Abstract #3078

Integrating Machine Learning and Image Inpainting to Predict Tumour Invasion in Glioblastoma using multi-parametric MRI

Chao Li^1,2,3, Pan Liu³, Shuo Wang^3,4, Carola-Bibiane Schönlieb³, and Stephen John Price¹

¹Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom, ²Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ³The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, United Kingdom, ⁴Department of Radiology, University of Cambridge, Cambridge, United Kingdom

The multi-parametric MRI has the potential to compensate for the non-specific contrast-enhancing imaging in delineating tumor margin. The purpose of this study was to propose a method by integrating machine learning with image inpainting to predict the glioblastoma invasion using advanced multi-parametric MRI. The predictive tumor regions using this approach showed significance for patient prognosis, in a cohort containing 115 glioblastoma patients. This approach could advance the scenario of mathematical image analysis by considering both imaging features and brain structure. The predictive region may have significant clinical impact on personalized and targeted surgical treatment of patients.

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