Next Article in Journal
Quantifying Tissue Complexity via Fractal Analysis of Salivary Gland Ultrasound Images in Patients with Autoimmune Thyroid Disease
Previous Article in Journal
Abundant Non-Traveling Fractal Solutions of Dromion Type for the Extended Hirota–Satsuma–Ito Equation
Previous Article in Special Issue
Optimization of Region-of-Interest Configuration for Fractal Analysis of Peri-Implant Bone on Panoramic Radiographs
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Fractal Fract
Volume 10
Issue 6
10.3390/fractalfract10060357
fractalfract-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Individualized Prediction of Meningioma Response to Gamma Knife Radiosurgery Using Nested Consensus Machine Learning with 3D Fractal, Lacunarity and Radiomic Features from MRI

by
Herwin Speckter
1,2,3,
Marko Radulovic
3,4,*,
Ivan Gonzalez
1,
Giancarlo Hernandez
1,
Jose Bido
1,
Diones Rivera
1,
Luis Suazo
1,
Santiago Valenzuela
1,
Ismael Peralta
1,
Jeffrey Paulino
1,
Teuddis Bernard
1,
Issael Ramirez
1,
Peter Stoeter
1,2 and
Velicko Vranes
3
1
Centro Gamma Knife Dominicano, CEDIMAT, Plaza de la Salud, Santo Domingo 10514, Dominican Republic
2
Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
3
Department of Basic and Environmental Science, Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo 10602, Dominican Republic
4
Department of Experimental Oncology, Institute for Oncology & Radiology of Serbia, Belgrade 11000, Serbia
*
Author to whom correspondence should be addressed.
Fractal Fract. 2026, 10(6), 357; https://doi.org/10.3390/fractalfract10060357
Submission received: 1 April 2026 / Revised: 4 May 2026 / Accepted: 8 May 2026 / Published: 25 May 2026
(This article belongs to the Special Issue Fractal Analysis in Biology and Medicine)
Versions Notes

Abstract

Background: This study aimed to develop a fully nested, information leakage-free machine-learning workflow to predict the volumetric response of meningioma to Gamma Knife radiosurgery (GKRS) from pre-treatment MRI and to compare the predictive value of radiomic, fractal, lacunarity and clinical/radiosurgical features. GKRS is widely used for treating meningiomas because of its high precision and efficacy. Variability in tumor volumetric response highlights the need for reliable predictors of treatment outcome. Methods: This retrospective cohort study included 204 patients treated with GKRS for grade I meningioma. Radiomic, fractal and lacunarity features were extracted from pre-treatment CE-T1w 3-Tesla MRIs. Feature signatures were generated using a machine-learning workflow incorporating five feature selectors based on a consensus principle to reduce spurious feature selection, followed by five classifiers to predict binary outcome. Results: The models demonstrated consistent predictive performance in the test folds, with AUC values from 0.77 to 0.84. Supplementing radiomic features with clinical, fractal or lacunarity features did not improve predictive performance. Conclusions: Radiomic features showed the strongest predictive value for meningioma volumetric response to GKRS. Darker intratumoral intensity values were associated with a favorable volumetric response, possibly reflecting biologically less active tumor regions. The supplied code enables individual-level prediction for newly encountered patients.
Keywords: meningioma; gamma knife radiosurgery; radiomics; fractal; lacunarity; outcome prediction; machine learning meningioma; gamma knife radiosurgery; radiomics; fractal; lacunarity; outcome prediction; machine learning

Share and Cite

MDPI and ACS Style

Speckter, H.; Radulovic, M.; Gonzalez, I.; Hernandez, G.; Bido, J.; Rivera, D.; Suazo, L.; Valenzuela, S.; Peralta, I.; Paulino, J.; et al. Individualized Prediction of Meningioma Response to Gamma Knife Radiosurgery Using Nested Consensus Machine Learning with 3D Fractal, Lacunarity and Radiomic Features from MRI. Fractal Fract. 2026, 10, 357. https://doi.org/10.3390/fractalfract10060357

AMA Style

Speckter H, Radulovic M, Gonzalez I, Hernandez G, Bido J, Rivera D, Suazo L, Valenzuela S, Peralta I, Paulino J, et al. Individualized Prediction of Meningioma Response to Gamma Knife Radiosurgery Using Nested Consensus Machine Learning with 3D Fractal, Lacunarity and Radiomic Features from MRI. Fractal and Fractional. 2026; 10(6):357. https://doi.org/10.3390/fractalfract10060357

Chicago/Turabian Style

Speckter, Herwin, Marko Radulovic, Ivan Gonzalez, Giancarlo Hernandez, Jose Bido, Diones Rivera, Luis Suazo, Santiago Valenzuela, Ismael Peralta, Jeffrey Paulino, and et al. 2026. "Individualized Prediction of Meningioma Response to Gamma Knife Radiosurgery Using Nested Consensus Machine Learning with 3D Fractal, Lacunarity and Radiomic Features from MRI" Fractal and Fractional 10, no. 6: 357. https://doi.org/10.3390/fractalfract10060357

APA Style

Speckter, H., Radulovic, M., Gonzalez, I., Hernandez, G., Bido, J., Rivera, D., Suazo, L., Valenzuela, S., Peralta, I., Paulino, J., Bernard, T., Ramirez, I., Stoeter, P., & Vranes, V. (2026). Individualized Prediction of Meningioma Response to Gamma Knife Radiosurgery Using Nested Consensus Machine Learning with 3D Fractal, Lacunarity and Radiomic Features from MRI. Fractal and Fractional, 10(6), 357. https://doi.org/10.3390/fractalfract10060357

Article Metrics

Back to TopTop