Delayed FDG PET Provides Superior Glioblastoma Conspicuity Compared to Conventional Image Timing

Background: Glioblastomas are malignant, often incurable brain tumors. Reliable discrimination between recurrent disease and treatment changes is a significant challenge. Prior work has suggested glioblastoma FDG PET conspicuity is improved at delayed time points vs. conventional imaging times. This study aimed to determine the ideal FDG imaging time point in a population of untreated glioblastomas in preparation for future trials involving the non-invasive assessment of true progression vs. pseudoprogression in glioblastoma.Methods: Sixteen pre-treatment adults with suspected glioblastoma received FDG PET at 1, 5, and 8 h post-FDG injection within the 3 days prior to surgery. Maximum standard uptake values were measured at each timepoint for the central enhancing component of the lesion and the contralateral normal-appearing brain.Results: Sixteen patients (nine male) had pathology confirmed IDH-wildtype, glioblastoma. Our results revealed statistically significant improvements in the maximum standardized uptake values and subjective conspicuity of glioblastomas at later time points compared to the conventional (1 h time point). The tumor to background ratio at 1, 5, and 8 h was 1.4 ± 0.4, 1.8 ± 0.5, and 2.1 ± 0.6, respectively. This was statistically significant for the 5 h time point over the 1 h time point (p > 0.001), the 8 h time point over the 1 h time point (p = 0.026), and the 8 h time point over the 5 h time point (p = 0.036).Conclusions: Our findings demonstrate that delayed imaging time point provides superior conspicuity of glioblastoma compared to conventional imaging. Further research based on these results may translate into improvements in the determination of true progression from pseudoprogression.

P14.86 Predictive value of MGMT promoter (pMGMT) methylation status on pseudoprogression (PsP) and survival analysis in Glioblastoma (GBM) patients: a retrospective single institutional analysis

BACKGROUND Glioblastoma is the most common and aggressive primary brain tumor. Conventional therapies, such as maximal extension of surgery followed by radiotherapy (RT) and chemotherapy with Temozolomide (TMZ) have not resulted in major improvements in terms of patients’ outcome, overall survival (OS) still remaining poor. In this context, radiological response assessment after radiotherapy remains challenging due to the potential effect of radionecrosis, often mimicking tumor progression. Differentiation between PsP and true progression is required to avoid further unnecessary surgeries, or the premature discontinuation of TMZ. It is known that pMGMT methylated patients respond better to chemotherapy than unmethylated counterpart, so, tumor cells necrosis can be enhanced in this setting. The aim of the study is to observe the correlation between pMGMT methylation status with the incidence of PsP in GBM patients at the first radiological evaluation after RT. MATERIALS AND METHODS Patients with histologically diagnosis of GBM from 2017 to 2021 and availability of pMGMT methylation status were enrolled. PsP was radiologically defined at first brain MRI after RT in case of increasing size of the enhancing component and of peritumoral oedema that remain stable or decrease after antioedema therapy, such as a clinical improvement was observed. RESULTS We analysed 55 GBM patients, 35 (64%) displayed pMGMT methylation whereas 20 (36%) resulted pMGMT unmethylated. PsP was evident in 29 patients (53%), all of them showed methylation of pMGMT. In our analysis, none of pMGMT unmethylated patients experienced PsP. Regarding survival outcome for pMGMT methylated patients, our analysis shows a mPFS of 8.7 (95% CI: 5–10) months versus 9.3 (95%CI: 4.6–12.3) months in methylated and unmethylated respectively (p=0.87). CONCLUSIONS Methylation status of pMGMT showed to be predictor of PsP in GBM patients. If validated, this information could be very useful to guide clinicians in differentiating PsP from true progression. To date, our survival analysis regarding PFS showed no statistical difference among methylated patients with respect to the presence or absence of PsP. Thus, PsP seems not to be a marker of responsiveness to common treatment. Further data are needed to validate our results.

Classification of True-progression after Radiotherapy of Brain Metastasis on MRI using Artificial Intelligence: a Systematic Review and Meta-Analysis

Background Classification of true-progression from non-progression (e.g., radiation-necrosis) after stereotactic radiotherapy/radiosurgery of brain metastasis is known to be a challenging diagnostic task on conventional magnetic resonance imaging (MRI). The scope and status of research using artificial intelligence (AI) on classifying true-progression is yet unknown. Methods We performed a systematic literature search of MEDLINE and EMBASE databases to identify studies that investigated the performance of AI-assisted MRI in classifying true-progression after stereotactic radiotherapy/radiosurgery of brain metastasis, published before November 11th, 2020. Pooled sensitivity and specificity were calculated using bivariate random-effects modeling. Meta-regression was performed for identification of factors contributing to the heterogeneity among the studies. We assessed the quality of the studies using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria and a modified version of the radiomics quality score (RQS). Results 7 studies were included, with a total of 485 patients and 907 tumors. The pooled sensitivity and specificity were 77% (95% CI, 70–83%) and 74% (64–82%), respectively. All 7 studies used radiomics, and none used deep learning. Several covariates including the proportion of lung cancer as the primary site, MR field strength, and radiomics segmentation slice showed a statistically significant association with the heterogeneity. Study quality was overall favorable in terms of the QUADAS-2 criteria, but not in terms of the RQS. Conclusion The diagnostic performance of AI-assisted MRI seems yet inadequate to be used reliably in clinical practice. Future studies with improved methodologies and a larger training set are needed.

Redo Craniotomy or Bevacizumab For Symptomatic Steroid Refractory True Or Pseudoprogression Following IMRT For Glioblastoma

Objective There is minimal evidence to support decision-making for symptomatic steroid-refractory pseudoprogression or true progression occurring after IMRT for glioblastoma (GBM). This study audited the survival outcome of patients managed with redo craniotomy (RedoSx) or Bevacizumab (BEV) for steroid-refractory mass effect after IMRT for GBM. Methods Patients with GBM managed between 2008 and 2019 with the EORTC-NCIC Protocol were entered into a prospective database. Patients with symptomatic steroid-refractory mass effect within 6 months of IMRT managed with either RedoSx or BEV were identified for analysis. For the primary endpoint of median overall survival (OS) post intervention, outcome was analysed in regards to potential prognostic factors, and differences between groups assessed by log-rank analyses. Results Of 399 patients managed with the EORTC-NCIC Protocol, 78 required an intervention within 6 months of IMRT completion for either true or pseudoprogression (49 with RedoSx and 29 with BEV). Subsequently 20 of the 43 patients managed with RedoSx when BEV was clinically available, required salvage with BEV within 6 months after RedoSx. Median OS post intervention was 8.7 months (95%CI: 7.84-11.61) for the total group; and 8.7 months (95%CI: 6.8-13.1) for RedoSx and 9.4 months (95%CI: 7.8-13.6) for BEV (p=0.38). Subsequent use of BEV in RedoSx patients was not associated with improved survival compared with RedoSx alone (p=0.10).Age, time from IMRT, and ECOG performance status were not associated with OS. In the RedoSx patients, immunohistochemical features such as Ki67% reduction correlated with survival. Presence of pure necrosis and residual tumour cells only had improved survival compared with presence of gross tumour (p<0.001). Conclusions At time of symptomatic steroid-refractory true or pseudoprogression following IMRT for GBM, BEV was equivalent to RedoSx in terms of OS. Pseudoprogression with residual cells at RedoSx was not associated with worse outcome compared to pure necrosis.

The predictive value of absolute lymphocyte counts on tumor progression and pseudoprogression in patients with glioblastoma

Background Differentiating true glioblastoma multiforme (GBM) from pseudoprogression (PsP) remains a challenge with current standard magnetic resonance imaging (MRI). The objective of this study was to explore whether patients’ absolute lymphocyte count (ALC) levels can be utilized to predict true tumor progression and PsP. Methods Patients were considered eligible for the study if they had 1) GBM diagnosis, 2) a series of blood cell counts and clinical follow-ups, and 3) tumor progression documented by both MRI and pathology. Data analysis results include descriptive statistics, median (IQR) for continuous variables and count (%) for categorical variables, p values from Wilcoxon rank sum test or Fisher’s exact test for comparison, respectively, and Kaplan-Meier analysis for overall survival (OS). OS was defined as the time from patients’ second surgery to their time of death or last follow up if patients were still alive. Results 78 patients were included in this study. The median age was 56 years. Median ALC dropped 34.5% from baseline 1400 cells/mm3 to 917 cells/mm3 after completion of radiation therapy (RT) and temozolomide (TMZ). All study patients had undergone surgical biopsy upon MRI-documented progression. 37 had true tumor progression (47.44%) and 41 had pseudoprogression (52.56%). ALC before RT/TMZ, post RT/TMZ and at the time of MRI-documented progression did not show significant difference between patients with true progression and PsP. Although not statistically significant, this study found that patients with true progression had worse OS compared to those with PsP (Hazard Ratio [HR] 1.44, 95% CI 0.86–2.43, P = 0.178). This study also found that patients with high ALC (dichotomized by median) post-radiation had longer OS. Conclusion Our results indicate that ALC level in GBM patients before or after treatment does not have predictive value for true disease progression or pseudoprogression. Patients with true progression had worse OS compared to those who had pseudoprogression. A larger sample size that includes CD4 cell counts may be needed to evaluate the PsP predictive value of peripheral blood biomarkers.

Role of Dynamic Susceptibility Contrast Perfusion MRI in Glioma Progression Evaluation

Accurately and quickly differentiating true progression from pseudoprogression in glioma patients is still a challenge. This study aims to explore if dynamic susceptibility contrast- (DSC-) MRI can improve the evaluation of glioma progression. We enrolled 65 glioma patients with suspected gadolinium-enhancing lesion. Longitudinal MRI follow-up (mean 590 days, range: 210–2670 days) or re-operation (n = 3) was used to confirm true progression (n = 51) and pseudoprogression (n = 14). We assessed the diagnostic performance of each MRI variable and the different combinations. Our results showed that the relative cerebral blood volume (rCBV) in the true progression group (1.094, 95%CI: 1.135–1.636) was significantly higher than that of the pseudoprogression group (0.541 ± 0.154) p < 0.001 . Among the 18 patients who had serial DSC-MRI, the rCBV of the progression group (0.480, 95%CI: 0.173–0.810) differed significantly from pseudoprogression (-0.083, 95%CI: −1.138–0.620) group p = 0.015 . With an rCBV threshold of 0.743, the sensitivity and specificity for discriminating true progression from pseudoprogression were 76.5% and 92.9%, respectively. The Cho/Cr and Cho/NAA ratios of the true progression group (2.520, 95%CI: 2.331–2.773; 2.414 ± 0.665, respectively) were higher than those of the pseudoprogression group (1.719 ± 0.664; 1.499 ± 0.500, respectively) ( p = 0.001 , p < 0.001 , respectively). The areas under ROC curve (AUCs) of enhancement pattern, MRS, and DSC-MRI for the differentiation were 0.782, 0.881, and 0.912, respectively. Interestingly, when combined enhancement pattern, MRS, and DSC-MRI variables, the AUC was 0.965 and achieved sensitivity 90.2% and specificity 100.0%. Our results suggest that DSC-MRI can significantly improve the diagnostic performance for identifying glioma progression. DSC-MRI combined with conventional MRI may promptly distinguish true gliomas progression from pseudoprogression when the suspected gadolinium-enhancing lesion was found, without the need for a long-term follow-up.

Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T1-weighted Contrast-enhanced Imaging

Background Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate the diagnostic performance of machine learning using radiomics modelfrom T1-weighted contrast enhanced imaging(T1CE) in differentiating pseudoprogression from true progression after standard treatment for GBM. Methods Seventy-sevenGBM patients, including 51 with true progression and 26 with pseudoprogression,who underwent standard treatment and T1CE, were retrospectively enrolled.Clinical information, including sex, age, KPS score, resection extent, neurological deficit and mean radiation dose, were also recorded collected for each patient. The whole tumor enhancementwas manually drawn on the T1CE image, and a total of texture 9675 features were extracted and fed to a two-step feature selection scheme. A random forest (RF) classifier was trained to separate the patients by their outcomes.The diagnostic efficacies of the radiomics modeland radiologist assessment were further compared by using theaccuracy (ACC), sensitivity and specificity. Results No clinical features showed statistically significant differences between true progression and pseudoprogression.The radiomic classifier demonstrated ACC, sensitivity, and specificity of 72.78%(95% confidence interval [CI]: 0.45,0.91), 78.36%(95%CI: 0.56,1.00) and 61.33%(95%CI: 0.20,0.82).The accuracy, sensitivity and specificity of three radiologists’ assessment were66.23%(95% CI: 0.55,0.76), 61.50%(95% CI: 0.43,0.78) and 68.62%(95% CI: 0.55,0.80); 55.84%(95% CI: 0.45,0.66),69.25%(95% CI: 0.50,0.84) and 49.13%(95% CI: 0.36,0.62); 55.84%(95% CI: 0.45,0.66), 69.23%(95% CI: 0.50,0.84) and 47.06%(95% CI: 0.34,0.61), respectively. Conclusion T1CE–based radiomics showed better classification performance compared with radiologists’ assessment.The radiomics modelwas promising in differentiating pseudoprogression from true progression.

The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients

Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clinical features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre-RT MRIs.