Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients. Issue 139 (June 2021) - Casale, Roberto; Lavrova, Elizaveta; Sanduleanu, Sebastian; Woodruff, Henry C.; Lambin, Philippe
Highlights: MRI radiomics analysis based on T2- and T1-weighted images may be useful for the prediction of the 1p/19q status in LGG. Cubic and linear interpolation methods showed similar performance for the prediction of the 1p/19q status in LGG. The proposed algorithm has a satisfactory clinical utility value for screening patients with 1p-19q non-co-deletion status. Abstract: Purpose: The 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. Method: 209 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. Results: Seven 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). Conclusions: MRI has the potential for predicting the 1p/19q status in LGGs. Both cubic and linear interpolation methods showed similar performance in external validation.