Differentiating pseudoprogression from true progression: analysis of radiographic, biologic, and clinical clues in GBM

Distinguishing glioblastoma progression from treatment-related changes using DTI directionality growth analysis

BACKGROUND

It is difficult to distinguish between tumor progression (TP) and treatment-related abnormalities (TRA) in treated glioblastoma patients via conventional MRI, but this distinction is crucial for treatment decision making. Glioblastoma is known to exhibit an invasive growth pattern along white matter architecture and vasculature. This study quantified lesion development patterns in treated glioblastoma lesions and their relation to white matter microstructure to distinguish TP from TRA.

MATERIALS AND METHODS

Glioblastoma patients with confirmed TP or TRA with T1-weighted contrast-enhanced and DTI MR scans from two posttreatment follow-up timepoints were reviewed. The contrast-enhancing regions were segmented, and the regions were coregistered to the DTI data. Lesion increase vectors were categorized into two groups: parallel (0-20 degrees) and perpendicular (70-90 degrees) to white matter. FA-values were also extracted. To test for a statistically significant difference between the TP and TRA groups, a Mann‒Whitney U test was performed.

RESULTS

Of 73 glioblastoma patients, fifteen were diagnosed with TRA, whereas 58 patients suffered TP. TP had a 25.8% (95% CI 24.1%-27.6%) increase in parallel lesions, and TRA had a 25.4% (95% CI 20.9%-29.9%) increase in parallel lesions. The perpendicular increase was 14.7% for TP (95% CI 13.0%-16.4%) and 18.0% (95% CI 13.5%-22.5%) for TRA. These results were not significantly different (p = 0.978). FA value for TP showed to be 0.248 (SD = 0.054) and for TRA it was 0.231 (SD = 0.075), showing no statistically significant difference (p = 0.121).

CONCLUSIONS

Based on our results, quantifying posttreatment contrast-enhancing lesion development directionality with DTI in glioblastoma patients does not appear to effectively distinguish between TP and TRA.

Incidence and outcome of pseudoprogression after radiation therapy in glioblastoma patients: A cohort study

Background

Differentiating post-radiation MRI changes from progressive disease (PD) in glioblastoma (GBM) patients represents a major challenge. The clinical problem is two-sided; avoid termination of effective therapy in case of pseudoprogression (PsP) and continuation of ineffective therapy in case of PD. We retrospectively assessed the incidence, management, and prognostic impact of PsP and analyzed factors associated with PsP in a GBM patient cohort.

Methods

Consecutive GBM patients diagnosed in the South-Eastern Norway Health Region from 2015 to 2018 who had received RT and follow-up MRI were included. Tumor, patient, and treatment characteristics were analyzed in relationship to re-evaluated MRI examinations at 3 and 6 months post-radiation using Response Assessment in Neuro-Oncology criteria.

Results

A total of 284 patients were included in the study. PsP incidence 3 and 6 months post-radiation was 19.4% and 7.0%, respectively. In adjusted analyses, methylated O6-methylguanine-DNA methyltransferase (MGMT) promoter and the absence of neurological deterioration were associated with PsP at both 3 (p < .001 and p = .029, respectively) and 6 months (p = .045 and p = .034, respectively) post-radiation. For patients retrospectively assessed as PD 3 months post-radiation, there was no survival benefit of treatment change (p = .838).

Conclusions

PsP incidence was similar to previous reports. In addition to the previously described correlation of methylated MGMT promoter with PsP, we also found that absence of neurological deterioration significantly correlated with PsP. Continuation of temozolomide courses did not seem to compromise survival for patients with PD at 3 months post-radiation; therefore, we recommend continuing adjuvant temozolomide courses in case of inconclusive MRI findings.

Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review

Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.

Assessment of treatment response to dendritic cell vaccine in patients with glioblastoma using a multiparametric MRI-based prediction model

PURPOSE

Autologous tumor lysate-loaded dendritic cell vaccine (DCVax-L) is a promising treatment modality for glioblastomas. The purpose of this study was to investigate the potential utility of multiparametric MRI-based prediction model in evaluating treatment response in glioblastoma patients treated with DCVax-L.

METHODS

Seventeen glioblastoma patients treated with standard-of-care therapy + DCVax-L were included. When tumor progression (TP) was suspected and repeat surgery was being contemplated, we sought to ascertain the number of cases correctly classified as TP + mixed response or pseudoprogression (PsP) from multiparametric MRI-based prediction model using histopathology/mRANO criteria as ground truth. Multiparametric MRI model consisted of predictive probabilities (PP) of tumor progression computed from diffusion and perfusion MRI-derived parameters. A comparison of overall survival (OS) was performed between patients treated with standard-of-care therapy + DCVax-L and standard-of-care therapy alone (external controls). Additionally, Kaplan-Meier analyses were performed to compare OS between two groups of patients using PsP, Ki-67, and MGMT promoter methylation status as stratification variables.

RESULTS

Multiparametric MRI model correctly predicted TP + mixed response in 72.7% of cases (8/11) and PsP in 83.3% (5/6) with an overall concordance rate of 76.5% with final diagnosis as determined by histopathology/mRANO criteria. There was a significant concordant correlation coefficient between PP values and histopathology/mRANO criteria (r = 0.54; p = 0.026). DCVax-L-treated patients had significantly prolonged OS than those treated with standard-of-care therapy (22.38 ± 12.8 vs. 13.8 ± 9.5 months, p = 0.040). Additionally, glioblastomas with PsP, MGMT promoter methylation status, and Ki-67 values below median had longer OS than their counterparts.

CONCLUSION

Multiparametric MRI-based prediction model can assess treatment response to DCVax-L in patients with glioblastoma.

Management of initial and recurrent radiation-induced contrast enhancements following radiotherapy for brain metastases: Clinical and radiological impact of bevacizumab and corticosteroids

Purpose

The appearance of radiation-induced contrast enhancements (RICE) after radiotherapy for brain metastases can go along with severe neurological impairments. The aim of our analysis was to evaluate radiological changes, the course and recurrence of RICE and identify associated prognostic factors.

Methods

We retrospectively identified patients diagnosed with brain metastases, who were treated with radiotherapy and subsequently developed RICE. Patient demographic and clinical data, radiation-, cancer-, and RICE-treatment, radiological results, and oncological outcomes were reviewed in detail.

Results

A total of 95 patients with a median follow-up of 28.8 months were identified. RICE appeared after a median time of 8.0 months after first radiotherapy and 6.4 months after re-irradiation. Bevacizumab in combination with corticosteroids achieved an improvement of clinical symptoms and imaging features in 65.9% and 75.6% of cases, respectively, both significantly superior compared to treatment with corticosteroids only, and further significantly prolonged RICE-progression-free survival to a median of 5.6 months. Recurrence of RICE after initially improved or stable imaging occurred in 63.1% of cases, significantly more often in patients after re-irradiation and was associated with high mortality of 36.6% after the diagnosis of flare-up. Response of recurrence significantly depended on the applied treatment and multiple courses of bevacizumab achieved good response.

Conclusion

Our results suggest that bevacizumab in combination with corticosteroids is superior in achieving short-term imaging and symptom improvement of RICE and prolongs the progression-free time compared to corticosteroids alone. Long-term RICE flare-up rates after bevacizumab discontinuation are high, but repeated treatments achieved effective symptomatic control.

Diffusion basis spectrum imaging as an adjunct to conventional MRI leads to earlier diagnosis of high-grade glioma tumor progression versus treatment effect

Background

Following chemoradiotherapy for high-grade glioma (HGG), it is often challenging to distinguish treatment changes from true tumor progression using conventional MRI. The diffusion basis spectrum imaging (DBSI) hindered fraction is associated with tissue edema or necrosis, which are common treatment-related changes. We hypothesized that DBSI hindered fraction may augment conventional imaging for earlier diagnosis of progression versus treatment effect.

Methods

Adult patients were prospectively recruited if they had a known histologic diagnosis of HGG and completed standard-of-care chemoradiotherapy. DBSI and conventional MRI data were acquired longitudinally beginning 4 weeks post-radiation. Conventional MRI and DBSI metrics were compared with respect to their ability to diagnose progression versus treatment effect.

Results

Twelve HGG patients were enrolled between August 2019 and February 2020, and 9 were ultimately analyzed (5 progression, 4 treatment effect). Within new or enlarging contrast-enhancing regions, DBSI hindered fraction was significantly higher in the treatment effect group compared to progression group (P = .0004). Compared to serial conventional MRI alone, inclusion of DBSI would have led to earlier diagnosis of either progression or treatment effect in 6 (66.7%) patients by a median of 7.7 (interquartile range = 0-20.1) weeks.

Conclusions

In the first longitudinal prospective study of DBSI in adult HGG patients, we found that in new or enlarging contrast-enhancing regions following therapy, DBSI hindered fraction is elevated in cases of treatment effect compared to those with progression. Hindered fraction map may be a valuable adjunct to conventional MRI to distinguish tumor progression from treatment effect.

DEGRO practical guideline for central nervous system radiation necrosis part 1: classification and a multistep approach for diagnosis

PURPOSE

The Working Group for Neuro-Oncology of the German Society for Radiation Oncology in cooperation with members of the Neuro-Oncology Working Group of the German Cancer Society aimed to define a practical guideline for the diagnosis and treatment of radiation-induced necrosis (RN) of the central nervous system (CNS).

METHODS

Panel members of the DEGRO working group invited experts, participated in a series of conferences, supplemented their clinical experience, performed a literature review, and formulated recommendations for medical treatment of RN including bevacizumab in clinical routine.

CONCLUSION

Diagnosis and treatment of RN requires multidisciplinary structures of care and defined processes. Diagnosis has to be made on an interdisciplinary level with the joint knowledge of a neuroradiologist, radiation oncologist, neurosurgeon, neuropathologist, and neuro-oncologist. A multistep approach as an opportunity to review as many characteristics as possible to improve diagnostic confidence is recommended. Additional information about radiotherapy (RT) techniques is crucial for the diagnosis of RN. Misdiagnosis of untreated and progressive RN can lead to severe neurological deficits. In this practice guideline, we propose a detailed nomenclature of treatment-related changes and a multistep approach for their diagnosis.

RADIomic Spatial TexturAl descripTor (RADISTAT): Quantifying spatial organization of imaging heterogeneity associated with tumor response to treatment

Localized disease heterogeneity on imaging extracted via radiomics approaches have recently been associated with disease prognosis and treatment response. Traditionally, radiomics analyses leverage texture operators to derive voxel- or region-wise feature values towards quantifying subtle variations in image appearance within a region-of-interest (ROI). With the goal of mining additional voxel-wise texture patterns from radiomic expression maps, we introduce a new RADIomic Spatial TexturAl descripTor (RADISTAT). This was driven by the hypothesis that quantifying spatial organization of texture patterns within an ROI could allow for better capturing interactions between different tissue classes present in a given region; thus enabling more accurate characterization of disease or response phenotypes. RADISTAT involves: (a) robustly identifying sub-compartments of low, intermediate, and high radiomic expression (i.e. heterogeneity) in a feature map and (b) quantifying spatial organization of sub-compartments via graph interactions. RADISTAT was evaluated in two clinically challenging problems: (1) discriminating nodal/distant metastasis from metastasis-free rectal cancer patients on post-chemoradiation T2w MRI, and (2) distinguishing tumor progression from pseudo-progression in glioblastoma multiforme using post-chemoradiation T1w MRI. Across over 800 experiments, RADISTAT yielded a consistent discriminatory signature for tumor progression (GBM) and disease metastasis (RCa); where its sub-compartments were associated with pathologic tissue types (fibrosis or tumor, determined via fusion of MRI and pathology). In a multi-institutional setting for both clinical problems, RADISTAT resulted in higher classifier performance (11% improvement in AUC, on average) compared to radiomic descriptors. Furthermore, combining RADISTAT with radiomic descriptors resulted in significantly improved performance compared to using radiomic descriptors alone.

Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review

Background: Glioblastoma is the most frequent malignant primitive brain tumor in adults. The treatment includes surgery, radiotherapy, and chemotherapy. During follow-up, combined chemoradiotherapy can induce treatment-related changes mimicking tumor progression on medical imaging, such as pseudoprogression (PsP). Differentiating PsP from true progression (TP) remains a challenge for radiologists and oncologists, who need to promptly start a second-line treatment in the case of TP. Advanced magnetic resonance imaging (MRI) techniques such as diffusion-weighted imaging, perfusion MRI, and proton magnetic resonance spectroscopic imaging are more efficient than conventional MRI in differentiating PsP from TP. None of these techniques are fully effective, but current advances in computer science and the advent of artificial intelligence are opening up new possibilities in the imaging field with radiomics (i.e., extraction of a large number of quantitative MRI features describing tumor density, texture, and geometry). These features are used to build predictive models for diagnosis, prognosis, and therapeutic response. Method: Out of 7350 records for MR spectroscopy, GBM, glioma, recurrence, diffusion, perfusion, pseudoprogression, radiomics, and advanced imaging, we screened 574 papers. A total of 228 were eligible, and we analyzed 72 of them, in order to establish the role of each imaging modality and the usefulness and limitations of radiomics analysis.

Determining the optimum tumor control probability model in radiotherapy of glioblastoma multiforme using magnetic resonance imaging data pre- and post- radiation therapy

Background:

Glioblastoma multiforme (GBM) is the most common and malignant brain tumor. The current standard of care is surgery followed by radiation therapy (RT). Radiotherapy treatment plan evaluation relies on radiobiological models for accurate estimation of tumor control probability (TCP). This study aimed to assess the impact of obtained magnetic resonance imaging (MRI) data before and 12 weeks after RT to achieve the optimum TCP model to improve dose prescriptions in radiation therapy of GBM.

Materials and Methods::

In this quasi-experimental study, MR images and its relevant data from 30 patients consisting of 9 females and 21 males (mean age of 46.3 ± 15.8 years) diagnosed with GBM, whose referred for radiotherapy were selected. The data of age, gender, tumor size, volume, and signal intensity using analysis of MRI data pre- and postradiotherapy were used for calculating TCP. TCP was calculated from three common radiobiological models including Poisson, linear quadratic, and equivalent uniform dose. The impact of some radiobiological parameters on final TCP in all patients planned with three-dimensional conformal radiation therapy was obtained.

Results:

A statistically significant difference was found among TCP in Poisson model compared to the other two models (P < 0.001). Changes in tumor volume and size after treatment were statistically significant (P < 0.05). Different combinations of radiobiological parameters (α/β and SF₂ in all models) observed were meaningful (P < 0.05).

Conclusion:

The results showed that among TCP radiobiological models, the optimum is the Poisson. The results also identified the importance of TCP radiobiological models in order to improve radiotherapy dose prescriptions.

Current emerging MRI tools for radionecrosis and pseudoprogression diagnosis

PURPOSE OF REVIEW

This review aims to cover current MRI techniques for assessing treatment response in brain tumors, with a focus on radio-induced lesions.

RECENT FINDINGS

Pseudoprogression and radionecrosis are common radiological entities after brain tumor irradiation and are difficult to distinguish from real progression, with major consequences on daily patient care. To date, shortcomings of conventional MRI have been largely recognized but morphological sequences are still used in official response assessment criteria. Several complementary advanced techniques have been proposed but none of them have been validated, hampering their clinical use. Among advanced MRI, brain perfusion measures increase diagnostic accuracy, especially when added with spectroscopy and susceptibility-weighted imaging. However, lack of reproducibility, because of several hard-to-control variables, is still a major limitation for their standardization in routine protocols. Amide Proton Transfer is an emerging molecular imaging technique that promises to offer new metrics by indirectly quantifying intracellular mobile proteins and peptide concentration. Preliminary studies suggest that this noncontrast sequence may add key biomarkers in tumor evaluation, especially in posttherapeutic settings.

SUMMARY

Benefits and pitfalls of conventional and advanced imaging on posttreatment assessment are discussed and the potential added value of APT in this clinicoradiological evolving scenario is introduced.

Physiological Imaging Methods for Evaluating Response to Immunotherapies in Glioblastomas

Glioblastoma (GBM) is the most malignant brain tumor in adults, with a dismal prognosis despite aggressive multi-modal therapy. Immunotherapy is currently being evaluated as an alternate treatment modality for recurrent GBMs in clinical trials. These immunotherapeutic approaches harness the patient's immune response to fight and eliminate tumor cells. Standard MR imaging is not adequate for response assessment to immunotherapy in GBM patients even after using refined response assessment criteria secondary to amplified immune response. Thus, there is an urgent need for the development of effective and alternative neuroimaging techniques for accurate response assessment. To this end, some groups have reported the potential of diffusion and perfusion MR imaging and amino acid-based positron emission tomography techniques in evaluating treatment response to different immunotherapeutic regimens in GBMs. The main goal of these techniques is to provide definitive metrics of treatment response at earlier time points for making informed decisions on future therapeutic interventions. This review provides an overview of available immunotherapeutic approaches used to treat GBMs. It discusses the limitations of conventional imaging and potential utilities of physiologic imaging techniques in the response assessment to immunotherapies. It also describes challenges associated with these imaging methods and potential solutions to avoid them.

Dynamic Susceptibility Perfusion Imaging for Differentiating Progressive Disease from Pseudoprogression in Diffuse Glioma Molecular Subtypes

Rationale and Objectives: Advanced adjuvant therapy of diffuse gliomas can result in equivocal findings in follow-up imaging. We aimed to assess the additional value of dynamic susceptibility perfusion imaging in the differentiation of progressive disease (PD) from pseudoprogression (PsP) in different molecular glioma subtypes. Materials and Methods: 89 patients with treated diffuse glioma with different molecular subtypes (IDH wild type (Astro-IDH^wt), IDH mutant astrocytomas (Astro-IDH^mut) and oligodendrogliomas), and tumor-suspect lesions on post-treatment follow-up imaging were classified into two outcome groups (PD or PsP) retrospectively by histopathology or clinical follow-up. The relative cerebral blood volume (rCBV) was assessed in the tumor-suspect FLAIR and contrast-enhancing (CE) lesions. We analyzed how a multilevel classification using a molecular subtype, the presence of a CE lesion, and two rCBV histogram parameters performed for PD prediction compared with a decision tree model (DTM) using additional rCBV parameters. Results: The PD rate was 69% in the whole cohort, 86% in Astro-IDH^wt, 52% in Astro-IDH^mut, and 55% in oligodendrogliomas. In the presence of a CE lesion, the PD rate was higher with 82%, 94%, 59%, and 88%, respectively; if there was no CE lesion, however, the PD rate was only 44%, 60%, 40%, and 33%, respectively. The additional use of the rCBV parameters in the DTM yielded a prediction accuracy for PD of 99%, 100%, 93%, and 95%, respectively. Conclusion: Utilizing combined information about the molecular tumor type, the presence or absence of CE lesions and rCBV parameters increases PD prediction accuracy in diffuse glioma.

Recognizing Radiation-induced Changes in the Central Nervous System: Where to Look and What to Look For

Radiation therapy (RT) continues to play a central role as an effective therapeutic modality for a variety of tumors and vascular malformations in the central nervous system. Although the planning and delivery techniques of RT have evolved substantially during the past few decades, the structures surrounding the target lesion are inevitably exposed to radiation. A wide variety of radiation-induced changes may be observed at posttreatment imaging, which may be confusing when interpreting images. Histopathologically, radiation can have deleterious effects on the vascular endothelial cells as well as on neuroglial cells and their precursors. In addition, radiation induces oxidative stress and inflammation, leading to a cycle of further cellular toxic effects and tissue damage. On the basis of the time of expression, radiation-induced injury can be divided into three phases: acute, early delayed, and late delayed. Acute and early delayed injuries are usually transient and reversible, whereas late delayed injuries are generally irreversible. The authors provide a comprehensive review of the timeline and expected imaging appearances after RT, including the characteristic imaging features after RT with concomitant chemotherapy. Specific topics discussed are imaging features that help distinguish expected posttreatment changes from recurrent disease, followed by a discussion on the role of advanced imaging techniques. Knowledge of the RT plan, the amount of normal structures included, the location of the target lesion, and the amount of time elapsed since RT is highly important at follow-up imaging, and the reporting radiologist should be able to recognize the characteristic imaging features after RT and differentiate these findings from tumor recurrence. ^©RSNA, 2020.

Machine Learning Model to Predict Pseudoprogression Versus Progression in Glioblastoma Using MRI: A Multi-Institutional Study (KROG 18-07)

Simple Summary

Even after the introduction of a standard regimen consisting of concurrent chemoradiotherapy and adjuvant temozolomide, patients with glioblastoma multiforme mostly experience disease progression. Clinicians often encounter a situation where they need to distinguish progressive disease from pseudoprogression after treatment. We tried to investigate the feasibility of machine learning algorithm to distinguish pseudoprogression from progressive disease. In multi-institutional dataset, the developed machine learning model showed an acceptable performance. This algorithm involving MRI data and clinical features could help making decision during patients’ disease course. For the practical use, we calibrated the machine learning model to offer the probability of pseudoprogression to clinicians, then we constructed the web-based user interface to access the model.

Abstract

Some patients with glioblastoma show a worsening presentation in imaging after concurrent chemoradiation, even when they receive gross total resection. Previously, we showed the feasibility of a machine learning model to predict pseudoprogression (PsPD) versus progressive disease (PD) in glioblastoma patients. The previous model was based on the dataset from two institutions (termed as the Seoul National University Hospital (SNUH) dataset, N = 78). To test this model in a larger dataset, we collected cases from multiple institutions that raised the problem of PsPD vs. PD diagnosis in clinics (Korean Radiation Oncology Group (KROG) dataset, N = 104). The dataset was composed of brain MR images and clinical information. We tested the previous model in the KROG dataset; however, that model showed limited performance. After hyperparameter optimization, we developed a deep learning model based on the whole dataset (N = 182). The 10-fold cross validation revealed that the micro-average area under the precision-recall curve (AUPRC) was 0.86. The calibration model was constructed to estimate the interpretable probability directly from the model output. After calibration, the final model offers clinical probability in a web-user interface.

Clinical Outcomes in Patients with Recurrent Glioblastoma Treated with Proton Beam Therapy Reirradiation: Analysis of the Multi-Institutional Proton Collaborative Group Registry

Purpose

As a means of limiting normal tissue toxicity, proton-beam therapy (PBT) is an emerging radiation modality for glioblastoma (GBM) reirradiation. However, data for recurrent GBM treated with PBT reirradiation is limited. Therefore, we analyzed treatment patterns, toxicities, and clinical outcomes of patients with recurrent GBM treated with PBT reirradiation using the multi-institutional Proton Collaborative Group registry.

Methods and Materials

Prospectively collected data for patients with recurrent GBM who underwent PBT while enrolled in Proton Collaborative Group study 01-009 (NCT01255748) were analyzed. We evaluated overall survival (OS), progression-free survival (PFS), and toxicity. Toxicities were scored per the Common Terminology Criteria for Adverse Events, version 4.0. Descriptive statistics were used to report patient, tumor, and treatment characteristics. Multivariable analyses (MVA) for toxicity were conducted using logistic regression. The Kaplan-Meier method was used to calculate OS and PFS. MVA for OS and PFS was conducted using Cox proportional-hazards models. The SAS statistical software was used for the analysis.

Results

We identified 45 recurrent patients with GBM who underwent PBT reirradiation between 2012 and 2018. The median time between initial GBM diagnosis and recurrence was 20.2 months. The median follow-up time from PBT reirradiation was 10.7 months. Median PFS was 13.9 months (95% confidence interval [CI], 8.23-20.0 months) and median OS was 14.2 months (95% CI, 9.6-16.9 months) after PBT reirradiation. One patient experienced an acute grade 3 toxicity, 4 patients experienced late grade 3 toxicity (no grade ≥4 toxicities). MVA revealed that prior surgery was associated with a 91.3% decreased hazard of death (hazard ratio: 0.087; 95% CI, 0.02-0.42; P < .01). No explanatory variables were associated with PFS or grade 3 toxicities.

Conclusions

This is the largest series to date reporting outcomes for PBT reirradiation of patients with recurrent GBM. Our analysis indicates that PBT is well tolerated and offers efficacy rates comparable with previously reported photon reirradiation.

FAP-specific PET signaling shows a moderately positive correlation with relative CBV and no correlation with ADC in 13 IDH wildtype glioblastomas

OBJECTIVES

Targeting Fibroblast Activation Protein (FAP) is a new approach for glioblastoma imaging. In a recent pilot study glioblastomas showed elevated tracer uptake with high intratumoral heterogeneity in projection on the corresponding T2w/FLAIR and contrast enhanced MRI lesions. In this study, we correlated FAP-specific signaling with apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) signals in MRI to further characterize the significance of FAP uptake.

METHODS

Clinical PET/CT scans of 13 glioblastoma patients were performed post i. v. administration of ⁶⁸Ga-labelled-FAP-specific tracer molecules. PET- and corresponding MRI-scans were co-registrated. 3d volumetric segmentations were performed of T2w/FLAIR lesions and contrast enhancing lesions within co-registrated MRI slides. Signal intensity values of FAP-specific PET signaling, ADC and rCBV were analyzed for their pixel wise correlation in each patient. Pooled estimates of the correlation coefficients were calculated by using the Fisher z-transformation.

RESULTS

FAP-specific PET signals showed a moderately positive correlation with rCBV values which is more pronounced within the T2w/FLAIR lesion (pooled correlation 0,229) than in the contrast enhancing tumor region (pooled correlation 0.09). FAP-specific PET signals showed no correlation with ADC values.

CONCLUSIONS

The moderately positive correlation of FAP-specific signals with rCBV values in MRI indicates that FAP-signaling is not independent from perfusion, but also does not only reflect intratumoral perfusion differences. The missing correlation of FAP-specific signals with ADC indicates that FAP-specific imaging does not reflect cell density, but the spot-like expression of FAP in glioblastomas. The clinical value of FAP-specific imaging needs further investigation.

Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma

BACKGROUND

Imaging of glioblastoma patients after maximal safe resection and chemoradiation commonly demonstrates new enhancements that raise concerns about tumor progression. However, in 30% to 50% of patients, these enhancements primarily represent the effects of treatment, or pseudo-progression (PsP). We hypothesize that quantitative machine learning analysis of clinically acquired multiparametric magnetic resonance imaging (mpMRI) can identify subvisual imaging characteristics to provide robust, noninvasive imaging signatures that can distinguish true progression (TP) from PsP.

METHODS

We evaluated independent discovery (n = 40) and replication (n = 23) cohorts of glioblastoma patients who underwent second resection due to progressive radiographic changes suspicious for recurrence. Deep learning and conventional feature extraction methods were used to extract quantitative characteristics from the mpMRI scans. Multivariate analysis of these features revealed radiophenotypic signatures distinguishing among TP, PsP, and mixed response that compared with similar categories blindly defined by board-certified neuropathologists. Additionally, interinstitutional validation was performed on 20 new patients.

RESULTS

Patients who demonstrate TP on neuropathology are significantly different (P < .0001) from those with PsP, showing imaging features reflecting higher angiogenesis, higher cellularity, and lower water concentration. The accuracy of the proposed signature in leave-one-out cross-validation was 87% for predicting PsP (area under the curve [AUC], 0.92) and 84% for predicting TP (AUC, 0.83), whereas in the discovery/replication cohort, the accuracy was 87% for predicting PsP (AUC, 0.84) and 78% for TP (AUC, 0.80). The accuracy in the interinstitutional cohort was 75% (AUC, 0.80).

CONCLUSION

Quantitative mpMRI analysis via machine learning reveals distinctive noninvasive signatures of TP versus PsP after treatment of glioblastoma. Integration of the proposed method into clinical studies can be performed using the freely available Cancer Imaging Phenomics Toolkit.

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.

Does Surveillance-Detected Disease Progression Yield Superior Patient Outcomes in High-Grade Glioma?

BACKGROUND

Standard follow-up care for patients with high-grade glioma (HGG) involves routine surveillance imaging to detect disease progression, assess treatment response, and monitor clinical symptoms. Although logical in nature, evidence supporting this practice is limited. We hypothesize patients with tumor recurrence detected on routine surveillance imaging will experience superior outcomes relative to symptomatic detection, using measures of survival and postrecurrence neurologic function.

METHODS

Adult patients receiving treatment for HGG at our institution from 2004 to 2018 were identified, and data including tumor characteristics, imaging results, neurologic status, and survival were extracted from the medical records of patients meeting inclusion criteria. All participants were followed for a minimum of 12 months, or for survival duration. Survival and neurologic function differences were assessed using log rank and 2-sample t tests with 2-sided 0.05 alpha level of significance.

RESULTS

Of the 74 patients meeting inclusion criteria, 47 (63.5%) had recurrence detected via routine surveillance imaging, and 27 (36.5%) had symptomatic detection outside of the surveillance schedule. Neither median overall survival (14.8 months for surveillance and 15.7 months for symptomatic; P = 0.600) nor postrecurrence neurologic function (assessed by Karnofsky Performance Scale Index and Eastern Cooperative Oncology Group) differed between the surveillance and symptomatic detection groups (P = 0.699 and P = 0.908, respectively).

CONCLUSIONS

Recurrence detection occurring via routine surveillance imaging did not yield superior patient outcomes relative to symptomatic detection occurring outside of the standard surveillance schedule in patients with HGG. Further evaluation of surveillance imaging and alternative follow-up methods for this patient population may be warranted.

18F-Fluciclovine PET to distinguish treatment-related effects from disease progression in recurrent glioblastoma: PET fusion with MRI guides neurosurgical sampling

Differentiation of true tumor progression from treatment-related effects remains a major unmet need in caring for patients with glioblastoma. Here, we report how the intraoperative combination of MRI with¹⁸F-fluciclovine PET guided surgical sampling in 2 patients with recurrent glioblastoma.¹⁸F-Fluciclovine PET is FDA approved for use in prostate cancer and carries an orphan drug designation in glioma. To investigate its utility in recurrent glioblastoma, we fused PET and MRI images using 2 different surgical navigation systems and performed targeted stereotactic biopsies from the areas of high (“hot”) and low (“cold”) radiotracer uptake. Concordant histopathologic and imaging findings suggest that a combined¹⁸F-fluciclovine PET-MRI–guided approach can guide neurosurgical resection of viable recurrent glioblastoma in the background of treatment-related effects, which can otherwise look similar on MRI.

In silico study of pseudoprogression in glioblastoma: collaboration of radiologists and radiation oncologists in the estimation of extent of high dose RT region

BACKGROUND AND AIM

Oncologists play a vital role in the interpretation of radiographic results in glioblastoma patients. Molecular pathology and information on radiation treatment protocols among others are all important for accurate interpretation of radiology images. One important issue that may arise in interpreting such images is the phenomenon of tumor "pseudoprogression"; oncologists need to be able to distinguish this effect from true disease progression.Exact knowledge about the location of high-dose radiotherapy region is needed for valid determination of pseudoprogression according to RANO (Response Assessment in Neuro-Oncology) criteria in neurooncology. The aim of the present study was to evaluate the radiologists' understanding of a radiotherapy high-dose region in routine clinical practice since radiation oncologists do not always report 3-dimensional isodoses when ordering follow up imaging.

METHODS

Eight glioblastoma patients who underwent postresection radiotherapy were included in this study. Four radiologists worked with their pre-radiotherapy planning MR, however, they were blinded to RT target volumes which were defined by radiation oncologists according to current guidelines. The aim was to draw target volume for high dose RT fields (that is the region, where they would consider that there may be a pseudoprogression in future MRI scans). Many different indices describing structure differences were analyzed in comparison with original per-protocol RT target volumes.

RESULTS

The median volume for RT high dose field was 277 ccm (range 218 to 401 ccm) as defined per protocol by radiation oncologist and 87 ccm (range 32-338) as defined by radiologists (median difference of paired difference 31%, range 15-112%). The Median Dice index of similarity was 0.46 (range 0.14 - 0.78), the median Hausdorff distance 25 mm.

CONCLUSION

Continuing effort to improve education on specific procedures in RT and in radiology as well as automatic tools for exporting RT targets is needed in order to increase specificity and sensitivity in response evaluation.

Biomarkers and smart intracranial devices for the diagnosis, treatment, and monitoring of high-grade gliomas: a review of the literature and future prospects

Glioblastoma (GBM) is the most common primary brain neoplasm with median overall survival (OS) around 15 months. There is a dearth of effective monitoring strategies for patients with high-grade gliomas. Relying on magnetic resonance images of brain has its challenges, and repeated brain biopsies add significant morbidity. Hence, it is imperative to establish a less invasive way to diagnose, monitor, and guide management of patients with high-grade gliomas. Currently, multiple biomarkers are in various phases of development and include tissue, serum, cerebrospinal fluid (CSF), and imaging biomarkers. Here we review and summarize the potential biomarkers found in blood and CSF, including extracellular macromolecules, extracellular vesicles, circulating tumor cells, immune cells, endothelial cells, and endothelial progenitor cells. The ability to detect tumor-specific biomarkers in blood and CSF will potentially not only reduce the need for repeated brain biopsies but also provide valuable information about the heterogeneity of tumor, response to current treatment, and identify disease resistance. This review also details the status and potential scope of brain tumor-related cranial devices and implants including Ommaya reservoir, microelectromechanical systems-based depot device, Alzet mini-osmotic pump, Metronomic Biofeedback Pump (MBP), ipsum G1 implant, ultra-thin needle implant, and putative devices. An ideal smart cranial implant will overcome the blood-brain barrier, deliver various drugs, provide access to brain tissue, and potentially measure and monitor levels of various biomarkers.

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma.