Title: Improving treatment of glioblastoma: validating the efficacy of a machine learning model to detect pseudoprogression
Funder: Department of Health and Social Care

Abstract: Background Glioblastoma has one of the poorest prognoses of all cancers. Typical treatment involves surgical resection combined with radiotherapy and concomitant and adjuvant temozolomide chemotherapy. This regimen of treatment is associated with pseudoprogression: the false appearance of progressive disease manifesting as an increase in the size of the contrast-enhancing area on conventional, follow-up, magnetic resonance imaging (MRI). This confounds response assessment and may affect clinical management. Research question This project aims to improve the accuracy of distinguishing pseudoprogression from true progression using routine MR imaging and clinical data. The project specifically aims to use routine, rather than more advanced MR imaging techniques, in order to allow translation of the resulting analysis package into clinical practice. Aims and objectives A machine learning model for pseudoprogression will be trained using retrospective patient MRI and clinical data from King's College Hospital. This work will be facilitated by the newly-founded London Medical Imaging and AI Centre for Value-Based Healthcare. A novel MRI radiomics phantom will be designed in order to quantify the robustness of imaging features across MRI scanners and institutions. This will then inform the development of the machine learning model in order to improve its robustness to data from multiple sources. The trained model will be externally validated on an external validation cohort of patients prospectively recruited from King's College Hospital and nine participating sites. Methods Candidate machine learning models will be trained on MRI and clinical features from a retrospective dataset of 450 patients. According to the 'no free lunch theorem', it is not possible to know a priori which model or hyperparameters would best solve a given problem. As such I will evaluate empirically several supervised machine learning methods. Routine follow-up structural imaging will be used as the standard to define the treatment response and it is against this standard that the accuracy of each classifier will be clinically validated. The MR images will first be segmented and then radiomic imaging features will be extracted. Candidate models will be trained and tested with the integration of routinely collected non-imaging features. The non-imaging features include tumour location, radiotherapy dose map, Karnofsky Performance Status, surgery type, molecular markers and demographic data. An MRI radiomics phantom will measure the robustness of the imaging features. Those shown to be not robust to variations in MRI scanners or institution will be excluded. We will implement a strategy to mitigate the variation in the remaining features through image pre-processing techniques. We will determine the performance of the final model using an external validation cohort and evaluate the sensitivity, specificity, positive predictive value, negative predictive value and accuracy. Patients will be recruited from King's College Hospital and 9 other participating sites if they are undergoing the Stupp regimen for the treatment of glioblastoma and are aged 18-80.Anticipated impact and dissemination The results from this project will be used to inform a separate future Health Technology Assessment study in collaboration with the King's Technology Evaluation Centre, in order to promote uptake of the technology.

Lay abstract: Aims The aim of this study is to provide doctors with a validated computer program that will use images of the brain (and other medical information) that are routinely obtained throughout treatment, in order to help them more accurately identify when a cancer does and does not regrow. Background Glioblastoma is the most aggressive kind of brain cancer and leads on average to 20 years of life lost, more than any other cancer. Images of the brain are taken before the operation, and every few months after treatment, to see if the tumour regrows. The similarity of the appearance of the treatment side-effects to cancer is confusing and is known as pseudoprogression. If doctors mistake the appearance of treatment side-effects for growing cancer, they can think that the treatment is failing and may change the patient's treatment too early to a treatment that they don't need. This means that patients may not be given the full treatment that they need. If cancer could be correctly identified in these cases, there would be a positive impact both on the quality of the patients' lives and the effectiveness of treatment in terms of overall survival. Design & Methods Normally, computer programs operate following a set of hard-coded rules, which allow them to solve specific problems. 'Machine learning' is an alternative approach whereby computer programs are given real-world examples of data to learn from; once trained, the software can then apply what has been learned to new situations. The first phase of the project will be spent training a machine learning program using existing patient data from King's College Hospital's Neuroradiology Department. We have already collected this data (450 patients including data about their treatment, images, and surgery) and it is larger and more diverse than any previously published glioblastoma dataset. During this phase, we will use a test object to measure and mitigate the variability in images across different MRI scanners and integrate this into our program. The second phase will test the robustness of the machine learning program using prospective patient data from all our collaborating sites in order to measure the clinical efficacy of the program. We are asking patients who are referred to King's College Hospital and our nine other participating sites for treatment to allow access to their relevant medical data. There is no additional demand on the patient's time nor any participant burden. HRA approval has been granted for this research. Patient and public involvement The Brain Tumour Charity; The Brainstrust, a UK-based brain cancer support charity; PPI representatives from the Royal College of Radiologists, the NIHR CRN Consumer Liaison Group, and the UK Database of Uncertainties about the Effects of Treatments, reviewed the proposal and highlighted the impact that the uncertainty of pseudoprogression had on the quality of life of patients and their family members. Our research proposal has been fully supported and improved following their feedback.Dissemination The research will be published in high-impact academic journals, as well as being presented at scientific meetings. The research outcomes will also be publicly available in the 'Making a Difference' e-zine, which showcases the research that takes place within King's College London.PPI groups, and specifically the Braintrust charity and The Brain Tumour Charity - with whom we have collaborated to gain patient and public feedback in the development of this proposal - will be actively involved in disseminating the research findings. In particular, their input will be helpful in communicating the scientific and computing aspects of the research to a lay audience, as well as being able to target the communications to appropriate audiences.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS