An hypothesis generating study of MRI-Derived Radiomics on tumor and microenvironment tissue heterogeneity to guide post-operative management of glioblastoma: toward personalized radiation treatment volume delineation

Abstract Background The glioblastoma’s bad prognosis is primarily due to intra-tumor heterogeneity, demonstrated from several studies that collected molecular biology, cytogenetic data and more recently radiomic features for a better prognostic stratification.The GLIFA project (GLIoblastoma Feature Analysis) is a multicentric project planned to investigated the role of radiomic analysis in GBM management, to verify if radiomic features in the tissue around the resection cavity which may guide the radiation target volume delineation. Material and Methods We retrospectively analyze from three centers radiomic features extracted from 90 patients with total or near total resection, who completed the standard adjuvant treatment and for whom we had post-operative images available for features extraction.The Manual segmentation was performed on post gadolinium T1w MRI sequence by 2 radiation oncologist reviewed by a neuroradiologist, both with at least 10 years of experience. The Region of interest (ROI) considered for the analysis were: the surgical cavity +/- post-surgical residual mass (CTV_cavity); the CTV a margin of 1.5 cm added to CTV_cavity and the volume resulting from subtracting the CTV_cavity from the CTV was defined as CTV_Ring. Radiomic analysis and modelling were conducted in RStudio. Z-score normalization was applied to each radiomic feature. A radiomic model was generated using the 226 features extracted from the Ring to perform a binary classification and predict the PFS at 6 months (statistical, morphological and textural features). A 3-fold cross-validation repeated five times was implemented for internal validation of the model. Results Two-hundred and seventy ROIs were contoured. The proposed radiomic model was given by the best fitting logistic regression model, and included the following 3 features: F_cm_merged.contrast, F_cm_merged.info.corr.2, F_rlm_merged.rlnu. A good agreement between model predicted probabilities and observed outcome probabilities was obtained (p-value of 0.49 by Hosmer and Lemeshow statistical test). The ROC curve of the model reported an AUC of 0.78 (95% CI: 0.68 - 0.88). Conclusion This is the first hypothesis-generating study who applies a radiomic analysis focusing on healthy tissue ring around the surgical cavity on post-operative MRI. This study provides a preliminary model for a decision support tool for a customization of the radiation target volume in GBM patients in order to achieve a margin reduction strategy.