Earliest radiological progression in glioblastoma by multidisciplinary consensus review

How useful is contrast-enhanced MRI in the long-term surveillance of glioma? A multicentre retrospective longitudinal cohort study

OBJECTIVE

To examine whether MRI with routine gadolinium-based contrast agent (GBCA) administration in the long-term surveillance of adult-type diffuse glioma identifies tumour progression earlier than T2-weighted (T2w) and/or T2w fluid-attenuated inversion recovery (FLAIR) MRI only.

MATERIALS AND METHODS

In this longitudinal retrospective multicentre cohort study patients with histopathologically confirmed adult-type diffuse glioma and at least two years survival after diagnosis in 2009-2010 were included. Progression was determined by the treating physician or during the multidisciplinary team meeting and defined as the moment a change in treatment or follow-up was required. The primary outcome was the proportion of patients that showed an increase of abnormalities on both contrast-enhanced T1-weighted (CET1w) and T2w/T2w-FLAIR at the time of progression. Chi-square testing was performed to analyse the relationship between the detection of progression on both scan sequences, with calculating the Phi coefficient to determine the degree of association.

RESULTS

One hundred eight consecutive patients were included (58 male; 53 grade 2, 21 grade 3, 34 grade 4). Progression was present in 82 patients and was determined on both CET1w and T2w/T2w-FLAIR images in 59 patients (72.0%). In 20 patients (24.4%), progression was determined based solely on T2w/T2w-FLAIR abnormalities. Only three patients showed progression exclusively on CET1w (3.7%). There was a strong positive significant relationship between the detection of progression on both scan types (p < 0.001; Phi = 0.467).

CONCLUSION

An increase in CET1w abnormalities was generally accompanied by an increase in T2w/T2w-FLAIR abnormalities, raising the question of whether routine administration of GBCA is always necessary for long-term survivors of glioma.

KEY POINTS

Question Long-term survivors with glioma undergo many contrast-enhanced MRI scans, which involve a patient, financial, and environmental burden. Findings In almost all patients, an increase in T2w/T2w-FLAIR abnormalities was present at the time of tumour progression, mostly but not always accompanying contrast-enhancing findings. Clinical relevance T2w/T2-FLAIR MRI seems to detect glioma progression in long-term surviving patients similar to contrast-enhanced T1w MRI, raising the question of whether the routine administration of GBCA is necessary and justified in patients under long-term surveillance of glioma.

Additive Value of Magnetic Resonance Simulation Before Chemoradiation in Evaluating Treatment Response and Pseudoprogression in High-Grade Gliomas

PURPOSE

A dedicated magnetic resonance imaging simulation (MRsim) for radiation treatment (RT) planning in patients with high-grade glioma (HGG) can detect early radiologic changes, including tumor progression after surgery and before standard of care chemoradiation. This study aimed to determine the effect of using postoperative magnetic resonance imaging (MRI) versus MRsim as the baseline for response assessment and reporting pseudoprogression on follow-up imaging at 1 month (FU1) after chemoradiation.

METHODS AND MATERIALS

Histologically confirmed patients with HGG were planned for 6 weeks of RT in a prospective study for adaptive RT planning. All patients underwent postoperative MRI, MRsim, and follow-up MRI scans every 2 to 3 months. Tumor response was assessed by 3 independent blinded reviewers using Response Assessment in Neuro-Oncology criteria when baseline was either postoperative MRI or MRsim. Interobserver agreement was calculated using Light's kappa.

RESULTS

Thirty patients (median age, 60.5 years; IQR, 54.5-66.3) were included. Median interval between surgery and RT was 34 days (IQR, 27-41). Response assessment at FU1 differed in 17 patients (57%) when the baseline was postoperative MRI versus MRsim, including true progression versus partial response or stable disease in 11 (37%) and stable disease versus partial response in 6 (20%) patients. True progression was reported in 19 patients (63.3%) on FU1 when the baseline was postoperative MRI versus 8 patients (26.7%) when the baseline was MRsim (P = .004). Pseudoprogression was observed at FU1 in 12 (40%) versus 4 (13%) patients, when the baseline was postoperative MRI versus MRsim (P = .019). Interobserver agreement between observers was moderate (κ = 0.579; P < .001).

CONCLUSIONS

Our study demonstrates the value of acquiring an updated MR closer to RT in patients with HGG to improve response assessment, and accuracy in evaluation of pseudoprogression even at the early time point of first follow-up after RT. Earlier identification of patients with true progression would enable more timely salvage treatments including potential clinical trial enrollment to improve patient outcomes.

Imaging timing after surgery for glioblastoma: an evaluation of practice in Great Britain and Ireland (INTERVAL-GB)- a multi-centre, cohort study

PURPOSE

Post-operative MRI is used to assess extent of resection, monitor treatment response and detect progression in high-grade glioma. However, compliance with accepted guidelines for follow-up MRI, and impact on management/outcomes is unclear.

METHODS

Multi-center, retrospective observational cohort study of patients with confirmed WHO grade 4 glioma (August 2018-February 2019) receiving oncological treatment.

PRIMARY OBJECTIVE

investigate follow-up MRI surveillance practice and compliance with recommendations from NICE (Post-operative scan < 72h, MRI every 3-6 months) and EANO (Post-operative scan < 48h, MRI every 3 months).

RESULTS

There were 754 patients from 26 neuro-oncology centers with a median age of 63 years (IQR 54-70), yielding 10,100 (median, 12.5/person, IQR 5.2-19.4) person-months of follow-up. Of patients receiving debulking surgery, most patients had post-operative MRI within 72 h of surgery (78.0%, N = 407/522), and within 48 h of surgery (64.2%, N = 335/522). The median number of subsequent follow-up MRI scans was 1 (IQR 0-4). Compliance with NICE and EANO recommendations for follow-up MRI was 52.8% (N = 398/754) and 24.9% (N = 188/754), respectively. On multivariable Cox regression analysis, increased time spent in recommended follow-up according to NICE guidelines was associated with longer OS (HR 0.56, 95% CI 0.46-0.66, P < 0.001), but not PFS (HR 0.93, 95% CI 0.79-1.10, P = 0.349). Increased time spent in recommended follow-up according to EANO guidelines was associated with longer OS (HR 0.54, 95% CI 0.45-0.63, P < 0.001) but not PFS (HR 0.99, 95% CI 0.84-1.16, P = 0.874).

CONCLUSION

Regular surveillance follow-up for glioblastoma is associated with longer OS. Prospective trials are needed to determine whether regular or symptom-directed MRI influences outcomes.

Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms

This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals' data. All models' median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74-0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring.

Case Series: Glioblastoma Highlighting the Eye Care Provider's Role

SIGNIFICANCE

Although the incidence of glioblastoma is relatively low, patients presenting with this malignant central nervous system tumor will typically experience visual symptoms. Eye care providers are uniquely positioned to assist with the diagnosis of glioblastoma and to manage its associated visual symptoms.

PURPOSE

This case series will review the pathophysiology, diagnosis, treatment, and management of glioblastoma while highlighting the eye care provider's role over the course of the disease process.

CASE REPORTS

Three patients with glioblastoma are presented: a 63-year-old White man with new onset of headache and blurred vision upon awakening, a 65-year-old White man with a weeklong history of peripheral vision deficit and neurological symptoms, and a 65-year-old White woman with visual field deficit and visual perception difficulties related to a known history of glioblastoma.

CONCLUSIONS

It is incumbent upon eye care providers to recognize examination findings such as new neurological symptoms and/or visual field defects that may warrant additional workup. In addition, eye care providers can serve a palliative role by maximizing the patient's remaining visual function and thereby quality of life.

Robust Deep Learning-based Segmentation of Glioblastoma on Routine Clinical MRI Scans Using Sparsified Training

PURPOSE

To improve the robustness of deep learning-based glioblastoma segmentation in a clinical setting with sparsified datasets.

MATERIALS AND METHODS

In this retrospective study, preoperative T1-weighted, T2-weighted, T2-weighted fluid-attenuated inversion recovery, and postcontrast T1-weighted MRI from 117 patients (median age, 64 years; interquartile range [IQR], 55-73 years; 76 men) included within the Multimodal Brain Tumor Image Segmentation (BraTS) dataset plus a clinical dataset (2012-2013) with similar imaging modalities of 634 patients (median age, 59 years; IQR, 49-69 years; 382 men) with glioblastoma from six hospitals were used. Expert tumor delineations on the postcontrast images were available, but for various clinical datasets, one or more sequences were missing. The convolutional neural network, DeepMedic, was trained on combinations of complete and incomplete data with and without site-specific data. Sparsified training was introduced, which randomly simulated missing sequences during training. The effects of sparsified training and center-specific training were tested using Wilcoxon signed rank tests for paired measurements.

RESULTS

A model trained exclusively on BraTS data reached a median Dice score of 0.81 for segmentation on BraTS test data but only 0.49 on the clinical data. Sparsified training improved performance (adjusted P < .05), even when excluding test data with missing sequences, to median Dice score of 0.67. Inclusion of site-specific data during sparsified training led to higher model performance Dice scores greater than 0.8, on par with a model based on all complete and incomplete data. For the model using BraTS and clinical training data, inclusion of site-specific data or sparsified training was of no consequence.

CONCLUSION

Accurate and automatic segmentation of glioblastoma on clinical scans is feasible using a model based on large, heterogeneous, and partially incomplete datasets. Sparsified training may boost the performance of a smaller model based on public and site-specific data.Supplemental material is available for this article.Published under a CC BY 4.0 license.

Early Mortality of Brain Cancer Patients and its Connection to Cytomegalovirus Reactivation During Radiochemotherapy

PURPOSE

If routine diagnostics are inconclusive, neurologic deterioration and death of patients with brain cancer are attributed to tumor or therapy. Therefore, diagnosing symptoms of encephalopathy caused by human cytomegalovirus (HCMV) reactivation remains uncommon. We investigated the role of HCMV reactivation in neurologic decline and clinical outcome after the start of radiochemotherapy.

EXPERIMENTAL DESIGN

HCMV analyses and extended MRI studies including additional independent retrospective neuroradiologic evaluation were performed at predetermined intervals and in case of sudden neurologic decline for 118 adult patients: 63 histologically proven high-grade gliomas, 55 with brain metastases. Immunophenotyping from simultaneously taken whole-blood samples was carried out to detect immune cells serving as prognostic marker for HCMV-associated complications. Symptomatic viremia and overall survival (OS) were the endpoints.

RESULTS

Twenty-four percent (28/118) of all patients (12/44 glioblastoma, 3/13 anaplastic astrocytoma; 8/31 non-small cell lung cancer (NSCLC), 13/24 other brain metastases) developed HCMV-viremia during or within 4 weeks after radiotherapy; 21 of 28 patients experienced concurrent major neurologic decline, reversible by antiviral treatment. Identified by immunophenotyping, pretherapeutically low basophil counts predicted a high-risk for HCMV-associated encephalopathy (glioblastoma: P = 0.002, NSCLC: P = 0.02). Median OS was substantially reduced after HCMV-associated encephalopathy without MRI signs of tumor progression [glioblastoma: 99 vs. 570 days (calculated 1-year OS: 22% vs. 69%; P = 0.01) and NSCLC: 47 vs. 219 days (calculated 1-year OS: 0% vs. 32%; P = 0.02)].

CONCLUSIONS

For patients with brain cancer, HCMV reactivation after the start of radiochemotherapy is a frequent risk for cognitively detrimental but treatable encephalopathy and premature death. Routinely performed HCMV diagnostics, assessing basophil counts and study-based anti-viral regimens, are necessary to combat this hidden threat.See related commentary by Lawler et al., p. 3077.