Abstract
PurposeThe 1p/19q co-deletion status has been demonstrated to be a prognostic biomarker in lower grade glioma (LGG). The objective of this study was to build a magnetic resonance (MRI)-derived radiomics model to predict the 1p/19q co-deletion status. Method209 pathology-confirmed LGG patients from 2 different datasets from The Cancer Imaging Archive were retrospectively reviewed; one dataset with 159 patients as the training and discovery dataset and the other one with 50 patients as validation dataset.Radiomics features were extracted from T2- and T1-weighted post-contrast MRI resampled data using linear and cubic interpolation methods.For each of the voxel resampling methods a three-step approach was used for feature selection and a random forest (RF) classifier was trained on the training dataset. Model performance was evaluated on training and validation datasets and clinical utility indexes (CUIs) were computed. The distributions and intercorrelation for selected features were analyzed. ResultsSeven radiomics features were selected from the cubic interpolated features and five from the linear interpolated features on the training dataset. The RF classifier showed similar performance for cubic and linear interpolation methods in the training dataset with accuracies of 0.81 (0.75−0.86) and 0.76 (0.71−0.82) respectively; in the validation dataset the accuracy dropped to 0.72 (0.6−0.82) using cubic interpolation and 0.72 (0.6−0.84) using linear resampling. CUIs showed the model achieved satisfactory negative values (0.605 using cubic interpolation and 0.569 for linear interpolation). ConclusionsMRI has the potential for predicting the 1p/19q status in LGGs. Both cubic and linear interpolation methods showed similar performance in external validation.
Highlights
Gliomas are tumors of the central nervous system and are the most frequent primary tumors arising in the brain [1]
Training dataset One hundred and fifty-nine consecutive lower grade glioma (LGG) patients with preoperative magnetic resonance imaging (MRI) images collected between 01-10-2002 and 01-09-2011 and biopsy proven 1p/19q status were identified within the LGG1p19q Deletion archive (Supplementary Materials Table 1)
Results on validation dataset The area under curve (AUC) for features extracted for cubic interpolation was 0.87
Summary
Gliomas are tumors of the central nervous system and are the most frequent primary tumors arising in the brain [1]. They are classified into four grades based on their aggressiveness by The World Health. Treatment choices for LGG are based on WHO grades, molecular profiles, and patient characteristics (e.g. age and Karnofsky performance status) [4]. The co-deletion of chromosome arms 1p and 19q has an important role in choosing the right treatment, co-deletion is a useful prognostic molecular marker as it can be used for the prediction of response to radiotherapy and chemotherapy, and it is associated with longer survival [5,6,7,8]. Efficient treatment planning necessitates proper classification of WHO grade and 1p/19q co-deletion status
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