MRI-based radiomics for prognosis of non-midline, pediatric high-grade gliomas.
Authors
Affiliations (12)
Affiliations (12)
- UMass Chan Medical School, Worcester, MA, USA.
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Neurosurgery, Sutter Health California Pacific Medical Center, San Francisco, CA, USA.
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA. [email protected].
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
- Departments of Clinical Radiology & Imaging Sciences, Riley Children's Hospital, Indiana University, Indianapolis, IN, USA.
- Department of Neurosurgery, Dayton Children's Hospital, Wright State University, Boonshoft School of Medicine, Dayton, OH, USA.
- Department of Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT, USA.
- Department of Medical Biophysics, Western University, London, ON, Canada.
- Department of Oncology, Western University, London, ON, Canada.
- Department of Medical Imaging, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA. [email protected].
Abstract
Non-midline, pediatric high-grade gliomas (HGG) represent one of the leading causes of cancer-related deaths in children with underlying molecular genetics and driver mutations that are distinct from adult HGGs. Image-based biomarkers that assist pediatric HGG risk-stratification could potentiate treatment strategies. Towards this, we investigated radiomics approaches for non-midline pediatric HGG prognosis. We identified 77 children (mean age: 140 months; 43 males) with non-midline-origin, hemispheric pediatric HGG tumors across five pediatric institutions. We extracted 1800 image-biomarker standardization initiative (IBSI)-based radiomics tumor features from treatment-naïve, axial gadolinium-enhanced axial T1- and axial T2-weighted brain MRI (Gad T1-MRI, T2-MRI). We performed k-fold cross validation and Cox regression to identify optimal predictive features of overall survival. We calculated the risk scores for each patient using a linear combination of the selected features weighted by their coefficients determined by the Cox regression model. Patients were stratified based on the median risk score into high- and low-risk groups. Python (version 3.10.0) was used for all model development. The Cox regression model that included both clinical (age and sex) and MRI-derived radiomics features demonstrated a concordance of 0.78 (95% CI: 0.70-0.83) compared to concordance of 0.75 (95% CI: 0.69-0.82) that used radiomics features alone and concordance of 0.61 (95% CI: 0.54-0.67) that used clinical features (age and sex) alone. Using the risk scores derived from our radiomics model, we present a Kaplan-Meier curve on our HGG cohort. Median overall survival was 21.7 months in the high-risk group and 44.6 months in the low-risk group (log-rank P = 0.007; hazard ratio 2.42, 95% CI 1.26-4.66). In this multi-center, pilot study, we identified optimal radiomics features in the creation of a prognostic pediatric HGG model. Computational MRI techniques may offer new approaches for quantitatively evaluating tumor phenotype and serve a potential future role in therapy planning and clinical trials eligibility.