Advanced imaging and artificial intelligence for diagnostic and prognostic biomarkers in glioblastoma

Conventional magnetic resonance imaging (MRI) has a pivotal role in diagnosis and post-treatment management of glioblastoma, however it has limitations. This work investigates the use of advanced MRI techniques that assess the tumour microenvironment, and artificial intelligence (AI) techniques that compute quantitative features, as potential imaging biomarkers in key clinical issues faced by clinicians, through several retrospective studies. Results show that advanced multiparametric MRI is superior to current standard-of-care imaging for the diagnosis of glioblastoma, and in treatment response assessment. Results of AI techniques on pre-operative imaging show the ability to differentiate between glioblastoma and metastasis with an accuracy of 88.7%, prediction of overall survival with a high level of accuracy, and stratification of patients into high- and low-level groups of MGMT promoter methylation with accuracies between 45-67%. In the early post-treatment phase, AI analysis of imaging can distinguish between disease progression and pseudoprogression with an accuracy of 73.7%, compared to neuroradiologist accuracy of 32.9%. Integrating these techniques into routine clinical practice is essential to improve patient outcomes. Further work is required to validate advanced imaging and AI biomarkers, towards the longer-term goal of using these as clinical decision support tools, to benefit patients with glioblastoma and other brain tumours

Distribution and fibre field similarity mapping of the human anterior commissure fibres by diffusion tensor imaging

PMID: 20229087International audienceno abstrac

Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma.

INTRODUCTION Survival varies in patients with glioblastoma due to intratumoral heterogeneity and radiomics/imaging biomarkers have potential to demonstrate heterogeneity. The objective was to combine radiomic, semantic and clinical features to improve prediction of overall survival (OS) and O-methylguanine-DNA methyltransferase (MGMT) promoter methylation status from pre-operative MRI in patients with glioblastoma. METHODS A retrospective study of 181 MRI studies (mean age 58 ± 13 years, mean OS 497 ± 354 days) performed in patients with histopathology-proven glioblastoma. Tumour mass, contrast-enhancement and necrosis were segmented from volumetric contrast-enhanced T1-weighted imaging (CE-T1WI). 333 radiomic features were extracted and 16 Visually Accessible Rembrandt Images (VASARI) features were evaluated by two experienced neuroradiologists. Top radiomic, VASARI and clinical features were used to build machine learning models to predict MGMT status, and all features including MGMT status were used to build Cox proportional hazards regression (Cox) and random survival forest (RSF) models for OS prediction. RESULTS The optimal cut-off value for MGMT promoter methylation index was 12.75%; 42 radiomic features exhibited significant differences between high and low-methylation groups. However, model performance accuracy combining radiomic, VASARI and clinical features for MGMT status prediction varied between 45 and 67%. For OS predication, the RSF model based on clinical, VASARI and CE radiomic features achieved the best performance with an average iAUC of 96.2 ± 1.7 and C-index of 90.0 ± 0.3. CONCLUSIONS VASARI features in combination with clinical and radiomic features from the enhancing tumour show promise for predicting OS with a high accuracy in patients with glioblastoma from pre-operative volumetric CE-T1WI