A Position Statement on the Utility of Interval Imaging in Standard of Care Brain Tumour Management: Defining the Evidence Gap and Opportunities for Future Research

Abstract

Objectiv eTo summarise current evidence for the utility of interval imaging in monitoring disease in adult brain tumours, and to develop a position for future evidence gathering while incorporating the application of data science and health economics.MethodsExperts in ‘interval imaging’ (imaging at pre-planned time-points to assess tumour status); data science; health economics, trial management of adult brain tumours, and patient representatives convened in London, UK. The current evidence on the use of interval imaging for monitoring brain tumours was reviewed. To improve the evidence that interval imaging has a role in disease management, we discussed specific themes of data science, health economics, statistical considerations, patient and carer perspectives, and multi-centre study design. Suggestions for future studies aimed at filling knowledge gaps were discussed.ResultsMeningioma and glioma were identified as priorities for interval imaging utility analysis. The “monitoring biomarkers” most commonly used in adult brain tumour patients were standard structural MRI features. Interval imaging was commonly scheduled to provide reported imaging prior to planned, regular clinic visits. There is limited evidence relating interval imaging in the absence of clinical deterioration to management change that alters morbidity, mortality, quality of life, or resource use. Progression-free survival is confounded as an outcome measure when using structural MRI in glioma. Uncertainty from imaging causes distress for some patients and their caregivers, while for others it provides an important indicator of disease activity. Any study design that changes imaging regimens should consider the potential for influencing current or planned therapeutic trials, ensure that opportunity costs are measured, and capture indirect benefits and added value.ConclusionEvidence for the value, and therefore utility, of regular interval imaging is currently lacking. Ongoing collaborative efforts will improve trial design and generate the evidence to optimise monitoring imaging biomarkers in standard of care brain tumour management.

Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies

“…Second, contrast enhancement is biologically non-specific, which can result in false negative, false positive, and indeterminate outcomes, especially in regards to the post-treatment related pseudophenomena observed in glioblastoma patients (61). Pseudoprogression is an early post-treatment related effect characteristically appearing within 6 months of glioblastoma patients completing radiotherapy and concomitant temozolomide, whereas pseudoresponse (not examined in this systematic review) appears after patients have been treated with anti-angiogenic agents such as bevacizumab.…”

“…In these recommendations, treatment response assessment is based on simple linear metrics of contrast-enhancing tumor, specifically, the product of maximal perpendicular cross-sectional dimensions in "measurable" lesions defined as > 10 mm in all perpendicular dimensions. The recommendations are based on expert opinion informed by observational studies and derived from the biologically plausible assumption that an increase in the size of a tumor identifies disease progression, potentially resulting in a lead time improvement for therapeutic intervention before the tumor becomes clinically apparent (61). The rationale is that there may be advantages in altering management early on before the onset of irreversible disability or the tumor extent precludes intervention.…”

“…Clinical translation is far from ubiquitous (65) reflecting that further investigation and consensus standardization is required before implementing any particular widespread quantitative strategy (68). Indeed, advanced imaging is not yet recommended for determining treatment response in trials (4), and there is a lack of evidence that using advanced MRI techniques leads to a reduction in morbidity or mortality (61). However, compared to ML where accuracy-driven performance metrics have resulted in increasingly opaque models, particularly when using structural images, the underlying biological processes relating to advanced imaging appear to be well understood whilst also demonstrating high performance accuracy.…”

“…In future studies, it would be beneficial to perform analytical validation using external hold-out tests as epitomized by several studies in the current review. Using larger datasets which include a TABLE 3 | Advantages and disadvantages of using ML-based monitoring biomarkers for glioblastoma treatment response assessment (61).…”

“…wider range of tumors and mimics as well as parameters from different sequences, manufacturers coils, and thereby reduce overfitting, would also improve future studies. Multidisciplinary efforts and multicenter collaborations are therefore necessary (61). However, datasets will always be relatively small in neurooncological imaging even if distributed machine learning approaches such as federated learning, where the model comes to the data rather than the data comes to the model, overcome data sharing regulatory bottlenecks (61).…”