Img-23. An Mri-Based Radiomic Signature to Predict Long-Term Survival in Patients With Diffuse Intrinsic Pontine Gliomas

BACKGROUND Diffuse intrinsic pontine gliomas are characterized by a short life expectancy after diagnosis, with a mean overall survival of less than 12 months. To stratify patients and possibly better adapt therapy, we are proposing a radiomic signature that predicts, at the time of diagnosis, which patients might survive beyond 2 years. METHODS A retrospective database comprising 80 patients who had undergone 3D multimodal MRI at the time of diagnosis was compiled. All images were acquired at a single center, and the protocol included T1-weighted images acquired before and after contrast injection, T2-weighted and FLAIR images. Depending on the patient, some modalities were missing (around 10% per modality). After manual tumor delineation, a dedicated radiomic pipeline extracted 79 features (first-order statistics and texture parameters) per imaging modality. In addition, gender, age and 14 tumor shape features were collected without missing data. After feature selection (maximum of 4 features per model), five logistic regression models were defined, one per modality and another for clinical and shape variables. Each patient was eligible for 2 to 5 models, with an average score calculated over these models. RESULTS Based on a leave-one-out approach, the multi-model was able to define patients who survive more than 2 years correctly, (sensitivity=100%, specificity =79%). A prediction could be made for each patient despite missing MR modalities. Defining a radiomic signature based on the multi-model score (cut-off=0.5), Kaplan-Meier analysis shows a good separation of the two groups (p-value<0.001). This result is superior to that obtained with the TP53 mutation (available for 61 patients, with p-value<0.01). CONCLUSIONS We have demonstrated the value of multimodal 3D MRI in predicting which DIPG patients might survive beyond 2 years. The robustness of this radiomic signature has yet to be tested using multicentric images.