MRI-based radiomics for prognosis of non-midline, pediatric high-grade gliomas.
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.
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.
Authors
Tolba Tolba, Zhang Zhang, Duh Duh, Liverani Liverani, Chang Chang, Supakul Supakul, Lober Lober, Cheshier Cheshier, Mattonen Mattonen, Jaju Jaju, Prolo Prolo, Yeom Yeom
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