Purpose

This report presents the first investigation of the radiomics value in predicting the meningioma volumetric response to gamma knife radiosurgery (GKRS).

Methods

The retrospective study included 93 meningioma patients imaged by three Tesla MRI. Tumor morphology was quantified by calculating 337 shape, first- and second-order radiomic features from MRI obtained before GKRS. Analysis was performed on original 3D MR images and after their laplacian of gaussian (LoG), logarithm and exponential filtering. The prediction performance was evaluated by Pearson correlation, linear regression and ROC analysis, with meningioma volume change per month as the outcome.

Results

Sixty calculated features significantly correlated with the outcome. The feature selection based on LASSO and multivariate regression started from all available 337 radiomic and 12 non-radiomic features. It selected LoG-sigma-1-0-mm-3D_firstorder_InterquartileRange and logarithm_ngtdm_Busyness as the predictively most robust and non-redundant features. The radiomic score based on these two features produced an AUC = 0.81. Adding the non-radiomic karnofsky performance status (KPS) to the score has increased the AUC to 0.88. Low values of the radiomic score defined a homogeneous subgroup of 50 patients with consistent absence (0%) of tumor progression.

Conclusion

This is the first report of a strong association between MRI radiomic features and volumetric meningioma response to radiosurgery. The clinical importance of the early and reliable prediction of meningioma responsiveness to radiosurgery is based on its potential to aid individualized therapy decision making.

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Data availability

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

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Funding

This work was supported by the University Instituto Tecnologico de Santo Domingo (INTEC), Dominican Republic, Grant Number CBA-221024-2020-P-1.

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Authors and Affiliations

  1. Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic

    Herwin Speckter, Johanna Joaquin, Wenceslao Hernandez, Angel Mota, Jose Bido, Giancarlo Hernandez, Diones Rivera, Luis Suazo, Santiago Valenzuela & Peter Stoeter

  2. Department of Experimental Oncology, Institute for Oncology & Radiology of Serbia, Pasterova 14, 11000, Belgrade, Serbia

    Marko Radulovic

  3. QMENTA Inc., Boston, MA, USA

    Kire Trivodaliev

  4. Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic

    Velicko Vranes

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  1. Herwin Speckter
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  2. Marko Radulovic
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  3. Kire Trivodaliev
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  4. Velicko Vranes
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  11. Luis Suazo
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  12. Santiago Valenzuela
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  13. Peter Stoeter
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Contributions

Conception and design: HS and MR. Data collection: HS, JJ, WH, AM, JB, GH, DR, LS, SV, PS. Data analysis and interpretation: All authors. Manuscript writing: HS, MR, PS. All authors reviewed the manuscript.

Corresponding author

Correspondence to Herwin Speckter.

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Conflict of interest

Kire Trivodaliev is employed by QMENTA Inc., Boston, MA, USA. All other authors have nothing to disclose.

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Speckter, H., Radulovic, M., Trivodaliev, K. et al. MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery. J Neurooncol 159, 281–291 (2022). https://doi.org/10.1007/s11060-022-04063-y

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