Radiation Necrosis, Pseudoprogression, Pseudoresponse, and Tumor Recurrence: Imaging Challenges for the Evaluation of Treated Gliomas

Optimizing Recurrent Glioblastoma Salvage Treatment: A Multicenter Study Integrating Genetic Biomarkers From the Korean Radiation Oncology Group (21-02)

BACKGROUND AND OBJECTIVES

Few studies have used real-world patient data to compare overall treatment patterns and survival outcomes for recurrent glioblastoma (rGBM). This study aimed to evaluate postprogression survival (PPS) according to the treatment strategy for rGBM by incorporating biomarker analysis.

METHODS

We assessed 468 adult patients with rGBM who underwent standard temozolomide-based chemoradiation. The impact of predictors on PPS was evaluated in patients with isocitrate dehydrogenase wild-type rGBM (n = 439) using survival probability analysis. We identified patients who would benefit from reirradiation (re-RT) during the first progression.

RESULTS

Median PPS was 3.4, 13.8, 6.6, and 10.0 months in the best supportive care (n = 82), surgery (with/without adjuvant therapy, n = 112), chemotherapy alone (n = 170), and re-RT (with/without chemotherapy, n = 75) groups, respectively. After propensity score matching analysis of the cohort, both the surgery and re-RT groups had a significantly better PPS than the chemotherapy-only group; however, no significant difference was observed in PPS between the surgery and re-RT groups. In the surgery subgroup, surgery with chemotherapy ( P = .024) and surgery with radio(chemo)therapy ( P = .039) showed significantly improved PPS compared with surgery alone. In the no-surgery subgroup, radio(chemo)therapy showed significantly improved PPS compared with chemotherapy alone ( P = .047). Homozygous deletion of cyclin-dependent kinase inhibitor 2A/B, along with other clinical factors (performance score and progression-free interval), was significantly associated with the re-RT survival benefit.

CONCLUSION

Surgery combined with radio(chemo)therapy resulted in the best survival outcomes for rGBM. re-RT should also be considered for patients with rGBM at first recurrence. Furthermore, this study identified a specific genetic biomarker and clinical factors that may enhance the survival benefit of re-RT.

How to evaluate perfusion imaging in post-treatment glioma: a comparison of three different analysis methods

INTRODUCTION

Dynamic susceptibility contrast (DSC) perfusion weighted (PW)-MRI can aid in differentiating treatment related abnormalities (TRA) from tumor progression (TP) in post-treatment glioma patients. Common methods, like the 'hot spot', or visual approach suffer from oversimplification and subjectivity. Using perfusion of the complete lesion potentially offers an objective and accurate alternative. This study aims to compare the diagnostic value and assess the subjectivity of these techniques.

METHODS

50 Glioma patients with enhancing lesions post-surgery and chemo-radiotherapy were retrospectively included. Outcome was determined by clinical/radiological follow-up or biopsy. Imaging analysis used the 'hot spot', volume of interest (VOI) and visual approach. Diagnostic accuracy was compared using receiving operator characteristics (ROC) curves for the VOI and 'hot spot' approach, visual assessment was analysed with contingency tables. Inter-operator agreement was determined with Cohens kappa and intra-class coefficient (ICC).

RESULTS

29 Patients suffered from TP, 21 had TRA. The visual assessment showed poor to substantial inter-operator agreement (κ = -0.72 - 0.68). Reliability of the 'hot spot' placement was excellent (ICC = 0.89), while reference placement was variable (ICC = 0.54). The area under the ROC (AUROC) of the mean- and maximum relative cerebral blood volume (rCBV) (VOI-analysis) were 0.82 and 0.72, while the rCBV-ratio ('hot spot' analysis) was 0.69. The VOI-analysis had a more balanced sensitivity and specificity compared to visual assessment.

CONCLUSIONS

VOI analysis of DSC PW-MRI data holds greater diagnostic accuracy in single-moment differentiation of TP and TRA than 'hot spot' or visual analysis. This study underlines the subjectivity of visual placement and assessment.

Imaging and Liquid Biopsy for Distinguishing True Progression From Pseudoprogression in Gliomas, Current Advances and Challenges

RATIONALE AND OBJECTIVES

Gliomas are aggressive brain tumors with a poor prognosis. Assessing treatment response is challenging because magnetic resonance imaging (MRI) may not distinguish true progression (TP) from pseudoprogression (PsP). This review aims to discuss imaging techniques and liquid biopsies used to distinguish TP from PsP.

MATERIALS AND METHODS

This review synthesizes existing literature to examine advances in imaging techniques, such as magnetic resonance diffusion imaging (MRDI), perfusion-weighted imaging (PWI) MRI, and liquid biopsies, for identifying TP or PsP through tumor markers and tissue characteristics.

RESULTS

Advanced imaging techniques, including MRDI and PWI MRI, have proven effective in delineating tumor tissue properties, offering valuable insights into glioma behavior. Similarly, liquid biopsy has emerged as a potent tool for identifying tumor-derived markers in biofluids, offering a non-invasive glimpse into tumor evolution. Despite their promise, these methodologies grapple with significant challenges. Their sensitivity remains inconsistent, complicating the accurate differentiation between TP and PSP. Furthermore, the absence of standardized protocols across platforms impedes the reliability of comparisons, while inherent biological variability adds complexity to data interpretation.

CONCLUSION

Their potential applications have been highlighted, but gaps remain before routine clinical use. Further research is needed to develop and validate these promising methods for distinguishing TP from PsP in gliomas.

Development and validation of a nomogram to predict overall survival in patients with glioma: a population-based study

AIM

The objective is to investigate the prognostic factors associated with gliomas and to develop and assess a predictive nomogram model connected to survival that may serve as an additional resource for the clinical management of glioma patients.

METHOD

From 2010 to 2015, participants included in the study were chosen from the Surveillance Epidemiology and End Results (SEER) database. Gliomas were definitively diagnosed in each of them. They were divided into the training group and the validation cohort at random (7/3 ratio) using a random number table. To identify the independent predictive markers for overall survival (OS), Cox regression analysis was utilized. Subsequently, the training cohort's survival-related nomogram predictive model for OS was created by incorporating the fundamental patient attributes. Following that, the training cohort's model underwent internal validation. The nomogram model's authenticity and reliability were assessed through the computation of receiver operating characteristic (ROC) curves and concordance index (C-index). To evaluate the degree of agreement between the observed and predicted values in the training and validation cohorts, calibration plots were created.

RESULT

Age, primary site, histological type, surgery, chemotherapy, marital status, and grade were the independent predictive factors for OS in the training cohort, according to Cox regression analysis. Moreover, the nomogram model for predicting 1-year, 3-year, and 5-year OS was built using these variables. The C-indexes of OS for glioma patients in the training cohort and internal validation cohort were found to be 0.779 (95% CI=0.769-0.789) and 0.776 (95% CI=0.760-0.792), respectively, according to the results. The ROC curves also demonstrated good discrimination. Additionally, calibration plots demonstrated a fair amount of agreement.

CONCLUSIONS

In summary, the nomogram prediction model of OS demonstrated a moderate level of reliability in its predictive performance, offering valuable reference data to enable doctors to quickly and easily determine the survival likelihood of patients with gliomas.

Diffusion tensor imaging in detecting gliomas sub-regions of infiltration, local and remote recurrences: a systematic review

Given that glioma cells tend to infiltrate and migrate along WM tracts, leading to demyelination and axonal injuries, Diffusion Tensor Imaging (DTI) emerged as a promising tool for identifying major "high-risk areas" of recurrence within the peritumoral brain zone (PBZ) or at a distance throughout the adjacents white matter tracts. Of our systematic review is to answer the following research question: In patients with brain tumor, is DTI able to recognizes within the peri-tumoral brain zone (PBZ) areas more prone to local (near the surgical cavity) or remote recurrence compared to the conventional imaging techniques?. We conducted a comprehensive literature search to identify relevant studies in line with the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. 15 papers were deemed compatible with our research question and included. To enhance the paper's readability, we have categorized our findings into two distinct groups: the first delves into the role of DTI in detecting PBZ sub-regions of infiltration and local recurrences (n = 8), while the second group explores the feasibility of DTI in detecting white matter tract infiltration and remote recurrences (n = 7). DTI values and, within a broader framework, radiomics investigations can provide precise, voxel-by-voxel insights into the state of PBZ and recurrences. Better defining the regions at risk for potential recurrence within the PBZ and along WM bundles will allow targeted therapy.

Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging

Glioma is the most common type of primary malignant tumor of the central nervous system (CNS), and is characterized by high malignancy, high recurrence rate and poor survival. Conventional imaging techniques only provide information regarding the anatomical location, morphological characteristics, and enhancement patterns. In contrast, advanced imaging techniques such as dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue microcirculation, including tumor vascular hyperplasia and vessel permeability. Although several studies have used DCE imaging to evaluate gliomas, the results of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or extended-Tofts model (ETM) have been ambiguous. More advanced models such as Brix's conventional two-compartment model (Brix), tissue homogeneity model (TH) and distributed parameter (DP) model have been developed, but their application in clinical trials has been limited. This review attempts to appraise issues on glioma studies using conventional TKMs, such as Tofts or ETM model, highlight advancement of DCE imaging techniques and provides insights on the clinical value of glioma management using more advanced TKMs.

Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review

Magnetic resonance (MR) relaxometry is a quantitative imaging method that measures tissue relaxation properties. This review discusses the state of the art of clinical proton MR relaxometry for glial brain tumors. Current MR relaxometry technology also includes MR fingerprinting and synthetic MRI, which solve the inefficiencies and challenges of earlier techniques. Despite mixed results regarding its capability for brain tumor differential diagnosis, there is growing evidence that MR relaxometry can differentiate between gliomas and metastases and between glioma grades. Studies of the peritumoral zones have demonstrated their heterogeneity and possible directions of tumor infiltration. In addition, relaxometry offers T2* mapping that can define areas of tissue hypoxia not discriminated by perfusion assessment. Studies of tumor therapy response have demonstrated an association between survival and progression terms and dynamics of native and contrast-enhanced tumor relaxometric profiles. In conclusion, MR relaxometry is a promising technique for glial tumor diagnosis, particularly in association with neuropathological studies and other imaging techniques.

[Advances in Pseudoprogression of Immune Checkpoint Inhibitors in Non-small Cell Lung Cancer]

The advent of immune checkpoint inhibitors (ICIs) has greatly improved the prognosis of advanced lung cancer patients, but can lead to pseudoprogression (PsP), which complicates clinical evaluation and management. PsP is manifested as temporary enlargement of the tumour or the appearance of new lesions, etc., and improvement in imaging occurs with continued treatment, mostly without worsening of clinical symptoms. Currently, there are still difficulties in the early diagnosis of PsP, and its occurrence mechanism is not yet clear, lacking good predictive factors and related biomarkers. This article reviews the current research status of PsP of ICIs in non-small cell lung cancer in order to provide helpful clinical strategies for oncologists using these drugs.
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Validating Brain Tumor Reporting and Data System (BT-RADS) as a Diagnostic Tool for Glioma Follow-Up after Surgery

Gliomas are a type of brain tumor that requires accurate monitoring for progression following surgery. The Brain Tumor Reporting and Data System (BT-RADS) has emerged as a potential tool for improving diagnostic accuracy and reducing the need for repeated operations. This prospective multicenter study aimed to evaluate the diagnostic accuracy and reliability of BT-RADS in predicting tumor progression (TP) in postoperative glioma patients and evaluate its acceptance in clinical practice. The study enrolled patients with a history of partial or complete resection of high-grade glioma. All patients underwent two consecutive follow-up brain MRI examinations. Five neuroradiologists independently evaluated the MRI examinations using the BT-RADS. The diagnostic accuracy of the BT-RADS for predicting TP was calculated using histopathology after reoperation and clinical and imaging follow-up as reference standards. Reliability based on inter-reader agreement (IRA) was assessed using kappa statistics. Reader acceptance was evaluated using a short survey. The final analysis included 73 patients (male, 67.1%; female, 32.9%; mean age, 43.2 ± 12.9 years; age range, 31-67 years); 47.9% showed TP, and 52.1% showed no TP. According to readers, TP was observed in 25-41.7% of BT-3a, 61.5-88.9% of BT-3b, 75-90.9% of BT-3c, and 91.7-100% of BT-RADS-4. Considering >BT-RADS-3a as a cutoff value for TP, the sensitivity, specificity, and accuracy of the BT-RADS were 68.6-85.7%, 84.2-92.1%, and 78.1-86.3%, respectively, according to the reader. The overall IRA was good (κ = 0.75) for the final BT-RADS classification and very good for detecting new lesions (κ = 0.89). The readers completely agreed with the statement "the application of the BT-RADS should be encouraged" (score = 25). The BT-RADS has good diagnostic accuracy and reliability for predicting TP in postoperative glioma patients. However, BT-RADS 3 needs further improvements to increase its diagnostic accuracy.

Artificial intelligence-based MRI radiomics and radiogenomics in glioma

The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.

Low rCBV values in glioblastoma tumor progression under chemoradiotherapy

PURPOSE

After standard treatment for glioblastoma, perfusion MRI remains challenging for differentiating tumor progression from post-treatment changes. Our objectives were (1) to correlate rCBV values at diagnosis and at first tumor progression and (2) to analyze the relationship of rCBV values at tumor recurrence with enhancing volume, localization of tumor progression, and time elapsed since the end of radiotherapy in tumor recurrence.

METHODS

Inclusion criteria were (1) age > 18 years, (2) histologically confirmed glioblastoma treated with STUPP regimen, and (3) tumor progression according to RANO criteria > 12 weeks after radiotherapy. Co-registration of segmented enhancing tumor VOIs with dynamic susceptibility contrast perfusion MRI was performed using Olea Sphere software. For tumor recurrence, we correlated rCBV values with enhancing tumor volume, with recurrence localization, and with time elapsed from the end of radiotherapy to progression. Analyses were performed with SPSS software.

RESULTS

Sixty-four patients with glioblastoma were included in the study. Changes in rCBV values between diagnosis and first tumor progression were significant (p < 0.001), with a mean and median decreases of 32% and 46%, respectively. Mean rCBV values were also different (p < 0.01) when tumors progressed distally (radiation field rCBV values of 1.679 versus 3.409 distally). However, changes and, therefore, low rCBV values after radiotherapy in tumor recurrence were independent of time.

CONCLUSION

Chemoradiation alters tumor perfusion and rCBV values may be decreased in the setting of tumor progression. Changes in rCBV values with respect to diagnosis, with low rCBV in tumor progression, are independent of time but related to the site of recurrence.

MRI Treatment Response Assessment Maps (TRAMs) for differentiating recurrent glioblastoma from radiation necrosis

BACKGROUND

MRI treatment response assessment maps (TRAMs) were introduced to distinguish recurrent malignant glioma from therapy related changes. TRAMs are calculated with two contrast-enhanced T1-weighted sequences and reflect the "late" wash-out (or contrast clearance) and wash-in of gadolinium. Vital tumor cells are assumed to produce a wash-out because of their high turnover rate and the associated hypervascularization, whereas contrast medium slowly accumulates in scar tissue. To examine the real value of this method, we compared TRAMs with the pathology findings obtained after a second biopsy or surgery when recurrence was suspected.

METHODS

We retrospectively evaluated TRAMs in adult patients with histologically demonstrated glioblastoma, contrast-enhancing tissue and a pre-operative MRI between January 1, 2017, and December 31, 2022. Only patients with a second biopsy or surgery were evaluated. Volumes of the residual tumor, contrast clearance and contrast accumulation before the second surgery were analyzed.

RESULTS

Among 339 patients with mGBM who underwent MRI, we identified 29 repeated surgeries/biopsies in 27 patients 59 ± 12 (mean ± standard deviation) years of age. Twenty-eight biopsies were from patients with recurrent glioblastoma histology, and only one was from a patient with radiation necrosis. We volumetrically evaluated the 29 pre-surgery TRAMs. In recurrent glioblastoma, the ratio of wash-out volume to tumor volume was 36 ± 17% (range 1-73%), and the ratio of the wash-out volume to the sum of wash-out and wash-in volumes was 48 ± 21% (range 22-92%). For the one biopsy with radiation necrosis, the ratios were 42% and 54%, respectively.

CONCLUSIONS

Typical recurrent glioblastoma shows a > 20%ratio of the wash-out volume to the sum of wash-out and wash-in volumes. The one biopsy with radiation necrosis indicated that such necrosis can also produce high wash-out in individual cases. Nevertheless, the additional information provided by TRAMs increases the reliability of diagnosis.

Comparing [18F]FET PET and [18F]FDOPA PET for glioma recurrence diagnosis: a systematic review and meta-analysis

Purpose

The purpose of our meta-analysis and systematic review was to evaluate and compare the diagnostic effectiveness of [18F]FET PET and [18F]FDOPA PET in detecting glioma recurrence.

Methods

Sensitivities and specificities were assessed using the DerSimonian and Laird methodology, and subsequently transformed using the Freeman-Tukey double inverse sine transformation. Confidence intervals were computed employing the Jackson method, while heterogeneity within and between groups was evaluated through the Cochrane Q and I² statistics. If substantial heterogeneity among the studies was observed (P < 0.10 or I² > 50%), we conducted meta-regression and sensitivity analyses. Publication bias was assessed through the test of a funnel plot and the application of Egger's test. For all statistical tests, except for assessing heterogeneity (P < 0.10), statistical significance was determined when the two-tailed P value fell below 0.05.

Results

Initially, 579 publications were identified, and ultimately, 22 studies, involving 1514 patients(1226 patients for [18F]FET PET and 288 patients for [18F]FDOPA PET), were included in the analysis. The sensitivity and specificity of [18F]FET PET were 0.84 (95% CI, 0.75-0.90) and 0.86 (95% CI, 0.80-0.91), respectively, while for [18F]FDOPA PET, the values were 0.95 (95% CI, 0.86-1.00) for sensitivity and 0.90 (95% CI, 0.77-0.98) for specificity. A statistically significant difference in sensitivity existed between these two radiotracers (P=0.04), while no significant difference was observed in specificity (P=0.58).

Conclusion

It seems that [18F]FDOPA PET demonstrates superior sensitivity and similar specificity to [18F] FET PET. Nevertheless, it's crucial to emphasize that [18F]FDOPA PET results were obtained from studies with limited sample sizes. Further larger prospective studies, especially head-to-head comparisons, are needed in this issue.

Systematic Review Registration

identifier CRD42023463476.

Umbrella review and network meta-analysis of diagnostic imaging test accuracy studies in Differentiating between brain tumor progression versus pseudoprogression and radionecrosis

PURPOSE

In this study we gathered and analyzed the available evidence regarding 17 different imaging modalities and performed network meta-analysis to find the most effective modality for the differentiation between brain tumor recurrence and post-treatment radiation effects.

METHODS

We conducted a comprehensive systematic search on PubMed and Embase. The quality of eligible studies was assessed using the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) instrument. For each meta-analysis, we recalculated the effect size, sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio from the individual study data provided in the original meta-analysis using a random-effects model. Imaging technique comparisons were then assessed using NMA. Ranking was assessed using the multidimensional scaling approach and by visually assessing surface under the cumulative ranking curves.

RESULTS

We identified 32 eligible studies. High confidence in the results was found in only one of them, with a substantial heterogeneity and small study effect in 21% and 9% of included meta-analysis respectively. Comparisons between MRS Cho/NAA, Cho/Cr, DWI, and DSC were most studied. Our analysis showed MRS (Cho/NAA) and 18F-DOPA PET displayed the highest sensitivity and negative likelihood ratios. 18-FET PET was ranked highest among the 17 studied techniques with statistical significance. APT MRI was the only non-nuclear imaging modality to rank higher than DSC, with statistical insignificance, however.

CONCLUSION

The evidence regarding which imaging modality is best for the differentiation between radiation necrosis and post-treatment radiation effects is still inconclusive. Using NMA, our analysis ranked FET PET to be the best for such a task based on the available evidence. APT MRI showed promising results as a non-nuclear alternative.

Efficacy of tumour-treating fields therapy in recurrent glioblastoma: A narrative review of current evidence

Recurrent Glioblastoma presents a formidable challenge in oncology due to its aggressive nature and limited treatment options. Tumour-Treating Fields (TTFields) Therapy, a novel therapeutic modality, has emerged as a promising approach to address this clinical conundrum. This review synthesizes the current evidence surrounding the efficacy of TTFields Therapy in the context of recurrent Glioblastoma. Diverse academic databases were explored to identify relevant studies published within the last decade. Strategic keyword selection facilitated the inclusion of studies focusing on TTFields Therapy's efficacy, treatment outcomes, and patient-specific factors. The review reveals a growing body of evidence suggesting the potential clinical benefits of TTFields Therapy for patients with recurrent Glioblastoma. Studies consistently demonstrate its positive impact on overall survival (OS) and progression-free survival (PFS). The therapy's safety profile remains favorable, with mild to moderate skin reactions being the most commonly reported adverse events. Our analysis highlights the importance of patient selection criteria, with emerging biomarkers such as PTEN mutation status influencing therapy response. Additionally, investigations into combining TTFields Therapy with other treatments, including surgical interventions and novel approaches, offer promising avenues for enhancing therapeutic outcomes. The synthesis of diverse studies underscores the potential of TTFields Therapy as a valuable addition to the armamentarium against recurrent Glioblastoma. The narrative review comprehensively explains the therapy's mechanisms, clinical benefits, adverse events, and future directions. The insights gathered herein serve as a foundation for clinicians and researchers striving to optimize treatment strategies for patients facing the challenging landscape of recurrent Glioblastoma.

Artificial intelligence in neuroimaging of brain tumors: reality or still promise?

PURPOSE OF REVIEW

To provide an updated overview of artificial intelligence (AI) applications in neuro-oncologic imaging and discuss current barriers to wider clinical adoption.

RECENT FINDINGS

A wide variety of AI applications in neuro-oncologic imaging have been developed and researched, spanning tasks from pretreatment brain tumor classification and segmentation, preoperative planning, radiogenomics, prognostication and survival prediction, posttreatment surveillance, and differentiating between pseudoprogression and true disease progression. While earlier studies were largely based on data from a single institution, more recent studies have demonstrated that the performance of these algorithms are also effective on external data from other institutions. Nevertheless, most of these algorithms have yet to see widespread clinical adoption, given the lack of prospective studies demonstrating their efficacy and the logistical difficulties involved in clinical implementation.

SUMMARY

While there has been significant progress in AI and neuro-oncologic imaging, clinical utility remains to be demonstrated. The next wave of progress in this area will be driven by prospective studies measuring outcomes relevant to clinical practice and go beyond retrospective studies which primarily aim to demonstrate high performance.

Post-chemo-radiotherapy response and pseudo-progression evaluation on glioma cell types by multi-parametric magnetic resonance imaging: a prospective study

BACKGROUND

We focused on Differentiated pseudoprogression (PPN) of progression (PN) and the response to radiotherapy (RT) or chemoradiotherapy (CRT) using diffusion and metabolic imaging.

METHODS

Seventy-five patients with glioma were included in this prospective study (approved by the Iranian Registry of Clinical Trials (IRCT) (IRCT20230904059352N1) in September 2023). Contrast-enhanced lesion volume (CELV), non-enhanced lesion volume (NELV), necrotic tumor volume (NTV), and quantitative values ​​of apparent diffusion coefficient (ADC) and magnetic resonance spectroscopy (Cho/Cr, Cho/NAA and NAA/Cr) were calculated by a neuroradiologist using a semi-automatic method. All patients were followed at one and six months after CRT.

RESULTS

The results of the study showed statistically significant changes before and six months after RT-CRT for M-CELV in all glioma types (𝑝 < 0.05). In glioma cell types, the changes in M-ADC, M-Cho/Cr, and Cho/NAA indices for PN were incremental and greater for PPN patients. M-NAA/Cr ratio decreased after six months which was significant only on PN for GBM, and Epn (𝑝 < 0.05). A significant difference was observed between diffusion indices, metabolic ratios, and CELV changes after six months in all types (𝑝 < 0.05). None of the patients were suspected PPN one month after treatment. The DWI/ADC indices had higher sensitivity and specificity (98.25% and 96.57%, respectively).

CONCLUSION

The results of the present study showed that ADC values and Cho/Cr and Cho/NAA ratios can be used to differentiate between patients with PPN and PN, although ADC is more sensitive and specific.

Treatment of Central Nervous System Tumors on Combination MR-Linear Accelerators: Review of Current Practice and Future Directions

Magnetic resonance imaging (MRI) provides excellent visualization of central nervous system (CNS) tumors due to its superior soft tissue contrast. Magnetic resonance-guided radiotherapy (MRgRT) has historically been limited to use in the initial treatment planning stage due to cost and feasibility. MRI-guided linear accelerators (MRLs) allow clinicians to visualize tumors and organs at risk (OARs) directly before and during treatment, a process known as online MRgRT. This novel system permits adaptive treatment planning based on anatomical changes to ensure accurate dose delivery to the tumor while minimizing unnecessary toxicity to healthy tissue. These advancements are critical to treatment adaptation in the brain and spinal cord, where both preliminary MRI and daily CT guidance have typically had limited benefit. In this narrative review, we investigate the application of online MRgRT in the treatment of various CNS malignancies and any relevant ongoing clinical trials. Imaging of glioblastoma patients has shown significant changes in the gross tumor volume over a standard course of chemoradiotherapy. The use of adaptive online MRgRT in these patients demonstrated reduced target volumes with cavity shrinkage and a resulting reduction in radiation dose to uninvolved tissue. Dosimetric feasibility studies have shown MRL-guided stereotactic radiotherapy (SRT) for intracranial and spine tumors to have potential dosimetric advantages and reduced morbidity compared with conventional linear accelerators. Similarly, dosimetric feasibility studies have shown promise in hippocampal avoidance whole brain radiotherapy (HA-WBRT). Next, we explore the potential of MRL-based multiparametric MRI (mpMRI) and genomically informed radiotherapy to treat CNS disease with cutting-edge precision. Lastly, we explore the challenges of treating CNS malignancies and special limitations MRL systems face.

Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme

Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios. Preliminary published studies are promising about the role of radiomics in post-treatment glioma/GBM. However, this field faces significant challenges, including a lack of evidence-based solid data, scattering publication, heterogeneity of studies, and small sample sizes. The present review explores radiomics's capabilities in following patients with glioma/GBM status post-treatment and to differentiate tumor progression, recurrence, pseudoprogression, and radionecrosis.

Intraoperative or postoperative stereotactic radiotherapy for brain metastases: time to systemic treatment onset and other patient-relevant outcomes

PURPOSE

Intraoperative radiotherapy (IORT) has become a viable treatment option for resectable brain metastases (BMs). As data on local control and radiation necrosis rates are maturing, we focus on meaningful secondary endpoints such as time to next treatment (TTNT), duration of postoperative corticosteroid treatment, and in-hospital time.

METHODS

Patients prospectively recruited within an IORT study registry between November 2020 and June 2023 were compared with consecutive patients receiving adjuvant stereotactic radiotherapy (SRT) of the resection cavity within the same time frame. TTNT was defined as the number of days between BM resection and start of the next extracranial oncological therapy (systemic treatment, surgery, or radiotherapy) for each of the groups.

RESULTS

Of 95 BM patients screened, IORT was feasible in 84 cases (88%) and ultimately performed in 64 (67%). The control collective consisted of 53 SRT patients. There were no relevant differences in clinical baseline features. Mean TTNT (range) was 36 (9 - 94) days for IORT patients versus 52 (11 - 126) days for SRT patients (p = 0.01). Mean duration of postoperative corticosteroid treatment was similar (8 days; p = 0.83), as was mean postoperative in-hospital time (11 versus 12 days; p = 0.97). Mean total in-hospital time for BM treatment (in- and out-patient days) was 11 days for IORT versus 19 days for SRT patients (p < 0.001).

CONCLUSION

IORT for BMs results in faster completion of interdisciplinary treatment when compared to adjuvant SRT, without increasing corticosteroid intake or prolonging in-hospital times. A randomised phase III trial will determine the clinical effects of shorter TTNT.

Pseudoprogression versus true progression in glioblastoma: what neurosurgeons need to know

Management of patients with glioblastoma (GBM) is complex and involves implementing standard therapies including resection, radiation therapy, and chemotherapy, as well as novel immunotherapies and targeted small-molecule inhibitors through clinical trials and precision medicine approaches. As treatments have advanced, the radiological and clinical assessment of patients with GBM has become even more challenging and nuanced. Advances in spatial resolution and both anatomical and physiological information that can be derived from MRI have greatly improved the noninvasive assessment of GBM before, during, and after therapy. Identification of pseudoprogression (PsP), defined as changes concerning for tumor progression that are, in fact, transient and related to treatment response, is critical for successful patient management. These temporary changes can produce new clinical symptoms due to mass effect and edema. Differentiating this entity from true tumor progression is a major decision point in the patient's management and prognosis. Providers may choose to start an alternative therapy, transition to a clinical trial, consider repeat resection, or continue with the current therapy in hopes of resolution. In this review, the authors describe the invasive and noninvasive techniques neurosurgeons need to be aware of to identify PsP and facilitate surgical decision-making.

Post-Surgical Depositions of Blood Products Are No Major Confounder for the Diagnostic and Prognostic Performance of CEST MRI in Patients with Glioma

Amide proton transfer (APT) and semi-solid magnetization transfer (ssMT) imaging can predict clinical outcomes in patients with glioma. However, the treatment of brain tumors is accompanied by the deposition of blood products within the tumor area in most cases. For this reason, the objective was to assess whether the diagnostic interpretation of the APT and ssMT is affected by methemoglobin (mHb) and hemosiderin (Hs) depositions at the first follow-up MRI 4 to 6 weeks after the completion of radiotherapy. A total of 34 participants underwent APT and ssMT imaging by applying reconstruction methods described by Zhou et al. (APTwasym), Goerke et al. (MTRRexAPT and MTRRexMT) and Mehrabian et al. (MTconst). Contrast-enhancing tumor (CE), whole tumor (WT), mHb and Hs were segmented on contrast-enhanced T1wCE, T2w-FLAIR, T1w and T2*w images. ROC-analysis, Kaplan-Meier analysis and the log rank test were used to test for the association of mean contrast values with therapy response and overall survival (OS) before (WT and CE) and after correcting tumor volumes for mHb and Hs (CEC and WTC). CEC showed higher associations of the MTRRexMT with therapy response (CE: AUC = 0.677, p = 0.081; CEC: AUC = 0.705, p = 0.044) and of the APTwasym with OS (CE: HR = 2.634, p = 0.040; CEC: HR = 2.240, p = 0.095). In contrast, WTC showed a lower association of the APTwasym with survival (WT: HR = 2.304, p = 0.0849; WTC: HR = 2.990, p = 0.020). Overall, a sophisticated correction for blood products did not substantially influence the clinical performance of APT and ssMT imaging in patients with glioma early after radiotherapy.

A Multidimensional Connectomics- and Radiomics-Based Advanced Machine-Learning Framework to Distinguish Radiation Necrosis from True Progression in Brain Metastases

We introduce tumor connectomics, a novel MRI-based complex graph theory framework that describes the intricate network of relationships within the tumor and surrounding tissue, and combine this with multiparametric radiomics (mpRad) in a machine-learning approach to distinguish radiation necrosis (RN) from true progression (TP). Pathologically confirmed cases of RN vs. TP in brain metastases treated with SRS were included from a single institution. The region of interest was manually segmented as the single largest diameter of the T1 post-contrast (T1C) lesion plus the corresponding area of T2 FLAIR hyperintensity. There were 40 mpRad features and 6 connectomics features extracted, as well as 5 clinical and treatment factors. We developed an Integrated Radiomics Informatics System (IRIS) based on an Isomap support vector machine (IsoSVM) model to distinguish TP from RN using leave-one-out cross-validation. Class imbalance was resolved with differential misclassification weighting during model training using the IRIS. In total, 135 lesions in 110 patients were analyzed, including 43 cases (31.9%) of pathologically proven RN and 92 cases (68.1%) of TP. The top-performing connectomics features were three centrality measures of degree, betweenness, and eigenvector centralities. Combining these with the 10 top-performing mpRad features, an optimized IsoSVM model was able to produce a sensitivity of 0.87, specificity of 0.84, AUC-ROC of 0.89 (95% CI: 0.82-0.94), and AUC-PR of 0.94 (95% CI: 0.87-0.97).

Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks

BACKGROUND

Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation.

METHODS

This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation.

RESULTS

The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method.

CONCLUSIONS

The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages.

Radionecrosis mimicking pseudo‑progression in a patient with lung cancer and brain metastasis following the combination of anti‑PD‑1 therapy and stereotactic radiosurgery: A case report

Brain metastases (BMs) usually develop in patients with non-small cell lung cancer. In addition to systemic therapy, radiation therapy and surgery, anti-programmed cell death-ligand 1 (PD-L1) therapy is another promising clinical anticancer treatment modality. However, the optimal timing and drug-drug interactions of anti-PD-L1 therapy with other combined treatments remain to be elucidated. Treatment with anti-PD-L1 therapy is associated with an increased risk of radionecrosis (RN) regardless of tumor histology. The present study described a case of RN in a patient with lung adenocarcinoma and with BM who received anti-PD-L1 therapy. Before anti-PD-L1 treatment, the patient received whole brain radiotherapy. During durvalumab treatment, the intracranial metastases regressed. The progression of intracranial lesions 9 months later prompted a second-line of therapy with PD-L1 inhibitor durvalumab and stereotactic radiotherapy (SRT). Despite stereotactic irradiation, the lesions progressed further, leading to surgical resection. On examination, RN was detected, but there was no evidence of metastatic lung cancer. The aim of the present study was to present the longitudinal change in magnetic resonance imaging in RN following STR and anti-PD-L1 combined therapy. The atypical image of RN is conditionally important for making an accurate preoperative diagnosis.

Advanced Magnetic Resonance Imaging in the Evaluation of Treated Glioblastoma: A Pictorial Essay

MRI plays a key role in the evaluation of post-treatment changes, both in the immediate post-operative period and during follow-up. There are many different treatment's lines and many different neuroradiological findings according to the treatment chosen and the clinical timepoint at which MRI is performed. Structural MRI is often insufficient to correctly interpret and define treatment-related changes. For that, advanced MRI modalities, including perfusion and permeability imaging, diffusion tensor imaging, and magnetic resonance spectroscopy, are increasingly utilized in clinical practice to characterize treatment effects more comprehensively. This article aims to provide an overview of the role of advanced MRI modalities in the evaluation of treated glioblastomas. For a didactic purpose, we choose to divide the treatment history in three main timepoints: post-surgery, during Stupp (first-line treatment) and at recurrence (second-line treatment). For each, a brief introduction, a temporal subdivision (when useful) or a specific drug-related paragraph were provided. Finally, the current trends and application of radiomics and artificial intelligence (AI) in the evaluation of treated GB have been outlined.

Impact of Postoperative Changes in Brain Anatomy on Target Volume Delineation for High-Grade Glioma

High-grade glioma has a poor prognosis, and radiation therapy plays a crucial role in its management. Every step of treatment planning should thus be optimised to maximise survival chances and minimise radiation-induced toxicity. Here, we compare structures needed for target volume delineation between an immediate postoperative magnetic resonance imaging (MRI) and a radiation treatment planning MRI to establish the need for the latter. Twenty-eight patients were included, with a median interval between MRIs (range) of 19.5 (8-50) days. There was a mean change in resection cavity position (range) of 3.04 ± 3.90 (0-22.1) mm, with greater positional changes in skull-distant (>25 mm) resection cavity borders when compared to skull-near (≤25 mm) counterparts (p < 0.001). The mean differences in resection cavity and surrounding oedema and FLAIR hyperintensity volumes were -32.0 ± 29.6% and -38.0 ± 25.0%, respectively, whereas the mean difference in midline shift (range) was -2.64 ± 2.73 (0-11) mm. These data indicate marked short-term volumetric changes and support the role of an MRI to aid in target volume delineation as close to radiation treatment start as possible. Planning adapted to the actual anatomy at the time of radiation limits the risk of geographic miss and might thus improve outcomes in patients undergoing adjuvant radiation for high-grade glioma.

CEST Imaging of the APT and ssMT predict the overall survival of patients with glioma at the first follow-up after completion of radiotherapy at 3T

BACKGROUND AND PURPOSE

Outcome prediction of patients with glioma early after the completion of radiotherapy represents a major clinical challenge. Previously, the prognostic value of chemical exchange saturation transfer (CEST) imaging has been demonstrated in patients with newly diagnosed glioma. The objective of this study was to assess the potential of amide proton transfer (APT)-, relayed nuclear Overhauser effect (rNOE)- and semi-solid magnetization transfer (ssMT)-imaging according to Zhou et al. (APTw_asym), Goerke et al. (MTR_RexAPT, MTR_RexNOE and MTR_RexMT) and Mehrabian et al. (PeakAreaAPT, PeakAreaNOE and MT_const) for the prognostication of the overall survival (OS) of patients with glioma at the first follow-up after the completion of radiotherapy.

MATERIALS AND METHODS

49 of 72 participants with diffuse glioma, who underwent CEST MRI at 3T between July 2018 and December 2021 4 to 6 weeks after the completion of radiotherapy, were analyzed. Contrast-enhancing tumor (CE) and whole tumor (WT) volumes were segmented on T2w-FLAIR and contrast-enhanced T1w images. Kaplan-Meier analysis and logrank-test were used for statistical analyses.

RESULTS

APTw imaging demonstrated the strongest association with OS (HR=4.66, p<0.001). The MT_const (HR=2.54, p=0.044) was associated with the OS of participants with residual contrast-enhancing glioma tissue, whilst the MTR_RexAPT (HR=2.44, p=0.056) showed a trend in this sub-cohort. The MTR_RexNOE, MTR_RexMT and PeakAreaNOE were not associated with survival.

CONCLUSION

Imaging of the APT and ssMT at the first follow-up 4 to 6 weeks after the completion of radiotherapy at 3T were associated with the overall survival of patients with glioma.

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.

Assessment and prediction of glioblastoma therapy response: challenges and opportunities

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

Radiological Biomarkers for Brain Metastases Prognosis: Quantitative Magnetic Resonance Imaging (MRI) Modalities As Non-invasive Biomarkers for the Effect of Radiotherapy

Radiotherapy effect is achieved by its ability to cause DNA damage and induce apoptosis. In contrast, radiation can induce tumor cells' proliferation, invasiveness, and epithelial-mesenchymal transition (EMT). Besides developing radioresistance, this paradoxical effect of radiotherapy is considered a challenging problem in the field of radiotherapy. This highlights the importance of developing new modalities to diagnose radioresistance early to avoid any unnecessary exposure to radiation and differentiate between metastases recurrence versus post-radiation changes. Quantitative magnetic resonance imaging (MRI) techniques including diffusion-weighted imaging (DWI), dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast-enhanced (DCE) represent potential biomarkers to diagnose metastases recurrence and radioresistance. In this review, we will focus on recent studies discussing the possibility of using DWI, DSC, ASL, and DCE to diagnose radioresistance and recurrence in patients with brain metastases.

Charged particle therapy for high-grade gliomas in adults: a systematic review

High-grade gliomas are the most common intracranial malignancies, and their current prognosis remains poor despite standard aggressive therapy. Charged particle beams have unique physical and biological properties, especially high relative biological effectiveness (RBE) of carbon ion beam might improve the clinical treatment outcomes of malignant gliomas. We systematically reviewed the safety, efficacy, and dosimetry of carbon-ion or proton radiotherapy to treat high-grade gliomas. The protocol is detailed in the online PROSPERO database, registration No. CRD42021258495. PubMed, EMBASE, Web of Science, and The Cochrane Library databases were collected for data analysis on charged particle radiotherapy for high-grade gliomas. Until July 2022, two independent reviewers extracted data based on inclusion and exclusion criteria. Eleven articles were eligible for further analysis. Overall survival rates were marginally higher in patients with the current standard of care than those receiving concurrent intensity-modulated radiotherapy plus temozolomide. The most common side effects of carbon-ion-related therapy were grade 1-2 (such as dermatitis, headache, and alopecia). Long-term toxicities (more than three to six months) usually present as radiation necrosis; however, toxicities higher than grade 3 were not observed. Similarly, dermatitis, headache, and alopecia are among the most common acute side effects of proton therapy treatment. Despite improvement in survival rates, the method of dose-escalation using proton boost is associated with severe brain necrosis which should not be clinically underestimated. Regarding dosimetry, two studies compared proton therapy and intensity-modulated radiation therapy plans. Proton therapy plans aimed to minimize dose exposure to non-target tissues while maintaining target coverage. The use of charged-particle radiotherapy seems to be effective with acceptable adverse effects when used either alone or as a boost. The tendency of survival outcome shows that carbon ion boost is seemingly superior to proton boost. The proton beam could provide good target coverage, and it seems to reduce dose exposure to contralateral organs at risk significantly. This can potentially reduce the treatment-related dose- and volume-related side effects in long-term survivors, such as neurocognitive impairment. High-quality randomized control trials should be conducted in the future. Moreover, Systemic therapeutic options that can be paired with charged particles are necessary.

A Novel Approach to Determining Tumor Progression Using a Three-Site Pilot Clinical Trial of Spectroscopic MRI-Guided Radiation Dose Escalation in Glioblastoma

Glioblastoma (GBM) is a fatal disease, with poor prognosis exacerbated by difficulty in assessing tumor extent with imaging. Spectroscopic MRI (sMRI) is a non-contrast imaging technique measuring endogenous metabolite levels of the brain that can serve as biomarkers for tumor extension. We completed a three-site study to assess survival benefits of GBM patients when treated with escalated radiation dose guided by metabolic abnormalities in sMRI. Escalated radiation led to complex post-treatment imaging, requiring unique approaches to discern tumor progression from radiation-related treatment effect through our quantitative imaging platform. The purpose of this study is to determine true tumor recurrence timepoints for patients in our dose-escalation multisite study using novel methodology and to report on median progression-free survival (PFS). Follow-up imaging for all 30 trial patients were collected, lesion volumes segmented and graphed, and imaging uploaded to our platform for visual interpretation. Eighteen months post-enrollment, the median PFS was 16.6 months with a median time to follow-up of 20.3 months. With this new treatment paradigm, incidence rate of tumor recurrence one year from treatment is 30% compared to 60-70% failure under standard care. Based on the delayed tumor progression and improved survival, a randomized phase II trial is under development (EAF211).

Imaging Recommendations for the Diagnosis, Staging, and Management of Adult Brain Tumors

Neuroimaging plays a pivotal role in the clinical practice of brain tumors aiding in the diagnosis, genotype prediction, preoperative planning, and prognostication. The brain tumors most commonly seen in adults are extra-axial lesions like meningioma, intra-axial lesions like gliomas and lesions of the pituitary gland. Clinical features may be localizing like partial seizures, weakness, and sensory disturbances or nonspecific like a headache. On clinical suspicion of a brain tumor, the primary investigative workup should focus on imaging. Other investigations like fundoscopy and electroencephalography may be performed depending on the clinical presentation. Obtaining a tissue sample after identifying a brain tumor on imaging is crucial for confirming the diagnosis and planning further treatment. Tissue sample may be obtained by techniques such as stereotactic biopsy or upfront surgery. The magnetic resonance (MR) imaging protocol needs to be standardized and includes conventional sequences like T1-weighted (T1W) imaging with and without contrast, T2w imaging, fluid-attenuated axial inversion recovery, diffusion-weighted imaging (DWI), susceptibility-weighted imaging, and advanced imaging sequences like MR perfusion and MR spectroscopy. Various tumor characteristics in each of these sequences can help us narrow down the differential diagnosis and also predict the grade of the tumor. Multidisciplinary co-ordination is needed for proper management and care of brain tumor patients. Treatment protocols need to be adapted and individualized for each patient depending on the age, general condition of the patient, histopathological characteristics, and genotype of the tumor. Treatment options include surgery, radiotherapy, and chemotherapy. Imaging also plays a vital role in post-treatment follow-up. Sequences like DWI, MR perfusion, and MR spectroscopy are useful to distinguish post-treatment effects like radiation necrosis and pseudoprogression from true recurrence. Radiological reporting of brain tumor images should follow a structured format to include all the elements that could have an impact on the treatment decisions in patients.

Exploring the Past, Present, and Future of Anti-Angiogenic Therapy in Glioblastoma

Glioblastoma, a WHO grade IV astrocytoma, constitutes approximately half of malignant tumors of the central nervous system. Despite technological advancements and aggressive multimodal treatment, prognosis remains dismal. The highly vascularized nature of glioblastoma enables the tumor cells to grow and invade the surrounding tissue, and vascular endothelial growth factor-A (VEGF-A) is a critical mediator of this process. Therefore, over the past decade, angiogenesis, and more specifically, the VEGF signaling pathway, has emerged as a therapeutic target for glioblastoma therapy. This led to the FDA approval of bevacizumab, a monoclonal antibody designed against VEGF-A, for treatment of recurrent glioblastoma. Despite the promising preclinical data and its theoretical effectiveness, bevacizumab has failed to improve patients' overall survival. Furthermore, several other anti-angiogenic agents that target the VEGF signaling pathway have also not demonstrated survival improvement. This suggests the presence of other compensatory angiogenic signaling pathways that surpass the anti-angiogenic effects of these agents and facilitate vascularization despite ongoing VEGF signaling inhibition. Herein, we review the current state of anti-angiogenic agents, discuss potential mechanisms of anti-angiogenic resistance, and suggest potential avenues to increase the efficacy of this therapeutic approach.

The Role of PET Imaging in the Differential Diagnosis between Radiation Necrosis and Recurrent Disease in Irradiated Adult-Type Diffuse Gliomas: A Systematic Review

Adult-type diffuse gliomas are treated with a multimodality treatment approach that includes radiotherapy both in the primary setting, and in the case of progressive or recurrent disease. Radiation necrosis represents a major complication of radiotherapy. Recurrent disease and treatment-related changes are often indistinguishable using conventional imaging methods. The present systematic review aims at assessing the diagnostic role of PET imaging using different radiopharmaceuticals in differentiating radiation necrosis and disease relapse in irradiated adult-type diffuse gliomas. We conducted a comprehensive literature search using the PubMed/MEDLINE and EMBASE databases for original research studies of interest. In total, 436 articles were assessed for eligibility. Ten original papers, published between 2014 and 2022, were selected. Four articles focused on [¹⁸F]FDG, seven on amino acid tracers ([¹⁸F]FET n = 3 and [¹¹C]MET n = 4), one on [¹¹C]CHO, and one on [⁶⁸Ga]Ga-PSMA. Visual assessment, semi-quantitative methods, and radiomics were applied for image analysis. Furthermore, 2/10 papers were comparative studies investigating different radiopharmaceuticals. The present review, the first one on the topic in light of the new 2021 CNS WHO classification, highlighted the usefulness of PET imaging in distinguishing radiation necrosis and tumour recurrence, but revealed high heterogeneity among studies.

Challenges and opportunities for advanced neuroimaging of glioblastoma

Glioblastoma is the most aggressive of glial tumours in adults. On conventional magnetic resonance (MR) imaging, these tumours are observed as irregular enhancing lesions with areas of infiltrating tumour and cortical expansion. More advanced imaging techniques including diffusion-weighted MRI, perfusion-weighted MRI, MR spectroscopy and positron emission tomography (PET) imaging have found widespread application to diagnostic challenges in the setting of first diagnosis, treatment planning and follow-up. This review aims to educate readers with regard to the strengths and weaknesses of the clinical application of these imaging techniques. For example, this review shows that the (semi)quantitative analysis of the mentioned advanced imaging tools was found useful for assessing tumour aggressiveness and tumour extent, and aids in the differentiation of tumour progression from treatment-related effects. Although these techniques may aid in the diagnostic work-up and (post-)treatment phase of glioblastoma, so far no unequivocal imaging strategy is available. Furthermore, the use and further development of artificial intelligence (AI)-based tools could greatly enhance neuroradiological practice by automating labour-intensive tasks such as tumour measurements, and by providing additional diagnostic information such as prediction of tumour genotype. Nevertheless, due to the fact that advanced imaging and AI-diagnostics is not part of response assessment criteria, there is no harmonised guidance on their use, while at the same time the lack of standardisation severely hampers the definition of uniform guidelines.

Apatinib combined with temozolomide treatment for pseudoprogression in glioblastoma: A case report

RATIONALE

Glioblastoma is the most common malignant tumor of the central nervous system, which originates from glial cells and corresponding precursors. Due to its strong invasion and rapid growth, the prognosis of patients after treatment is very poor and easy to relapse.

PATIENT CONCERNS

In August 2015, a 48 years old man with a relapse of glioblastoma.

DIAGNOSES

The patient was diagnosed by computed tomography, magnetic resonance imaging, and pathological biopsy in this case report.

INTERVENTIONS

The patient underwent 2 surgeries, radiotherapy, and multiple regular chemotherapy sessions over the next 6 years. Apatinib, an inhibitor of vascular endothelial growth factor receptor 2 was given to treat recurrent glioma.

OUTCOMES

It was found that radiotherapy combined with temozolomide administration often increased the size of the original lesion or produced a new glioblastoma lesion. The lesion development was similar to tumor progression, which was called pseudoprogression. And it significantly prolonged the survival of this patient.

LESSONS

Surgery, radiotherapy and chemotherapy with apatinib and temozolomide are effective to treat the patients with pseudoprogression in glioblastoma.

A Rapid Late Enhancement MRI Protocol Improves Differentiation between Brain Tumor Recurrence and Treatment-Related Contrast Enhancement of Brain Parenchyma

PURPOSE

Differentiation between tumor recurrence and treatment-related contrast enhancement in MRI can be difficult. Late enhancement MRI up to 75 min after contrast agent application has been shown to improve differentiation between tumor recurrence and treatment-related changes. We investigated the diagnostic performance of late enhancement using a rapid MRI protocol optimized for clinical workflow.

METHODS

Twenty-three patients with 28 lesions suspected for glioma recurrence underwent MRI including T1-MPRAGE-series acquired 2 and 20 min after contrast agent administration. Early contrast series were subtracted from late contrast series using motion correction. Contrast enhancing lesions were retrospectively and independently evaluated by two readers blinded to the patients' later clinical course and histology with or without the use of late enhancement series. Sensitivity, specificity, NPV, and PPV were calculated for both readers by comparing results of MRI with histological samples.

RESULTS

Using standard MR sequences, sensitivity, specificity, PPV, and NPV were 0.84, 0, 0.875, and 0 (reader 1) and 0.92, 0, 0.885, and 0 (reader 2), respectively. Early late enhancement increased sensitivity, specificity, PPV, and NPV to 1 for each value and for both readers. Inter-reader reliability increased from 0.632 (standard MRI sequences) to 1.0 (with early late enhancement).

CONCLUSION

The described rapid late enhancement MRI protocol improves MRI-based discrimination between tumor tissue and treatment-related changes of the brain parenchyma.

Application of 7T MRS to High-Grade Gliomas

MRS, including single-voxel spectroscopy and MR spectroscopic imaging, captures metabolites in high-grade gliomas. Emerging evidence indicates that 7T MRS may be more sensitive to aberrant metabolic activity than lower-field strength MRS. However, the literature on the use of 7T MRS to visualize high-grade gliomas has not been summarized. We aimed to identify metabolic information provided by 7T MRS, optimal spectroscopic sequences, and areas for improvement in and new applications for 7T MRS. Literature was found on PubMed using "high-grade glioma," "malignant glioma," "glioblastoma," "anaplastic astrocytoma," "7T," "MR spectroscopy," and "MR spectroscopic imaging." 7T MRS offers higher SNR, modestly improved spatial resolution, and better resolution of overlapping resonances. 7T MRS also yields reduced Cramér-Rao lower bound values. These features help to quantify D-2-hydroxyglutarate in isocitrate dehydrogenase 1 and 2 gliomas and to isolate variable glutamate, increased glutamine, and increased glycine with higher sensitivity and specificity. 7T MRS may better characterize tumor infiltration and treatment effect in high-grade gliomas, though further study is necessary. 7T MRS will benefit from increased sample size; reductions in field inhomogeneity, specific absorption rate, and acquisition time; and advanced editing techniques. These findings suggest that 7T MRS may advance understanding of high-grade glioma metabolism, with reduced Cramér-Rao lower bound values and better measurement of smaller metabolite signals. Nevertheless, 7T is not widely used clinically, and technical improvements are necessary. 7T MRS isolates metabolites that may be valuable therapeutic targets in high-grade gliomas, potentially resulting in wider ranging neuro-oncologic applications.

Multimodal MRI-Based Radiomic Nomogram for the Early Differentiation of Recurrence and Pseudoprogression of High-Grade Glioma

Objective

To evaluate the diagnostic value of multimodal MRI radiomics based on T2-weighted fluid attenuated inversion recovery imaging (T2WI-FLAIR) combined with T1-weighted contrast enhanced imaging (T1WI-CE) in the early differentiation of high-grade glioma recurrence from pseudoprogression.

Methods

A total of one hundred eighteen patients with brain gliomas who were diagnosed from March 2014 to April 2020 were retrospectively analyzed. According to the clinical characteristics, the patients were randomly split into a training group (n = 83) and a test group (n = 35) at a 7 : 3 ratio. The region of interest (ROI) was delineated, and 2632 radiomic features were extracted. We used multiple logistic regression to establish a classification model, including the T1 model, T2 model, and T1 + T2 model, to differentiate recurrence from pseudoprogression. The diagnostic efficiency of the model was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and accuracy (ACC) and by analyzing the calibration curve of the nomogram and decision curve.

Results

There were 75 cases of recurrence and 43 cases of pseudoprogression. The diagnostic efficacies of the multimodal MRI-based radiomic model were relatively high. The AUC values and ACC of the training group were 0.831 and 77.11%, respectively, and the AUC values and ACC of the test group were 0.829 and 88.57%, respectively. The calibration curve of the nomogram showed that the discrimination probability was consistent with the actual occurrence in the training group, and the discrimination probability was roughly the same as the actual occurrence in the test group. In the decision curve analysis, the T1 + T2 model showed greater overall net efficiency.

Conclusion

The multimodal MRI radiomic model has relatively high efficiency in the early differentiation of recurrence from pseudoprogression, and it could be helpful for clinicians in devising correct treatment plans so that patients can be treated in a timely and accurate manner.

Safety of Inhomogeneous Dose Distribution IMRT for High-Grade Glioma Reirradiation: A Prospective Phase I/II Trial (GLIORAD TRIAL)

Simple Summary

Glioblastoma multiforme (GBM) is the most frequent primary malignant brain tumor, and despite advances in imaging techniques and treatment options, the outcome remains poor and recurrence is inevitable. Salvage therapy presents a challenge, and re-irradiation can be a therapeutic option in recurrent GBM. The decision-making process for re-irradiation is a challenge for radiation oncologists due to the expected toxicity of a second course of radiotherapy and the limited radiation tolerance of normal tissue; nevertheless, it is being increasingly used, as several studies have demonstrated its feasibility. The current study aimed to investigate the safety of moderate–high-voxel-based dose escalation radiotherapy in recurrent GBM patients after conventional concurrent chemoradiation. Twelve patients were enrolled in this prospective single-center study. Retreatment consisted of re-irradiation with a total dose range of 30–50 Gy over 5 days using the IMRT (arc VMAT) technique using dose painting planning. The treatment was well tolerated. No toxicities greater than 3 were recorded; only one patient had severe G3 acute toxicity, characterized by muscle weakness and fatigue. Median overall survival (OS2) and progression-free survival (PFS2) from the time of re-irradiation were 10.4 months and 5.7 months, respectively. Our phase I study demonstrated an acceptable tolerance profile of this approach, and the future prospective phase II study will analyze the efficacy in terms of PFS and OS.

Abstract

Glioblastoma multiforme (GBM) is the most aggressive astrocytic primary brain tumor, and concurrent temozolomide (TMZ) and radiotherapy (RT) followed by maintenance of adjuvant TMZ is the current standard of care. Despite advances in imaging techniques and multi-modal treatment options, the median overall survival (OS) remains poor. As an alternative to surgery, re-irradiation (re-RT) can be a therapeutic option in recurrent GBM. Re-irradiation for brain tumors is increasingly used today, and several studies have demonstrated its feasibility. Besides differing techniques, the published data include a wide range of doses, emphasizing that no standard approach exists. The current study aimed to investigate the safety of moderate–high-voxel-based dose escalation in recurrent GBM. From 2016 to 2019, 12 patients met the inclusion criteria and were enrolled in this prospective single-center study. Retreatment consisted of re-irradiation with a total dose of 30 Gy (up to 50 Gy) over 5 days using the IMRT (arc VMAT) technique. A dose painting by numbers (DPBN)/dose escalation plan were performed, and a continuous relation between the voxel intensity of the functional image set and the risk of recurrence in that voxel were used to define target and dose distribution. Re-irradiation was well tolerated in all treated patients. No toxicities greater than G3 were recorded; only one patient had severe G3 acute toxicity, characterized by muscle weakness and fatigue. Median overall survival (OS2) and progression-free survival (PFS2) from the time of re-irradiation were 10.4 months and 5.7 months, respectively; 3-, 6-, and 12-month OS2 were 92%, 75%, and 42%, respectively; and 3-, 6-, and 12-month PFS2 were 83%, 42%, and 8%, respectively. Our work demonstrated a tolerable tolerance profile of this approach, and the future prospective phase II study will analyze the efficacy in terms of PFS and OS.

Iatrogenic Influence on Prognosis of Radiation-Induced Contrast Enhancements in Patients with Glioma WHO 1-3 following Photon and Proton Radiotherapy

BACKGROUND AND PURPOSE

Radiation-induced contrast enhancement (RICE) is a common side effect following radiotherapy for glioma, but both diagnosis and handling are challenging. Due to the potential risks associated with RICE and its challenges in differentiating RICE from tumor progression, it is critical to better understand how RICE prognosis depends on iatrogenic influence.

MATERIALS AND METHODS

We identified 99 patients diagnosed with RICE who were previously treated with either photon or proton therapy for World Health Organization (WHO) grade 1-3 primary gliomas. Post-treatment brain MRI-based volumetric analysis and clinical data collection was performed at multiple time points.

RESULTS

The most common histologic subtypes were astrocytoma (50%) and oligodendroglioma (46%). In 67%, it was graded WHO grade 2 and in 86% an IDH mutation was present. RICE first occurred after 16 months (range: 1 - 160) in median. At initial RICE occurrence, 39% were misinterpreted as tumor progression. A tumor-specific therapy including chemotherapy or re-irradiation led to a RICE size progression in 86% and 92% of cases, respectively and RICE symptom progression in 57% and 65% of cases, respectively. A RICE-specific therapy such as corticosteroids or Bevacizumab for larger or symptomatic RICE led to a RICE size regression in 81% of cases with symptom stability or regression in 62% of cases.

CONCLUSIONS

While with chemotherapy and re-irradiation a RICE progression was frequently observed, anti-edematous or anti-VEGF treatment frequently went along with a RICE regression. For RICE, correct diagnosis and treatment decisions are challenging and critical and should be made interdisciplinarily.

Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma

Simple Summary

Glioma is the most common primary malignant tumor of the adult central nervous system. Despite aggressive multimodal treatment, its prognosis remains poor. During follow-up, it remains challenging to distinguish treatment-related changes from tumor progression in treated patients with gliomas due to both share clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions). The early effective identification of tumor progression and treatment-related changes is of great significance for the prognosis and treatment of gliomas. We believe that advanced neuroimaging techniques can provide additional information for distinguishing both at an early stage. In this article, we focus on the research of magnetic resonance imaging technology and artificial intelligence in tumor progression and treatment-related changes. Finally, it provides new ideas and insights for clinical diagnosis.

Abstract

As the most common neuro-epithelial tumors of the central nervous system in adults, gliomas are highly malignant and easy to recurrence, with a dismal prognosis. Imaging studies are indispensable for tracking tumor progression (TP) or treatment-related changes (TRCs). During follow-up, distinguishing TRCs from TP in treated patients with gliomas remains challenging as both share similar clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions) and fulfill criteria for progression. Thus, the early identification of TP and TRCs is of great significance for determining the prognosis and treatment. Histopathological biopsy is currently the gold standard for TP and TRC diagnosis. However, the invasive nature of this technique limits its clinical application. Advanced imaging methods (e.g., diffusion magnetic resonance imaging (MRI), perfusion MRI, magnetic resonance spectroscopy (MRS), positron emission tomography (PET), amide proton transfer (APT) and artificial intelligence (AI)) provide a non-invasive and feasible technical means for identifying of TP and TRCs at an early stage, which have recently become research hotspots. This paper reviews the current research on using the abovementioned advanced imaging methods to identify TP and TRCs of gliomas. First, the review focuses on the pathological changes of the two entities to establish a theoretical basis for imaging identification. Then, it elaborates on the application of different imaging techniques and AI in identifying the two entities. Finally, the current challenges and future prospects of these techniques and methods are discussed.

Discriminators of pseudoprogression and true progression in high-grade gliomas: A systematic review and meta-analysis

High-grade gliomas remain the most common primary brain tumour with limited treatments options and early recurrence rates following adjuvant treatments. However, differentiating true tumour progression (TTP) from treatment-related effects or pseudoprogression (PsP), may critically influence subsequent management options. Structural MRI is routinely employed to evaluate treatment responses, but misdiagnosis of TTP or PsP may lead to continuation of ineffective or premature cessation of effective treatments, respectively. A systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses method. Embase, MEDLINE, Web of Science and Google Scholar were searched for methods applied to differentiate PsP and TTP, and studies were selected using pre-specified eligibility criteria. The sensitivity and specificity of included studies were summarised. Three of the identified methods were compared in a separate subgroup meta-analysis. Thirty studies assessing seven distinct neuroimaging methods in 1372 patients were included in the systematic review. The highest performing methods in the subgroup analysis were DWI (AUC = 0.93 [0.91-0.95]) and DSC-MRI (AUC = 0.93 [0.90-0.95]), compared to DCE-MRI (AUC = 0.90 [0.87-0.93]). 18F-fluoroethyltyrosine PET (18F-FET PET) and amide proton transfer-weighted MRI (APTw-MRI) also showed high diagnostic accuracy, but results were based on few low-powered studies. Both DWI and DSC-MRI performed with high sensitivity and specificity for differentiating PsP from TTP. Considering the technical parameters and feasibility of each identified method, the authors suggested that, at present, DSC-MRI technique holds the most clinical potential.

Differentiation between glioma recurrence and treatment effects using amide proton transfer imaging: A mini-Bayesian bivariate meta-analysis

Background

Amide proton transfer (APT) imaging as an emerging MRI approach has been used for distinguishing tumor recurrence (TR) and treatment effects (TEs) in glioma patients, but the initial results from recent studies are different.

Aim

The aim of this study is to systematically review and quantify the diagnostic performance of APT in assessing treatment response in patients with post-treatment gliomas.

Methods

A systematic search in PubMed, EMBASE, and the Web of Science was performed to retrieve related original studies. For the single and added value of APT imaging in distinguishing TR from TEs, we calculated pooled sensitivity and specificity by using Bayesian bivariate meta-analyses.

Results

Six studies were included, five of which reported on single APT imaging parameters and four of which reported on multiparametric MRI combined with APT imaging parameters. For single APT imaging parameters, the pooled sensitivity and specificity were 0.85 (95% CI: 0.75–0.92) and 0.88 (95% CI: 0.74–0.97). For multiparametric MRI including APT, the pooled sensitivity and specificity were 0.92 (95% CI: 0.85–0.97) and 0.83 (95% CI: 0.55–0.97), respectively. In addition, in the three studies reported on both single and added value of APT imaging parameters, the combined imaging parameters further improved diagnostic performance, yielding pooled sensitivity and specificity of 0.91 (95% CI: 0.80–0.97) and 0.92 (95% CI: 0.79–0.98), respectively, but the pooled sensitivity was 0.81 (95% CI: 0.65-0.93) and specificity was 0.82 (95% CI: 0.61–0.94) for single APT imaging parameters.

Conclusion

APT imaging showed high diagnostic performance in assessing treatment response in patients with post-treatment gliomas, and the addition of APT imaging to other advanced MRI techniques can improve the diagnostic accuracy for distinguishing TR from TE.

Simple approach for the histomolecular diagnosis of central nervous system gliomas based on 2021 World Health Organization Classification

The classification of central nervous system (CNS) glioma went through a sequence of developments, between 2006 and 2021, started with only histological approach then has been aided with a major emphasis on molecular signatures in the 4th and 5th editions of the World Health Organization (WHO). The recent reformation in the 5th edition of the WHO classification has focused more on the molecularly defined entities with better characterized natural histories as well as new tumor types and subtypes in the adult and pediatric populations. These new subclassified entities have been incorporated in the 5^th edition after the continuous exploration of new genomic, epigenomic and transcriptomic discovery. Indeed, the current guidelines of 2021 WHO classification of CNS tumors and European Association of Neuro-Oncology (EANO) exploited the molecular signatures in the diagnostic approach of CNS gliomas. Our current review presents a practical diagnostic approach for diffuse CNS gliomas and circumscribed astrocytomas using histomolecular criteria adopted by the recent WHO classification. We also describe the treatment strategies for these tumors based on EANO guidelines.

Advanced Neuroimaging Approaches to Pediatric Brain Tumors

Simple Summary

After leukemias, brain tumors are the most common cancers in children, and early, accurate diagnosis is critical to improve patient outcomes. Beyond the conventional imaging methods of computed tomography (CT) and magnetic resonance imaging (MRI), advanced neuroimaging techniques capable of both structural and functional imaging are moving to the forefront to improve the early detection and differential diagnosis of tumors of the central nervous system. Here, we review recent developments in neuroimaging techniques for pediatric brain tumors.

Abstract

Central nervous system tumors are the most common pediatric solid tumors; they are also the most lethal. Unlike adults, childhood brain tumors are mostly primary in origin and differ in type, location and molecular signature. Tumor characteristics (incidence, location, and type) vary with age. Children present with a variety of symptoms, making early accurate diagnosis challenging. Neuroimaging is key in the initial diagnosis and monitoring of pediatric brain tumors. Conventional anatomic imaging approaches (computed tomography (CT) and magnetic resonance imaging (MRI)) are useful for tumor detection but have limited utility differentiating tumor types and grades. Advanced MRI techniques (diffusion-weighed imaging, diffusion tensor imaging, functional MRI, arterial spin labeling perfusion imaging, MR spectroscopy, and MR elastography) provide additional and improved structural and functional information. Combined with positron emission tomography (PET) and single-photon emission CT (SPECT), advanced techniques provide functional information on tumor metabolism and physiology through the use of radiotracer probes. Radiomics and radiogenomics offer promising insight into the prediction of tumor subtype, post-treatment response to treatment, and prognostication. In this paper, a brief review of pediatric brain cancers, by type, is provided with a comprehensive description of advanced imaging techniques including clinical applications that are currently utilized for the assessment and evaluation of pediatric brain tumors.