True Tumor Progression Research Articles

Systemic inflammatory markers and volume of enhancing tissue on post-contrast T1w MRI images in differentiating true tumor progression from pseudoprogression in high-grade glioma

BackgroundHigh-grade glioma (HGG) patients post-radiotherapy often face challenges distinguishing true tumor progression (TTP) from pseudoprogression (PsP). This study evaluates the effectiveness of systemic inflammatory markers and volume of enhancing tissue on post-contrast T1 weighted (T1WCE) MRI images for this differentiation within the first six months after treatment. Material and MethodsWe conducted a retrospective analysis on a cohort of HGG patients from 2015 to 2021, categorized per WHO 2016 and 2021 criteria. We analyzed treatment responses using modified RANO criteria and conducted volumetry on T1WCE and T2W/FLAIR images.Blood parameters assessed included neutrophil/lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI). We employed Chi-square, Fisher’s exact test, and Mann-Whitney U test for statistical analyses, using log-transformed predictors due to multicollinearity. A Cox regression analysis assessed the impact of PsP- and TTP-related factors on overall survival (OS). ResultsThe cohort consisted of 39 patients, where 16 exhibited PsP and 23 showed TTP. Univariate analysis revealed significantly higher NLR and SII in the TTP group [NLR: 4.1 vs 7.3, p = 0.002; SII 546.5 vs 890.5p = 0.009]. T1WCE volume distinctly differentiated PsP from TTP [2.2 vs 11.7, p < 0.001]. In multivariate regression, significant predictors included NLR and T1WCE volume in the “NLR Model,” and T1WCE volume and SII in the “SII Model.” The study also found a significantly lower OS rate in TTP patients compared to those with PsP [HR 3.97, CI 1.59 to 9.93, p = 0.003]. ConclusionElevated both, SII and NLR, and increased T1WCE volume were effective in differentiating TTP from PsP in HGG patients post-radiotherapy. These results suggest the potential utility of incorporating these markers into clinical practice, though further research is necessary to confirm these findings in larger patient cohorts.

Resolving spatial response heterogeneity in glioblastoma.

Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future.

Letter to the Editor Regarding Cerebral Blood Flow Role in Delineating Treatment Effect from True Tumor Progression in Glioblastoma Multiforme

Letter to the Editor Regarding Cerebral Blood Flow Role in Delineating Treatment Effect from True Tumor Progression in Glioblastoma Multiforme

Radiogenomics-Based Risk Prediction of Glioblastoma Multiforme with Clinical Relevance.

Glioblastoma multiforme (GBM)is the most common and aggressive primary brain tumor. Although temozolomide (TMZ)-based radiochemotherapy improves overall GBM patients' survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudo-progression (PsP) is a treatment-related reaction with an increased contrast-enhancing lesion size at the tumor site or resection margins miming tumor recurrence on MRI. The accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate the tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or true tumor progression (TTP) from the Wake dataset. Based on these radiographic features, we conducted a radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were used as features to construct a 2YS (2-year survival rate) logistic regression model. GBM patients were classified into low- and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent The Cancer Genome Atlas Program (TCGA) dataset and found that 2YS scores were significantly associated with the patient's overall survival. We used two cohorts of the TCGA data to train and test our model. Our results show that the 2YS scores-based classification results from the training and testing TCGA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS (Karnofsky performance status), normal cell ratio) and found that these factors were unrelated or weakly correlated with patients' survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting the clinical outcomes of GBM patients after standard therapies.

Response assessment of GBM during immunotherapy by delayed contrast treatment response assessment maps.

Assessing the treatment response of glioblastoma multiforme during immunotherapy (IT) is an open issue. Treatment response assessment maps (TRAMs) might help distinguish true tumor progression (TTP) and pseudoprogression (PsP) in this setting. We recruited 16 naïve glioblastoma patients enrolled in a phase II trial consisting of the Stupp protocol (a standardized treatment for glioblastoma involving combined radiotherapy and chemotherapy with temozolomide, followed by adjuvant temozolomide) plus IT with dendritic cells. Patients were followed up till progression or death; seven underwent a second surgery for suspected progression. Clinical, immunological, and MRI data were collected from all patients and histology in case of second surgery. Patients were classified as responders (progression-free survival, PFS > 12 months), and non-responders (PFS ≤ 12), HIGH-NK (natural killer cells, i.e., immunological responders), and LOW-NK (immunological non-responders) based on immune cell counts in peripheral blood. TRAMs differentiate contrast-enhancing lesions with different washout dynamics into hypothesized tumoral (conventionally blue-colored) vs. treatment-related (red-colored). Using receiver operating characteristic (ROC) curves, a threshold of -0.066 in VBlue/VCE (volume of the blue portion of tumoral area/volume of contrast enhancement) variation between values obtained in the MRI performed before PsP/TTP and at TTP/PSP allowed to discriminate TTP from PsP with a sensitivity of 71.4% and a specificity of 100%. Among HIGH-NK patients, at month 6 there was a significant reduction compared to baseline and month 2 in median "blue" volumes. In conclusion, in our pilot study TRAMs support the discrimination between tumoral and treatment-related enhancing features in immunological responders vs. non-responders, the distinction between PsP and TTP, and might provide surrogate markers of immunological response.

Protoporphyrin IX in serum of high-grade glioma patients: A novel target for disease monitoring via liquid biopsy.

High-grade gliomas (HGG) carry a dismal prognosis. Diagnosis comprises MRI followed by histopathological evaluation of tissue; no blood biomarker is available. Patients are subjected to serial MRIs and, if unclear,surgery for monitoring of tumor recurrence, which is laborious. MRI provides only limited diagnostic information regarding the differentiation of true tumor progression from therapy-associated side effects. 5-aminolevulinic acid (5-ALA)is routinely used for induction of protoporphyrin IX (PpIX) accumulation in malignant glioma tissue, enablingimproved tumor visualizationduring fluorescence-guided resection (FGR). We investigated whether PpIX can also serve as a serum HGG marker to monitor relapse. Patients (HGG: n = 23 primary, pHGG; n = 5 recurrent, rHGG) undergoing FGR received 5-ALA following standard clinical procedure. The control group of eight healthy volunteers (HCTR) also received 5-ALA. Serum was collected before and repeatedly up to 72h after drug administration. Significant PpIX accumulation in HGG was observed after 5-ALA administration (ANOVA: p = 0.005, post-hoc: HCTR vs. pHGG p = 0.029, HCTR vs. rHGG p = 0.006). Separation of HCTR from pHGG was possible when maximum serum PpIX levels were reached (CI95% of tMax). ROC analysis of serum PpIX within CI95% of tMax showed successful classification of HCTR and pHGG (AUCROC 0.943, CI95% 0.884-1.000, p < 0.001); the optimal cut-off for diagnosis was 1275pmolPpIX/ml serum, reaching 87.0% accuracy, 90.5% positive predictive and 84.0% negative predictive value. Baseline PpIX level was similar in patient and control groups. Thus, 5-ALA is required for PpIX induction, which is safe at the standard clinical dosage. PpIX is a new target for liquid biopsy in glioma. More extensive clinical studies are required to characterize its full potential.

BIOM-07. NON-TUMOR PLASMA EXTRACELLULAR VESICLES PREDICT THE OCCURRENCE OF GBM: A MACHINE LEARNING ANALYSIS

Abstract INTRODUCTION Glioblastoma (GBM) is the most common primary malignant brain tumor and has a poor clinical outcome. Extracellular vesicles (EVs) are promising biomarkers to assist the differential diagnosis between pseudoprogression and true tumor progression. We aimed to identify non-tumor plasma EV markers that predicted GBM occurrence using trained multiple tree-based machine learning algorithms. METHODS We included plasma samples of 40 GBM and 40 non-GBM (20 brain metastasis, 20 normal donors). Spectral flow cytometry was performed to determine plasma EV subpopulation frequency based on CD9,CD81,CD63,CD11b,CD45,CD31, and CD41a expression. Multiparametric t-SNE and FlowSOM analysis clustered plasma EVs based on these markers. Decision trees and random forest classifiers were trained and tested to predict the disease status (GBM vs non-GBM) based on plasma EV subpopulations. RESULTS Compared to non-GBM, GBM had increased frequency of CD9+ EVs (64.52% vs 40.69%, p&amp;lt; 0.0001), CD9+CD63-CD81- EVs (54.45% vs 37.26%, p&amp;lt; 0.0001) and myeloid-derived EVs (CD9+CD11b+ EVs, 20.77% vs 12.48%, p = 0.0241). In the decision tree analysis, low frequency of CD9+ EVs was found in 44% of all cases and represented a chance of 75% to be classified as non-GBM cases. An algorithm based on random forest analysis (n = 500 trees) using these markers had a sensitivity of 85.71% and specificity of 77.8% with an overall accuracy of 81.25% for identifying GBM. The frequencies of CD9+, CD9+CD63-CD81-, and CD9+CD63+CD81+ were the most critical factors in predicting GBM diagnosis. FlowSOM analysis identified 20 plasma EV subpopulations but had a lower accuracy (62.5%) when combined with our machine learning algorithm. CONCLUSIONS Non-tumor plasma EV expression profile combined with tree-based machine learning demonstrated high accuracy in predicting patient disease status. CD9+ EV frequency was one of the most promising biomarkers to assist in this diagnosis. We will continue to test our established algorithm and further validate our findings in a larger cohort.

NCMP-12. PREVALENCE OF RADIONECROSIS IN RECURRENT GLIOBLASTOMA

Abstract INTRODUCTION Glioblastoma (GBM) standard treatment consists of maximum and safe surgical resection, followed by radiation therapy in combination with temozolomide. Some patients show radiological and/or clinical deterioration soon after radiotherapy treatment. Early MRI changes, in the first 3 months following radiation is considered pseudo-progression. After this initial period, changes are considered as tumor recurrence. However, cases of early radiological deterioration have been related to radionecrosis. When tumor progression is suspected on imaging, it may causes treatment withdrawal or change. Therefore, for a certain percentage of patients, effective treatment could be abandoned in the presence of radionecrosis. METHODS The aim of the study is to assess the prevalence of radionecrosis at the Hôpital de l'Enfant-Jésus. This is a retrospective, observational case-control study evaluating recurrent GBM patients who underwent a second resection surgery. RESULTS The charts of 110 GBM patients with second resection were retrospectively reviewed (men: 62%, mean age: 58.3 years). Among these patients, we observed 87 cases of GBM recurrence as compared to 23 cases of radionecrosis (21%). No statistically significant difference was observed between the two groups regarding demographics, survival, Karnofsky performance status, type of surgery and treatments received. However, in the radionecrosis group, decreases in time between the 2 surgeries (55.3 vs. 24.3 weeks, p = 0.006) and in the number of cycles of chemotherapy received (5.7 vs. 3.7 weeks, p = 0.004) were observed as compared to GBM group. CONCLUSIONS This study characterizes demographically the patients with second surgery for suspicion of GBM in order to better adapt the treatments. Clinical and radiological data could not differentiate radionecrosis from true tumor recurrence or progression. A prospective study will be carried out as a second step, in order to determine radiological or molecular factors that could predict the presence of radionecrosis.

P15.08.A MRI PHENOTYPES OF GLIOBLASTOMAS EARLY AFTER TREATMENT ARE SUGGESTIVE OF OVERALL PATIENT SURVIVAL

Abstract BACKGROUND Distinguishing between true tumor progression (TP) and treatment induced abnormalities (e.g. pseudo-progression (PP) after radiotherapy) early after treatment on conventional MRI scans remains challenging for glioblastomas (GBM). Despite MRI perfusion techniques being promising in differentiating between TP and PP, individual imaging markers have only showed a modest association with tumor progression and overall survival. Therefore, we aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of radiological scoring of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall-survival time. MATERIAL AND METHODS MR images and clinical data were retrospectively collected from 67 histologically confirmed GBM patients. Non-invasive (arterial spin labeling, ASL) and contrast-based (dynamic susceptibility contrast DSC) perfusion MRI, T2-FLAIR and post-contrast 3D-T1w scans were acquired on a 3T-MR scanner at around 3 months post-radiotherapy. Structural (presence and type of (enhancing) lesion patterns) and perfusion (increase, decrease or iso) MRI markers (MRIm) were visually scored by a neuroradiologist. Additionally, volume and eccentricity of the enhancing and T2-hyperintense areas were calculated.Subsequently, patients were grouped based on their similarity on tumor perfusion and structural brain MRIm by means of hierarchical clustering. These subgroups were then compared using a chi-square test and one way ANOVA. Survival outcome was assessed using a multivariate cox proportional hazard model, both unadjusted and adjusted for age, KPS and extension of resection. Progression at 9 months (when unavailable at 3/6 months) post-radiotherapy and clinical parameters were compared between subgroups using a chi-square test. RESULTS Four subgroups of patients were established with significantly different tumor perfusion and structural radiological markers (p≤0.05) and therefore showed distinct MRI phenotypes of GBM. Subgroup 1 had less aggressive MRIm with mostly nodular enhancing lesions; subgroup 2 had the least aggressive MRIm with mostly patchy enhancing lesions; subgroup 3 showed the most aggressive MRIm and subgroup 4 showed moderately aggressive MRIm. TP and PP did not differ significantly between subgroups (p = 0.775). However, after performing an adjusted and unadjusted cox regression analysis we found that subgroup 1 and 3 were at increased risk of mortality, respectively (subgroup 1 - HR: 2.649 (95% CI: 1.119 - 6.270; subgroup 3 - HR: 2.363 (95% CI: 1.110 - 5.030); p=0.03). CONCLUSION Our study suggests that differences in MRI phenotypes of GBM at 3 months post-radiotherapy can be indicative of overall patient survival. The early prognostic information our method provides might in the future be beneficial for treatment decisions in GBM patients.

P11.71.B THE MULTIDISCIPLINARY METHOD TO DISTINGUISH TRUE TUMOUR PROGRESSION FROM PSEUDOPROGRESSION IN HIGH GRADE GLIOMA VIA THE SYSTEMIC INFLAMMATORY MARKERS

Abstract BACKGROUND To identify the role of serum inflammatory markers and T1-gadolinium volume (T1GV) post-radiotherapy (RT), in the multidisciplinary approach to differentiate true tumor progression (TTP) from pseudo-progression (PsP) in high-grade glioma (HGG) patients. MATERIAL AND METHODS A retrospective cohort of HGG (WHO 2021) was consecutively enrolled from 2015 to 2021. Clinical, instrumental, and therapeutic data were collected at diagnosis, treatment and within 6 months after the end of RT.The response to treatment was assessed according to the modified RANO criteria; volumetry of suspected progression was performed on T1G and FLAIR. Blood tests also included the analysis of the neutrophil/lymphocyte ratio (NLR), systemic immune-inflammatory index (SII) and systemic inflammatory response index (SIRI). Patients with suspected progression underwent a new MRI within 3 months of the first one.Any association between the clinicopathological factors and the presence of progression were analysed by Chi-square and Fisher’s exact test. Differences between group in continuous variables were assessed by independent sample-T test or Mann-Whitney U test, as appropriate. Prognostic factors for progression were investigated with a regression logistic model where predictors were log-transformed owing to their skewed distribution. Due to an existing multicollinearity in the common multivariate regression model between the variables “NLR” and “SII index”, two further separate binary regression models were set up, whereby in the “Multivariate Model NLR” the SII index was not considered and in the “Multivariate Model SII index” the NLR was not included. A Cox regression analysis was conducted to assess the impact of PsP- and TTP-related factors on overall survival (OS). RESULTS 39 patients (41.0% females) were included, 16 showed PsP and 23 TTP.NLR, SII and T1 gadolinium volume were higher in the TTP group than in PsP (respectively 4.7 vs 2.8, p= 0.002; 890.5 vs 546.5, p = 0.009; and 11.7 cm3 vs 2.2 cm3, p&amp;lt;0.001 ). Also absolute neutrophil counts and SIRI were higher in the TTP group than in PsP, but without reaching statistical significance.A multivariate “NLR” regression model showed NLR (OR 7.9, 95%CI: 1.4 to 45.3, p=0.020) and T1GV (OR 3.0, 95%CI: 1.4 to 6.7, p=0.007) as predictors of PSP; whereas the “SII model” confirmed the effect of T1GV [OR 2.7, CI 95% 1.3 to 5.5, p=0.006)] and SII [OR 4.2, 95%CI: 1.1 to 15.3, p=0.030].The OS was significantly worsened in patients with TTP compared to patients with PsP [HR 3.97, 95%CI: 1.59 to 9.93, p = 0.003]. At multivariate analysis other factors did not impact the overall survival. CONCLUSION Higher NLR, SII and T1GV showed good sensitivity and specificity to differentiate between PsP and TTP.Further studies with larger cohorts are needed to evaluate the inclusion of inflammatory indices and volumetry in the differentiation diagram of PsP from TTP.

P15.06.A HYBRID [18F]FET PET-MRI; A VALUABLE ADVANCED IMAGING TOOL TO DISCRIMINATE BETWEEN TUMOR RECURRENCE AND PSEUDOPROGRESSION IN GLIOMA PATIENTS

Abstract BACKGROUND One of the well-known challenges in the response assessment of glioma patients is differentiating tumor recurrence from treatment-related effects, i.e. pseudoprogression. The PET tracer O-(2-18F-fluoroethyl)-L-tyrosine ([18F]FET) visualizes increased amino acid metabolism and can potentially aid differentiation between vital tumor tissue and reactive changes, and can herewith guide clinical decision-making and surgical planning. This study aimed to assess the diagnostic yield of hybrid [18F]FET PET-MRI in a series of pre-treated patients suspected of glioma recurrence. MATERIAL AND METHODS This retrospective study included histologically-confirmed glioma patients who had undergone standard-of-care therapy consisting of gross-total resection surgery and/or radiotherapy. [18F]FET PET-MRI scans were performed between 04/2022 and 03/2023. Patient received 204 MBq (median, range: 180-242) [18F]FET intravenously, followed directly by a 45 minute dynamic PET and simultaneous MRI including DWI and gadolinium-based sequences (PWI). Time-activity-curves (TACs), showing the mean tissue radioactivity in tumor regions of interest as a function of time, were compared to the TAC in healthy tissue. Static PET images for qualitative visual analysis were reconstructed by accumulating the 20-40 minute timeframes. Tumor recurrence (i.e., a positive scan) was defined as focal uptake exceeding uptake in healthy brain parenchyma according to the joint EANM/EANO/RANO practice guideline/SNMMI procedure standard. Outcomes were compared to previously-obtained MRIs, post-operative pathology, and clinical/radiological follow-up. RESULTS Thirteen patients, 31% female, median age 39 (range: 28-50), with astrocytoma (grade 2_n=2; grade 3_n=1; grade 4_n=2), oligodendroglioma (grade 2_n=6; grade 3_n=1), and glioblastoma (n=1) were included. [18F]FET PET-MRI was positive in 9/13 patients (70%), which in 5/9 (55%) was confirmed by pathology (n=3) or clinical follow-up (n=2). In 6/9 patients with a positive scan (66%), the previously-obtained MRI (time interval range: 0.5-4.1 months) showed negative (n=4) or unclear (n=2) DWI and PWI findings. Four/13 (30%) patients had a negative [18F]FET PET-MRI and did not show signs of tumor progression after an average follow-up of 3, 5, 11 and 12 months, respectively. CONCLUSION Simultaneous [18F]FET PET-MRI shows high potential for accurate differentiation between true tumor progression and treatment-related pseudoprogression in glioma patients with equivocal findings on MRI including DWI and PWI. The diagnostic yield of this hybrid imaging technique exceeded that of MRI-only in our case series, and thus provides a promising advanced imaging tool in the follow-up of glioma patients. We aim at prospective studies to validate the clinical value of this multimodal approach.

P15.14.A DIFFERENTIATE TREATMENT EFFECTS FROM DISEASE PROGRESSION IN TREATED BRAIN TUMOR: PROMISING RESULTS USING DELAYED CONTRAST MRI

Abstract BACKGROUND Treatment effects are reported in about 30% of treated glioma and brain metastases (BMs) characterized by increasing volume of contrast enhancing regions on MRI, similarly to tumor progression. Data interpretation are often difficult. The most methodology used to distinguish this 2 conditions is perfusion MRI; treatment effects are characterized by a decreased relative cerebral blood volume (rCBV) while persistent or progressive tumors by high rCBV. Limits of perfusion MRI are represented by overlap between two conditions, low spatial resolution, and presence of susceptibility artifacts. To overcome this crucial issue, encouraging results have been obtained using delayed contrast MRI that through contrast clearance analysis (CCA) allows to calculate high resolution maps called “treatment response assessment maps” (TRAMs). We prospectively evaluated patients with doubtful framework between treatment effects (TE) and tumor progression (TP) aiming to assess the ability of CCA in differentiate this two conditions in a cohort of treated brain tumor patients. MATERIAL AND METHODS Maps (TRAMS) were calculated through contrast clearance analysis (CCA) by subtracting ceT1MRI images acquired 5 minutes after contrast injection from images acquired at least 60-105 minutes after. Diagnosis of persistent tumors or disease progression was based on presence of contrast clearance (CC) region in blue, and treatment effects on contrast accumulation (CA) region in red. Data of CCA were compared to perfusion MRI. All images were uploaded, co-registered and elaborate into the image workstation of Brainlab. Whole tumor and enhancing tumor volumes were segmented and perfusion measures in area of signal alteration. TRAMs were generated and at last the calculation of effective volume of viable tumor tissue (CC region) and treatment-induced tissue changes within each lesion (CA region) have been calculated. RESULTS From February 2021 and November 2022, 62 patient (96 delayed-contrast-MRI) with doubtful framework between TE and TP underwent CCA and perfusion-MRI. Twenty-five treated for newly diagnosed glioma, 13 for recurrence, and 37 for BMs. Significantly increased rCBV value in CC regions compared to CA was observed (p=0.01). Patients with TP showed significantly greater CC that subjects with stable disease/partial response 33% vs 78% (p=0.01). TRAMs metric were in agreement with perfusion results. Greater accuracy of TRAMs compared to perfusion-MRI in discriminate TE from TP 81% vs 69% has been observed. CONCLUSION In our experience CCA tool compared to perfusion MRI had a greater accuracy in discriminating true tumor progression from treatment induced tissue changes, and can be of improvement in patient management.

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.

[18F]-fluoroethyl-L-tyrosine (FET) in glioblastoma (FIG) TROG 18.06 study: protocol for a prospective, multicentre PET/CT trial

IntroductionGlioblastoma is the most common aggressive primary central nervous system cancer in adults characterised by uniformly poor survival. Despite maximal safe resection and postoperative radiotherapy with concurrent and adjuvant temozolomide-based...

109 A Small RNA Signature from Extracellular Vesicles in Patient Plasma Correlates With Recurrence or Progression of High-Grade Gliomas

INTRODUCTION: Glial cell-derived tumors of the central nervous system make up the largest group of brain tumors. Most are high-grade gliomas (HGG) and are universally fatal despite multimodal therapy. In managing patients with HGG, predicting recurrence, or differentiating between pseudoprogression (radiation necrosis) and true tumor progression would be invaluable in improving overall prognosis. Characterizing small RNA (sRNA) expression profiles from plasma-derived extracellular vesicles (EVs) over the course of a patient’s treatments, may allow for patient-specific treatment modifications and improve outcomes. METHODS: Plasma EVs were isolated using Vn96 capture from HGG patients perioperatively and with routine, follow-up surveillance imaging. Small RNA sequencing was conducted and unsupervised hierarchal clustering of sRNA signatures were completed. Expression profiles were grouped longitudinally with the clinical status of patients. RESULTS: Cluster analysis of nine HGG patients, has revealed a sRNA signature that is able to distinguish between tumors showing evidence of progression and those remaining stable over time. Those samples obtained from patients where a clinical diagnosis of tumor progression or pseudoprogression were uncertain, were found to cluster into progression vs. stable signatures. Annotation of differentially expressed small RNAs identified from stable vs. clinically progressed patient include small nuclear RNA, ribosomal RNA, and micro RNA among others. MiRNA species we have identified correspond to previously identified miRNA, which have been ascribed functional significance to HGG aggressiveness and progression. CONCLUSIONS: These preliminary findings demonstrate the potential utility of sRNA profiling of plasma-derived EVs obtained from patients with high-grade gliomas as non-invasive biomarkers for recurrent/progressive disease or stability/pseudoprogression.

Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM)

PurposeThe Checklist for Artificial Intelligence in Medical Imaging (CLAIM) is a recently released guideline designed for the optimal reporting methodology of artificial intelligence (AI) studies. Gliomas are the most common form of primary malignant brain tumour and numerous outcomes derived from AI algorithms such as grading, survival, treatment-related effects and molecular status have been reported. The aim of the study is to evaluate the AI reporting methodology for outcomes relating to gliomas in magnetic resonance imaging (MRI) using the CLAIM criteria.MethodsA literature search was performed on three databases pertaining to AI augmentation of glioma MRI, published between the start of 2018 and the end of 2021ResultsA total of 4308 articles were identified and 138 articles remained after screening. These articles were categorised into four main AI tasks: grading (n= 44), predicting molecular status (n= 50), predicting survival (n= 25) and distinguishing true tumour progression from treatment-related effects (n= 10). The average CLAIM score was 20/42 (range: 10–31). Studies most consistently reported the scientific background and clinical role of their AI approach. Areas of improvement were identified in the reporting of data collection, data management, ground truth and validation of AI performance.ConclusionAI may be a means of producing high-accuracy results for certain tasks in glioma MRI; however, there remain issues with reporting quality. AI reporting guidelines may aid in a more reproducible and standardised approach to reporting and will aid in clinical integration.

Diagnosis of Glioblastoma Multiforme Progression via Interpretable Structure-Constrained Graph Neural Networks.

Glioblastoma multiforme (GBM) is the most common type of brain tumors with high recurrence and mortality rates. After chemotherapy treatment, GBM patients still show a high rate of differentiating pseudoprogression (PsP), which is often confused as true tumor progression (TTP) due to high phenotypical similarities. Thus, it is crucial to construct an automated diagnosis model for differentiating between these two types of glioma progression. However, attaining this goal is impeded by the limited data availability and the high demand for interpretability in clinical settings. In this work, we propose an interpretable structure-constrained graph neural network (ISGNN) with enhanced features to automatically discriminate between PsP and TTP. This network employs a metric-based meta-learning strategy to aggregate class-specific graph nodes, focus on meta-tasks associated with various small graphs, thus improving the classification performance on small-scale datasets. Specifically, a node feature enhancement module is proposed to account for the relative importance of node features and enhance their distinguishability through inductive learning. A graph generation constraint module enables learning reasonable graph structures to improve the efficiency of information diffusion while avoiding propagation errors. Furthermore, model interpretability can be naturally enhanced based on the learned node features and graph structures that are closely related to the classification results. Comprehensive experimental evaluation of our method demonstrated excellent interpretable results in the diagnosis of glioma progression. In general, our work provides a novel systematic GNN approach for dealing with data scarcity and enhancing decision interpretability. Our source codes will be released at https://github.com/SJTUBME-QianLab/GBM-GNN.

Labeling T Cells to Track Immune Response to Immunotherapy in Glioblastoma.

While the advent of immunotherapy has revolutionized cancer treatment, its use in the treatment of glioblastoma (GBM) has been less successful. Most studies using immunotherapy in GBM have been negative and the reasons for this are still being studied. In clinical practice, interpreting response to immunotherapy has been challenging, particularly when trying to differentiate between treatment-related changes (i.e., pseudoprogression) or true tumor progression. T cell tagging is one promising technique to noninvasively monitor treatment efficacy by assessing the migration, expansion, and engagement of T cells and their ability to target tumor cells at the tumor site.

Pseudoprogression in GBM versus true progression in patients with glioblastoma: A multiapproach analysis

Background and purposeTo investigate the feasibility of using a multiapproach analysis combining clinical data, diffusion- and perfusion-weighted imaging, and 3D magnetic resonance spectroscopic imaging to distinguish true tumor progression (TP) from pseudoprogression (PSP) in patients with glioblastoma. Materials and methodsProgression was suspected within 6 months of radiotherapy in 46 of the 180 patients included in the Phase-III SpectroGlio trial (NCT01507506). Choline/creatine (Cho/Cr), choline/N-acetyl aspartate (Cho/NAA) and lactate/N-acetyl aspartate (Lac/NAA) ratios were extracted. Apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps were calculated. ADC, relative CBV values and tumor volume (TV) were collected at relapse. Differences between TP and PSP were evaluated using Mann–Whitney tests, and p values were adjusted with Bonferroni correction. ResultsPatients with suspected progression underwent a new MRI scan 1 month after the first one. Of these, 28 were classified as PSP, and 18 as TP. After a median follow-up of 41 months, median overall survival was higher in PSP than in TP (25.2 vs 20.3 months; p = 0.0092). Lac/NAA and Cho/Cr ratios were higher in TP than in PSP (1.2 vs 0.5; p = 0.006; and 3 vs 2.2; p = 0.021). After multivariate regression analysis, TV was the most significant predictor of TP vs PSP, and the only one retained in the model (p = 0.028). ConclusionThree spectroscopic ratios could be used to differentiate PSP from TP. TV at relapse was the most predictive factor in the multivariate analysis, and overall survival was higher in PSP than in TP.

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

Distinguishing true tumor progression (TP) from treatment-induced abnormalities (eg, pseudo-progression (PP) after radiotherapy) on conventional MRI scans remains challenging in patients with a glioblastoma. We aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall survival time. Structural and perfusion MR images of 67 patients at 3 months post-radiotherapy were visually scored by a neuroradiologist. In total 23 parameters were predefined and used for hierarchical clustering analysis. Progression status was assessed based on the clinical course of each patient 9 months after radiotherapy (or latest available). Multivariable Cox regression models were used to determine the association between the phenotypes, recurrence rate, and overall survival. We established 4 subgroups with significantly different tumor MRI characteristics, representing distinct MRI phenotypes of glioblastomas: TP and PP rates did not differ significantly between subgroups. Regression analysis showed that patients in subgroup 1 (characterized by having mostly small and ellipsoid nodular enhancing lesions with some hyper-perfusion) had a significant association with increased mortality at 9 months (HR: 2.6 (CI: 1.1-6.3); P = .03) with a median survival time of 13 months (compared to 22 months of subgroup 2). Our study suggests that distinct MRI phenotypes of glioblastomas at 3 months post-radiotherapy can be indicative of overall survival, but does not aid in differentiating TP from PP. The early prognostic information our method provides might in the future be informative for prognostication of glioblastoma patients.