P10.21.A MODELLING GBM RECURRENCE - THE INFLUENCE OF PERI-LESIONAL OEDEMA AND THE DISCONNECTOME

• Published in: Neuro-oncology (Charlottesville, Va.) Vol. 26; no. Supplement_5; p. v60
• Main Authors: Tariq, M, Ruffle, J, Mohinta, S, Brothwell, M R S, Brandner, S, Nachev, P, Hyare, H
• Format: Journal Article
• Language: English
• Published: 17.10.2024
• ISSN: 1522-8517; 1523-5866
• DOI: 10.1093/neuonc/noae144.197

Abstract BACKGROUND Residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker, often involving the area of T2 signal elevation surrounding the tumour core, histopathologically representing infiltrative oedema with tumour cells. We present the first stage of a model to improve the prediction GBM recurrence by specifically investigating the perilesional oedema compartment. MATERIAL AND METHODS UK-wide sample of histologically proven IDHwt GBMs with MRIs at the time of diagnosis underwent state-of-the-art in-house tumour segmentation pipeline and radiomic feature extraction of whole-tumour, enhancing, non-enhancing and oedema components, and masked disconnectome map components. Of these, early GBM recurrence, within 6 months after completion of radiotherapy, was curated and distribution mapped across the brain. RESULTS 1464 patients were identified: male = 901, median age 63 years, average overall survival 597 days. The radiomic features: Compactness 1 and 2, Surface Area, Spherical Disproportion, Sphericity, and Maximum 2D Diameter column of the disconnectome-related perilesional oedema compartment were most predictive of survival outcome (p value <0.01). Of these, 47 patients with average overall survival 357 days and average time from end of radiotherapy to progression of 131 days were identified. CONCLUSION Our early results reveal the relationship between perilesional oedema, MRI features and survival, with disconnectome-related perilesional oedema features most predictive. This suggests that extended brain network aberrations caused by gliomas may also influence tumour recurrence. Further work will focus on the development of more robust predictive models of tumour recurrence that have potential for significant impact on surgical and radiotherapy planning.