MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study

Abstract

Background Diffuse Intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model. Methods We isolated tumor volumes of T1-post contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of LASSO Cox regression selected optimal features to predict overall survival (OS) in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. Results All selected features were intensity and texture-based on the wavelet filtered images (three T1 grey-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first order-mean). This multivariable Cox model demonstrated a concordance of 0.68 [95% CI: 0.61-0.74] in the training dataset, significantly outperforming the clinical-only model (C=0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C=0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C=0.59 [95% CI: 0.51-0.67], Noether’s test p=0.02). Conclusion In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.

Suggestions for Escaping the Dark Ages for Pediatric Diffuse Intrinsic Pontine Glioma Treated with Radiotherapy: Analysis of Prognostic Factors from the National Multicenter Study

“…Moreover, data on molecular parameters were available in a very limited number of patients, precluding further analysis of molecular parameters. Therefore, previous DIPG prognostication studies were based on MRI findings, which can be easily obtained in the clinical environment [17][18][19].…”

Diagnostic accuracy of qualitative MRI in 550 paediatric brain tumours: evaluating current practice in the computational era

“…Furthermore, via correlation with molecular profiling, it is hoped that these and related computational techniques may permit the accurate, image-based, non-invasive phenotyping and predictive genotyping of brain tumours. This would be of particular benefit in guiding therapy in the 'non-operable tumours', such as in certain DMGs (12,13). There are, however, many challenges with these techniques, including problems with external generalisation and reproducibility.…”

Suggestions for Escaping The Dark Ages for Pediatric Diffuse Intrinsic Pontine Glioma Treated with Radiotherapy: Analysis of Prognostic Factors From The National Multicenter Study

“…Moreover, molecular parameters were obtained only from a very limited number of patients, so it was impossible to conduct further analysis on the molecular parameters. Therefore, many studies regarding the prediction of the prognosis of patients with DIPG were based on imaging ndings, which may be applied easily in the actual clinical environment [17][18][19].…”