Improving Treatment of Glioblastoma by Distinguishing Progression From Pseudoprogression by Applying Machine Learning Techniques to Routine Clinical Data

The impact of pseudoprogression is significant on patient care and medical research. The existing evidence shows that it is feasible to use Support Vector Machine and Deep Learning classification models for predicting survival using routine MRI images as well as differentiating progression from pseudoprogression. The investigators wish to capture signal changing over time in routine MRI images using parametric response maps (via a state-of-the-art postoperative-to preoperative image registration method that they have developed) and use such classifiers to differentiate progression from pseudoprogression. The research the investigators are proposing is needed in order to provide a solution to the problem of pseudoprogression and be implemented across the NHS easily and efficiently. Importantly, this does not depend on advanced imaging techniques.

Data collected at KCH from the last 24 months shows that, even at a leading glioma imaging centre, only 66% of patients had advanced imaging (e.g. DSC-MRI) performed at the time of increase in contrast-enhancement i.e. possible progression. The primary aim of this research is to use routine clinical MRI data in order to train the classifier. This will increase the utility of the classifier, as such routine MRI data can be acquired by all imaging centres, and the new classifier can therefore provide a much more cost-efficient solution than an alternative classifier which may depend on advanced imaging techniques.

Initial training, testing and cross validation of a classification model will be carried out using MRI data of glioblastoma obtained from publicly-accessible imaging archives and King's College Hospital (KCH), London. For clinical validation, the trained model will undergo testing using MRI data from patients recruited prospectively.