P14.20.A PREDICTION OF RELAPSE PATTERNS IN ASTROCYTOMA GRADE 4 IDHM AND GLIOBLASTOMA BASED ON BREAKTHROUGH PERFUSION SEQUENCES
Authors: Satragno C , El Aichi M , Sambourg K , Dautun L , Carre A , Veres C , Cifliku V , Hachemi T , Yuste C , Mrissa L , Bockel S , Vakalopoulou M , Dhermain F , Deutsch E , Robert C 
Published: October 2024

BACKGROUND

Several studies have highlighted the potential of assessing tissue vascularity in brain cancer management. However, conventional T2w-Dynamic Susceptibility Contrast (DSC) perfusion maps have limited predictive power for relapse patterns in grade 4 astrocytoma IDHm and glioblastoma. Newly proposed biomarkers offer deeper insights into hemodynamic parameters. In this study, we evaluated the ability of these biomarkers to predict the active tumor regions at relapse based on standard pre-radiotherapy (preRT) T2*w-DSC sequences after surgery/biopsy.

MATERIAL AND METHODS

Our cohort study comprised 104 patients diagnosed with IDHm astrocytoma and GB (2009 - 2015). All underwent preRT scans including a T2*w-DSC sequence. Cercare Medical Neurosuite was used to generate conventional perfusion maps like rCBV as well as more innovative ones, including the Capillary Transit Time Heterogeneity (CTH) map, which reflects the morphology in the capillary bed. For each patient, the MR images at the time of first relapse were also recovered. For both time points, the active (L3) and necrotic (L1) regions of the lesion were automatically annotated, then manually corrected by oncologists based on MR sequences. A rigid registration was applied to align perfusion maps and L3 relapse (L3R) labels with the preRT T1 imaging to ensure spatial coherence. We also created a new label L3R-(L1+L3) which describes the previously healthy tissue that became active at the time of relapse. To account for uncertainties in label annotation and image registration, the final perfusion maps were created by convolving the software outputs through 3D mean filters, and the labels were expanded by applying a 3D mean filter and then applying a threshold to obtain a binary output. Finally, for each patient, feature, and label, we plotted the ROC curves, computed the AUC, and determined the optimal cutoff using Youden’s Index.

RESULTS

The results indicated that CTH had higher predictive value than the other biomarkers, with AUC of 0.70+/-0.16 (mean +/- SD) for the prediction of L3R, and an optimal cutoff point of 2.53. The respective values were equal to 0.68 +/- 0.17, and 2.45 for L3R-(L1+L3). Notably, these results outperformed conventional rCBV maps, with an average AUC of 0.51+/-0.16 for L3R and 0.49+/-0.16 for L3R-(L1+L3).

CONCLUSION

Results suggested that CTH, outperforms traditional biomarkers such as rCBV in predicting active tumor regions at the time of relapse. Future work will focus on validating these biomarkers in larger cohorts and refining the data processing to improve prediction precision and recall. Also, combining input parameters could be investigated, since the combined macro- and microvasculature-weighting information contained in, such as, rCBV and CTH, could yield more comprehensive insights into the intricate growth patterns of high-grade cerebral cancers.