Machine Learning and Glioma Imaging BiomarkersBackground: Increased computational processing power and advances in database curation will facilitate the development of biomarkers that may contribute to the defeat of cancer in the mid-21 stcentury.Aim: We review how machine learning is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis and treatment response monitoring.Methods: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high grade glioma biomarkers for treatment response monitoring, prognosis and prediction.Results: Magnetic resonance imaging is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, machine learning is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, machine learning also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade and prognosis using magnetic resonance images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of machine learning in glioma imaging).Conclusion: Whilst pioneering, most of the evidence is of a low level having been obtained retrospectively and in single centres. Studies applying machine learning to build neuro-oncology monitoring biomarker models have yet to show overall advantage over those using traditional statistical methods. Development and validation of machine learning models applied to neurooncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.

Objectives MRI remains the preferred imaging investigation for glioblastoma. Appropriate and timely neuroimaging in the follow-up period is considered to be important in making management decisions. There is a paucity of evidence-based information in current UK, European and international guidelines regarding the optimal timing and type of neuroimaging following initial neurosurgical treatment. This study assessed the current imaging practices amongst UK neuro-oncology centres, thus providing baseline data and informing future practice. Methods The lead neuro-oncologist, neuroradiologist and neurosurgeon from every UK neuro-oncology centre were invited to complete an online survey. Participants were asked about current and ideal imaging practices following initial treatment. Results Ninety-two participants from all 31 neuro-oncology centres completed the survey (100% response rate). Most centres routinely performed an early post-operative MRI (87%, 27/31), whereas only a third performed a pre-radiotherapy MRI (32%, 10/31). The number and timing of scans routinely performed during adjuvant TMZ treatment varied widely between centres. At the end of the adjuvant period, most centres performed an MRI (71%, 22/31), followed by monitoring scans at 3 monthly intervals (81%, 25/31). Additional short-interval imaging was carried out in cases of possible pseudoprogression in most centres (71%, 22/31). Routine use of advanced imaging was infrequent; however, the addition of advanced sequences was the most popular suggestion for ideal imaging practice, followed by changes in the timing of EPMRI. Conclusion Variations in neuroimaging practices exist after initial glioblastoma treatment within the UK. Multicentre, longitudinal, prospective trials are needed to define the optimal imaging schedule for assessment. Key Points • Variations in imaging practices exist in the frequency, timing and type of interval neuroimaging after initial treatment of glioblastoma within the UK. • Large, multicentre, longitudinal, prospective trials are needed to define the optimal imaging schedule for assessment.