Post-gadolinium 3-dimensional spatial, surface, and structural characteristics of glioblastomas differentiate pseudoprogression from true tumor progression

MRI phenotypes of glioblastomas early after treatment are suggestive of overall patient survival

Background

Distinguishing true tumor progression (TP) from treatment-induced abnormalities (eg, pseudo-progression (PP) after radiotherapy) on conventional MRI scans remains challenging in patients with a glioblastoma. We aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall survival time.

Methods

Structural and perfusion MR images of 67 patients at 3 months post-radiotherapy were visually scored by a neuroradiologist. In total 23 parameters were predefined and used for hierarchical clustering analysis. Progression status was assessed based on the clinical course of each patient 9 months after radiotherapy (or latest available). Multivariable Cox regression models were used to determine the association between the phenotypes, recurrence rate, and overall survival.

Results

We established 4 subgroups with significantly different tumor MRI characteristics, representing distinct MRI phenotypes of glioblastomas: TP and PP rates did not differ significantly between subgroups. Regression analysis showed that patients in subgroup 1 (characterized by having mostly small and ellipsoid nodular enhancing lesions with some hyper-perfusion) had a significant association with increased mortality at 9 months (HR: 2.6 (CI: 1.1-6.3); P = .03) with a median survival time of 13 months (compared to 22 months of subgroup 2).

Conclusions

Our study suggests that distinct MRI phenotypes of glioblastomas at 3 months post-radiotherapy can be indicative of overall survival, but does not aid in differentiating TP from PP. The early prognostic information our method provides might in the future be informative for prognostication of glioblastoma patients.

Selective vulnerability of brainstem and cervical spinal cord regions in people with non-progressive multiple sclerosis of Black or African American and European ancestry

Background:

We evaluated imaging features suggestive of neurodegeneration within the brainstem and upper cervical spinal cord (UCSC) in non-progressive multiple sclerosis (MS).

Methods:

Standardized 3-Tesla three-dimensional brain magnetic resonance imaging (MRI) studies were prospectively acquired. Rates of change in volume, surface texture, curvature were quantified at the pons and medulla-UCSC. Whole and regional brain volumes and T2-weighted lesion volumes were also quantified. Independent regression models were constructed to evaluate differences between those of Black or African ancestry (B/AA) and European ancestry (EA) with non-progressive MS.

Results:

209 people with MS (pwMS) having at least two MRI studies, 29% possessing 3–6 timepoints, resulted in 487 scans for analysis. Median follow-up time between MRI timepoints was 1.33 (25th–75th percentile: 0.51–1.98) years. Of 183 non-progressive pwMS, 88 and 95 self-reported being B/AA and EA, respectively. Non-progressive pwMS demonstrated greater rates of decline in pontine volume ( p < 0.0001) in B/AA and in medulla-UCSC volume ( p < 0.0001) for EA pwMS. Longitudinal surface texture and curvature changes suggesting reduced tissue integrity were observed at the ventral medulla-UCSC ( p < 0.001), dorsal pons ( p < 0.0001) and dorsal medulla ( p < 0.0001) but not the ventral pons ( p = 0.92) between groups.

Conclusions:

Selectively vulnerable regions within the brainstem-UCSC may allow for more personalized approaches to disease surveillance and management.

Direction and magnitude of displacement differ between slowly expanding and non-expanding multiple sclerosis lesions as compared to small vessel disease

BACKGROUND AND PURPOSE

Differentiating between multiple sclerosis (MS) and small vessel disease (SVD) lesions represents a key challenge in the day-to-day management of patients. We aimed to distinguish between MS and SVD by identifying the dynamics of lesion movement patterns between enlarging and contracting foci from two MRI time points.

METHODS

Standardized 3-Tesla 3-dimensional brain magnetic resonance imaging (MRI) studies were performed at two time points on enrolled MS and SVD patients. Selected supratentorial lesions were segmented and longitudinal changes in the direction of lesion displacement and magnitude along with the evolution of contracting and expanding T1-weighted and T2-weighted MS lesions were quantified based on lesion centroid positioning. Bayesian linear mixed effects regression models were constructed to evaluate associations between changes in lesion transitions and disease state.

RESULTS

A total of 420 lesions were analyzed from 35 MS (female (F):22 (62.9%); median age (range):38 years (y) (22-61), median disease duration:7.38y (0.38-20.99)) and 12 SVD patients (F:11 (100%); 54y (40-66)). MS T2-weighted lesions that increased in volume between MRI time points demonstrated movement toward the cortex (p = 0.01), whereas those that decreased in volume moved toward the center (p < 0.0001). Lesion volume changes related to SVD demonstrated no effect on movement direction over time. Both expanding (p = 0.03) and contracting (p = 0.01) MS lesions demonstrated greater distances between centroids when compared to SVD.

CONCLUSION

Lesion dynamics may reveal distinct characteristics associated with the biology of disease while providing further insights into the behavior of inflammatory CNS disorders.

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

OBJECTIV E

To 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.

METHODS

Experts 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.

RESULTS

Meningioma 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.

CONCLUSION

Evidence 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.

Local treatment for relapsing glioblastoma: A decision-making tree for choosing between reirradiation and second surgery

In case of circumscribed recurrent glioblastoma (rec-GBM), a second surgery (Re-S) and reirradiation (Re-RT) are local strategies to consider. The aim is to provide an algorithm to use in the daily clinical practice. The first step is to consider the life expectancy in order to establish whether the patient should be a candidate for active treatment. In case of a relatively good life expectancy (>3 months) and a confirmed circumscribed disease(i.e. without multiple lesions that are in different lobes/hemispheres), the next step is the assessment of the prognostic factors for local treatments. Based on the existing prognostic score systems, patients who should be excluded from local treatments may be identified; based on the validated prognostic factors, one or the other local treatment may be preferred. The last point is the estimation of expected toxicity, considering patient-related, tumor-related and treatment-related factors impacting on side effects. Lastly, patients with very good prognostic factors may be considered for receiving a combined treatment.

Utility of shape evolution and displacement in the classification of chronic multiple sclerosis lesions

The accurate recognition of multiple sclerosis (MS) lesions is challenged by the high sensitivity and imperfect specificity of MRI. To examine whether longitudinal changes in volume, surface area, 3-dimensional (3D) displacement (i.e. change in lesion position), and 3D deformation (i.e. change in lesion shape) could inform on the origin of supratentorial brain lesions, we prospectively enrolled 23 patients with MS and 11 patients with small vessel disease (SVD) and performed standardized 3-T 3D brain MRI studies. Bayesian linear mixed effects regression models were constructed to evaluate associations between changes in lesion morphology and disease state. A total of 248 MS and 157 SVD lesions were studied. Individual MS lesions demonstrated significant decreases in volume < 3.75mm³ (p = 0.04), greater shifts in 3D displacement by 23.4% with increasing duration between MRI time points (p = 0.007), and greater transitions to a more non-spherical shape (p < 0.0001). If 62.2% of lesions within a given MRI study had a calculated theoretical radius > 2.49 based on deviation from a perfect 3D sphere, a 92.7% in-sample and 91.2% out-of-sample accuracy was identified for the diagnosis of MS. Longitudinal 3D shape evolution and displacement characteristics may improve lesion classification, adding to MRI techniques aimed at improving lesion specificity.

African Americans experience disproportionate neurodegenerative changes in the medulla and upper cervical spinal cord in early multiple sclerosis

OBJECTIVE

To compare the temporal changes in the 3-dimensional (3D) structure of the medulla-upper cervical spinal cord region in African American (AA) and white multiple sclerosis (MS) patients to identify early patterns of anatomical change prior to progressive symptom development.

METHODS

Standardized 3-Tesla 3D brain MRI studies were performed at two time points on AA and white MS patients along with controls. Longitudinal changes in volume, surface area, tissue compliance, and surface texture measured in total and within ventral and dorsal compartments were studied. Independent regression models were constructed to evaluate differences between groups.

RESULTS

Thirty-five individuals were studied, 10 AA with MS (female (F): 8; median age [IQR]=33.8 years (y) [10.9], median disease duration: 11.8y [11.3]), 20 white MS patients (F: 10; 35.6y [17.4], 7.23y [8.83], and 5 controls (F: 2, 51.8y [10.2]). Expanded Disability Status Scale scores were 0.0 at baseline and at the second MRI time point. Within the medulla-upper cervical spinal cord, AA versus white MS patients exhibited greater rates of atrophy in total (p<0.0001) and within the ventral (p<0.0001) and dorsal (p<0.0001) compartments, reduced surface area (p<0.0001), and reduced tissue compliance in the ventral (p=0.002) and dorsal (p=0.0005) compartments. The rate of change at the dorsal surface, but not the ventral surface, between MRI time points was also greater in AA relative to white MS patients (p<0.0001).

CONCLUSION

Structural changes in distinct anatomical regions of the medulla-upper cervical spinal cord may be reflective of early and disproportionate neurodegeneration in AA MS as compared to whites.

Disparities in glioblastoma survival by case volume: a nationwide observational study

INTRODUCTION

High hospital case volumes are associated with improved treatment outcomes for numerous diseases. We assessed the association between academic non-profit hospital case volume and survival of adult glioblastoma patients.

METHODS

From the nationwide Finnish Cancer Registry, we identified all adult (≥ 18 years) patients with histopathological diagnoses of glioblastoma from 2000 to 2013. Five university hospitals (treating all glioblastoma patients in Finland) were classified as high-volume (one hospital), middle-volume (one hospital), and low-volume (three hospitals) based on their annual numbers of cases. We estimated one-year survival rates, estimated median overall survival times, and compared relative excess risk (RER) of death between high, middle, and low-volume hospitals.

RESULTS

A total of 2,045 patients were included. The mean numbers of annually treated patients were 54, 40, and 17 in the high, middle, and low-volume hospitals, respectively. One-year survival rates and median survival times were higher and longer in the high-volume (39%, 9.3 months) and medium-volume (38%, 8.9 months) hospitals than in the low-volume (32%, 7.8 months) hospitals. RER of death was higher in the low-volume hospitals than in the high-volume hospital (RER = 1.19, 95% CI 1.07-1.32, p = 0.002). There was no difference in RER of death between the high-volume and medium-volume hospitals (p = 0.690).

CONCLUSION

Higher glioblastoma case volumes were associated with improved survival. Future studies should assess whether this association is due to differences in patient-specific factors or treatment quality.

Machine learning and glioma imaging biomarkers

AIM

To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring.

MATERIALS AND 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 (MRI) 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, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI 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 ML in glioma imaging).

CONCLUSION

Although pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.

Current and Future Imaging Methods for Evaluating Response to Immunotherapy in Neuro-Oncology

Imaging plays a central role in evaluating responses to therapy in neuro-oncology patients. The advancing clinical use of immunotherapies has demonstrated that treatment-related inflammatory responses mimic tumor growth via conventional imaging, thus spurring the development of new imaging approaches to adequately distinguish between pseudoprogression and progressive disease. To this end, an increasing number of advanced imaging techniques are being evaluated in preclinical and clinical studies. These novel molecular imaging approaches will serve to complement conventional response assessments during immunotherapy. The goal of these techniques is to provide definitive metrics of tumor response at earlier time points to inform treatment decisions, which has the potential to improve patient outcomes. This review summarizes the available immunotherapy regimens, clinical response criteria, current state-of-the-art imaging approaches, and groundbreaking strategies for future implementation to evaluate the anti-tumor and immune responses to immunotherapy in neuro-oncology applications.

Quantifying effects of radiotherapy-induced microvascular injury; review of established and emerging brain MRI techniques

Microvascular changes are increasingly recognised not only as primary drivers of radiotherapy treatment response in brain tumours, but also as an important contributor to short- and long-term (cognitive) side effects arising from irradiation of otherwise healthy brain tissue. As overall survival of patients with brain tumours is increasing, monitoring long-term sequels of radiotherapy-induced microvascular changes in the context of their potential predictive power for outcome, such as cognitive disability, has become increasingly relevant. Ideally, radiotherapy-induced significant microvascular changes in otherwise healthy brain tissue should be identified as early as possible to facilitate adaptive radiotherapy and to proactively start treatment to minimise the influence on these side-effects on the final outcome. Although MRI is already known to be able to detect significant long-term radiotherapy induced microvascular effects, more recently advanced MR imaging biomarkers reflecting microvascular integrity and function have been reported and might provide a more accurate and earlier detection of microvascular changes. However, the use and validation of both established and new techniques in the context of monitoring early and late radiotherapy-induced microvascular changes in both target-tissue and healthy tissue currently are minimal at best. This review aims to summarise the performance and limitations of existing methods and future opportunities for detection and quantification of radiotherapy-induced microvascular changes, as well as the relation of these findings with key clinical parameters.