MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study

Abstract

Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified. There were 1800 features extracted from T2- and contrast-enhanced T1-weighted preoperative MRI scans. A two-stage sequential classifier was designed-one that first identifies non-wingless (WNT) and non-sonic hedgehog (SHH) MB and then differentiates therapeutically relevant WNT from SHH. Further, a classifier that distinguishes high-risk group 3 from group 4 MB was developed. An independent, binary subgroup analysis was conducted to uncover radiomics features unique to infantile versus childhood SHH subgroups. The best-performing models from six candidate classifiers were selected, and performance was measured on holdout test sets. CIs were obtained by bootstrapping the test sets for 2000 random samples. Model accuracy score was compared with the no-information rate using the Wald test. Results The study cohort comprised 263 patients (mean age ± SD at diagnosis, 87 months ± 60; 166 boys). A two-stage classifier outperformed a single-stage multiclass classifier. The combined, sequential classifier achieved a microaveraged F1 score of 88% and a binary F1 score of 95% specifically for WNT. A group 3 versus group 4 classifier achieved an area under the receiver operating characteristic curve of 98%. Of the Image Biomarker Standardization Initiative features, texture and first-order intensity features were most contributory across the molecular subgroups. Conclusion An MRI-based machine learning decision path allowed identification of the four clinically relevant molecular pediatric medulloblastoma subgroups. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chaudhary and Bapuraj in this issue.

Graphical abstract

Figure 1:

Flowchart shows workflow for training and testing of a two-stage classifier. Each stage consists of a binary classifier optimized for its own respective reduced-feature set obtained by sparse regression analyses. The first stage passes the subgroup composed of wingless (WNT) and sonic hedgehog (SHH) to the second stage for further separation. CE = contrast enhanced, LASSO = least absolute shrinkage and selection operator, NN = neural network.

Figure 2:

Examples of probability output on contrast-enhanced T1-weighted (left) and T2-weighted (right) MRI scans from the medulloblastoma (MB) test subset that did not participate in the model development. (A) Results of a staged primary classifier model are shown with probability outputs of non-wingless (WNT) and non–sonic hedgehog (SHH) and subsequent outputs of WNT and SHH generated from WNT and SHH. (B) Examples of tumors from an independent binary classifier model that differentiates between group 3 and group 4 are shown.

Figure 3:

(A) Bar plot shows the relative influence as calculated with logistic regression of the seven reduced features for the second stage, a binary classifier trained to distinguish wingless (WNT) from sonic hedgehog (SHH) medulloblastoma. (B–D) Density plots of the top three Image Biomarker Standardization Initiative features, including (B) T1-Correlation, (C) T2-Kurtosis, and (D) T2-Interquartile Range.

Figure 4:

Examples of axial contrast-enhanced T1 MRI scans of wingless (WNT) and sonic hedgehog (SHH) medulloblastoma. T1-Correlation, a global measure of homogeneity, was greater for SHH on contrast-enhanced T1-weighted MRI scans. At a macroscopic level, SHH tumors appear to have more homogeneous distribution of high signal intensity across pixels on T1-weighted MRI scans compared with more heterogeneous enhancement of WNT tumors, which might relate to higher vascular fragility and associated hemorrhagic components. Note that hemorrhagic fluid-level (*) and stippled and curvilinear foci of enhancement (arrows) are seen in the patient with WNT tumor.

Figure 5:

(A) Bar plot shows the relative influence as calculated by logistic regression of the top 10 reduced features for the follow-up binary classifier trained to distinguish group 3 from group 4. (B–D) Density plots of the top three Image Biomarker Standardization Initiative features, including (B) T2-Mean, (C) T1-Mean, and (D) T1-Run Length Nonuniformity.

Figure 6:

Contrast-enhanced (CE) T1-weighted, arterial spin labeling (ASL), and T2*-weighted MRI features of medulloblastoma and corresponding vascular immunohistochemistry. The ETS-related gene (ERG) antibody (pink) stains are for nuclei of endothelial cells, which line the interior surface of blood vessels and are a marker of vascular density. The Claudin-5 (Cldn5) antibody (brown) stains are for endothelial tight junction protein and mark blood-brain barrier integrity. (A) Images in a patient with group 3 right cerebellopontine mass. On T2*-weighted image, the mass shows heterogeneous enhancement with irregular T2* foci of blood products or deoxyhemoglobin of tumor vascularity (red arrows). Corresponding ASL image shows high perfusion (white arrow). Diffuse loss of Claudin-5 expression (lack of brown staining) along endothelial cells (pink ERG staining) suggests blood-brain barrier breakdown. (B) Images in a different patient with group 3 tumor show faint foci (red arrows) on T2*-weighted image, intermediate perfusion, and heterogeneous distribution of Claudin-5 expression, depending on the tumor specimen location (blue and green boxes). (C) Images in patient with group 4 tumor show mild, patchy enhancement and intermediate perfusion. No discrete blood products are present on T2*-weighted MRI scan. Diffuse Claudin-5 expression is seen associated with abundant endothelial cells (pink staining behind the brown staining) within the tumor, suggesting high vascular density associated with preserved blood-brain barrier integrity.