Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma

Abstract

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.

Keywords: machine learning; medulloblastoma; molecular subgroups; prediction; radiomics.

FIGURE 1

Overview of the radiomics pipeline in this study. The pipeline consisted of tumor delineation, image preprocessing, radiomics feature extraction, feature selection, model building, and model evaluation.

FIGURE 2

Receiver operating characteristic (ROC) curves of the radiomics model and the radiomics, location, hydrocephalus, and clinical factors (RLHC) model. (A,B) ROC curves of the radiomics model on the training cohort and testing cohort, respectively. (C,D) ROC curves of the RLHC model on the training cohort and testing cohort, respectively.

FIGURE 3

MR images and corresponding feature maps of the selected 11 features for a wingless patient, a sonic hedgehog patient, a group 3 patient, and a group 4 patient. The delineated tumor contour was overlapped on the MR images. The radiomics features f₁–f₁₁ are defined in Table 2. The feature maps visualized the intratumoral variations of the image patterns, revealing the association of the radiomics features with the molecular subgroups.

FIGURE 4

Heat map of the subgroup-specific importance of all parameters used in subgroup classification.