P09.10.B IMAGING HABITATS CAN PREDICT EARLY PROGRESSION AND CHARACTERISE TREATMENT RESPONSE IN GLIOBLASTOMA: MULTICENTRE APPLICATION OF A NOVEL PIPELINE INCORPORATING BIOINFORMATICS AND MACHINE LEARNING
Authors: M Waqar, E Hessen, P van Houdt, D Lewis, K Li, X Zhu, A Jackson, M Iqbal, U A van der Heide, D Coope, G R Borst
Journal: Neuro-Oncology (2024)

Abstract BACKGROUND Imaging of tumour habitats is new technique in cancer imaging which segments tumours into reproducible regions with defined imaging characteristics. This produces a unique habitat profile for each tumour, allowing image-based mapping of tumour heterogeneity and monitoring of subregional/habitat treatment response. Here we describe a novel pipeline to characterise imaging habitats in glioblastoma using bioinformatics and machine learning. We apply this pipeline to multicentre pilot data from 3 distinct clinical studies to demonstrate its ability to predict early progression and response to pre and postoperative radiotherapy. MATERIAL AND METHODS Histologically-confirmed (WHO 2021) glioblastoma patients recruited to three ethically-approved prospective studies in Manchester (UK) and Amsterdam (Netherlands) were included: (1) Netherlands Cancer Institute imaging biomarker study - included double baseline scans and serial imaging during postoperative chemoradiotherapy (N=9); (2) POBIG (PreOperative Brain Irradiation in Glioblastoma) - phase I trial of single-fraction preoperative radiotherapy (N=6); (3) Manchester preoperative imaging biomarker study - advanced preoperative imaging in patients treated with standard of care (N=12). All patients underwent physiological MRI scans including diffusion/perfusion MRI. Imaging habitats were derived using a novel three-stage clustering pipeline using Relative Cerebral Blood Volume (rCBV) and the Apparent Diffusion Coefficient (ADC). RESULTS Seven imaging habitats were defined that occupied 10-16% of mean enhancing tumour volume. Habitat repeatability was investigated in study 1 by comparing mean habitat proportions across two baseline scans without intervening treatment, revealing excellent correlation (p&amp;lt;0.001). Habitats were able to identify treatment response as both postoperative (study 1) and preoperative (study 2) radiotherapy showed the same significant increase in a habitat defined by low rCBV/medium-high ADC (p=0.02). The clinical relevance of pre-treatment habitats was demonstrated in study 3 with a habitat signature of rapid early progression; 4/12 patients (33%) experienced rapid tumour progression in the time interval between the postoperative and radiotherapy planning MRI scans. Preoperatively, these patients had a higher proportion of a habitat with unrecordable rCBV (p=0.03) and a lower proportion of a habitat containing high rCBV/low ADC (p=0.11). CONCLUSION We have developed a unique pipeline to delineate tumour heterogeneity using imaging habitats in glioblastoma. Our multi-centre data demonstrates that it is applicable in real-time to identify tumour regions susceptible to treatment and patients with distinct clinical outcomes. Our methodology could facilitate personalised therapy for future patients and should be evaluated in larger interventional studies.