Influence of arterial transit time delays on the differentiation between tumor progression and pseudoprogression in glioblastoma by arterial spin labeling magnetic resonance imaging

Arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) have shown potential for differentiating tumor progression from pseudoprogression. For pseudocontinuous ASL with a single postlabeling delay, the presence of delayed arterial transit times (ATTs) could affect the evaluation of ASL-MRI perfusion data. In this study, the influence of ATT artifacts on the perfusion assessment and differentiation between tumor progression and pseudoprogression were studied. This study comprised 66 adult patients (mean age 60 ± 13 years; 40 males) with a histologically confirmed glioblastoma who received postoperative radio (chemo)therapy. ASL-MRI and DSC-MRI scans were acquired at 3 months postradiotherapy as part of the standard clinical routine. These scans were visually scored regarding (i) the severity of ATT artifacts (%) on the ASL-MRI scans only, scored by two neuroradiologists; (ii) perfusion of the enhancing tumor lesion; and (iii) radiological evaluation of tumor progression versus pseudoprogression by one neuroradiologist. The final outcome was based on combined clinical and radiological follow-up until 9 months postradiotherapy. ATT artifacts were identified in all patients based on the mean scores of two raters. A significant difference between the radiological evaluation of ASL-MRI and DSC-MRI was observed only for ASL images with moderate ATT severity (30%-65%). The perfusion assessment showed ASL-MRI tending more towards hyperperfusion than DSC-MRI in the case of moderate ATT artifacts. In addition, there was a significant difference between the prediction of tumor progression with ASL-MRI and the final outcome in the case of severe ATT artifacts (McNemar test, p = 0.041). Despite using ASL imaging parameters close to the recommended settings, ATT artifacts frequently occur in patients with treated brain tumors. Those artifacts could hinder the radiological evaluation of ASL-MRI data and the detection of true disease progression, potentially affecting treatment decisions for patients with glioblastoma.

B1 inhomogeneity corrected CEST MRI based on direct saturation removed omega plot model at 5T

PURPOSE

CEST can image macromolecules/compounds via detecting chemical exchange between labile protons and bulk water. B1 field inhomogeneity impairs CEST quantification. Conventional B1 inhomogeneity correction methods depend on interpolation algorithms, B1 choices, acquisition number or calibration curves, making reliable correction challenging. This study proposed a novel B1 inhomogeneity correction method based on a direct saturation (DS) removed omega plot model.

METHODS

Four healthy volunteers underwent B1 field mapping and CEST imaging under four nominal B1 levels of 0.75, 1.0, 1.5, and 2.0 μT at 5T. DS was resolved using a multi-pool Lorentzian model and removed from respective Z spectrum. Residual spectral signals were used to construct the omega plot as a linear function of 1/B 1 2 $$ {B}_1^2 $$ , from which corrected signals at nominal B1 levels were calculated. Routine asymmetry analysis was conducted to quantify amide proton transfer (APT) effect. Its distribution across white matter was compared before and after B1 inhomogeneity correction and also with the conventional interpolation approach.

RESULTS

B1 inhomogeneity yielded conspicuous artifact on APT images. Such artifact was mitigated by the proposed method. Homogeneous APT maps were shown with SD consistently smaller than that before B1 inhomogeneity correction and the interpolation method. Moreover, B1 inhomogeneity correction from two and four CEST acquisitions yielded similar results, superior over the interpolation method that derived inconsistent APT contrasts among different B1 choices.

CONCLUSION

The proposed method enables reliable B1 inhomogeneity correction from at least two CEST acquisitions, providing an effective way to improve quantitative CEST MRI.

Advances in diffuse glioma assessment: preoperative and postoperative applications of chemical exchange saturation transfer

Chemical Exchange Saturation Transfer (CEST) is a technique that uses specific off-resonance saturation pulses to pre-saturate targeted substances. This process influences the signal intensity of free water, thereby indirectly providing information about the pre-saturated substance. Among the clinical applications of CEST, Amide Proton Transfer (APT) is currently the most well-established. APT can be utilized for the preoperative grading of gliomas. Tumors with higher APTw signals generally indicate a higher likelihood of malignancy. In predicting preoperative molecular typing, APTw values are typically lower in tumors with favorable molecular phenotypes, such as isocitrate dehydrogenase (IDH) mutations, compared to IDH wild-type tumors. For differential diagnosis, the average APTw values of meningiomas are significantly lower than those of high-grade gliomas. Various APTw measurement indices assist in distinguishing central nervous system lesions with similar imaging features, such as progressive multifocal leukoencephalopathy, central nervous system lymphoma, solitary brain metastases, and glioblastoma. Regarding prognosis, APT effectively differentiates between tumor recurrence and treatment effects, and also possesses predictive capabilities for overall survival (OS) and progression-free survival (PFS).

Amide proton transfer-weighted and arterial spin labeling imaging may improve differentiation between high-grade glioma recurrence and radiation-induced brain injury

Rationale and objectives

The management of tumor recurrence (TR) and radiation-induced brain injury (RIBI) poses significant challenges, necessitating the development of effective differentiation strategies. In this study, we investigated the potential of amide proton transfer-weighted (APTw) and arterial spin labeling (ASL) imaging for discriminating between TR and RIBI in patients with high-grade glioma (HGG).

Methods

A total of 64 HGG patients receiving standard treatment were enrolled in this study. The patients were categorized based on secondary pathology or MRI follow-up results, and the demographic characteristics of each group were presented. The APTw, rAPTw, cerebral blood flow (CBF) and rCBF values were quantified. The differences in various parameters between TR and RIBI were assessed using the independent-samples t-test. The discriminative performance of these MRI parameters in distinguishing between the two conditions was assessed using receiver operating characteristic (ROC) curve analysis. Additionally, the Delong test was employed to further evaluate their discriminatory ability.

Results

The APTw and CBF values of TR were significantly higher compared to RIBI (P < 0.05). APTw MRI demonstrated superior diagnostic efficiency in distinguishing TR from RIBI (area under the curve [AUC]: 0.864; sensitivity: 75.0 %; specificity: 81.8 %) when compared to ASL imaging. The combined utilization of APTw and CBF value further enhanced the AUC to 0.922. The Delong test demonstrated that the combination of APTw and ASL exhibited superior performance in the identification of TR and RIBI, compared to ASL alone (P = 0.048).

Conclusion

APTw exhibited superior diagnostic efficacy compared to ASL in the evaluation of TR and RIBI. Furthermore, the combination of APTw and ASL exhibits greater discriminatory capability and diagnostic performance.

Radiomic feature reliability of amide proton transfer-weighted MR images acquired with compressed sensing at 3T

Compressed sensing (CS) is a novel technique for MRI acceleration. The purpose of this paper was to assess the effects of CS on the radiomic features extracted from amide proton transfer-weighted (APTw) images. Brain tumor MRI data of 40 scans were studied. Standard images using sensitivity encoding (SENSE) with an acceleration factor (AF) of 2 were used as the gold standard, and APTw images using SENSE with CS (CS-SENSE) with an AF of 4 were assessed. Regions of interest (ROIs), including normal tissue, edema, liquefactive necrosis, and tumor, were manually drawn, and the effects of CS-SENSE on radiomics were assessed for each ROI category. An intraclass correlation coefficient (ICC) was first calculated for each feature extracted from APTw images with SENSE and CS-SENSE for all ROIs. Different filters were applied to the original images, and the effects of these filters on the ICCs were further compared between APTw images with SENSE and CS-SENSE. Feature deviations were also provided for a more comprehensive evaluation of the effects of CS-SENSE on radiomic features. The ROI-based comparison showed that most radiomic features extracted from CS-SENSE-APTw images and SENSE-APTw images had moderate or greater reliabilities (ICC ≥ 0.5) for all four ROIs and all eight image sets with different filters. Tumor showed significantly higher ICCs than normal tissue, edema, and liquefactive necrosis. Compared to the original images, filters (such as Exponential or Square) may improve the reliability of radiomic features extracted from CS-SENSE-APTw and SENSE-APTw images.

MR Perfusion Imaging for Gliomas

Accurate diagnosis and treatment evaluation of patients with gliomas is imperative to make clinical decisions. Multiparametric MR perfusion imaging reveals physiologic features of gliomas that can help classify them according to their histologic and molecular features as well as distinguish them from other neoplastic and nonneoplastic entities. It is also helpful in distinguishing tumor recurrence or progression from radiation necrosis, pseudoprogression, and pseudoresponse, which is difficult with conventional MR imaging. This review provides an update on MR perfusion imaging for the diagnosis and treatment monitoring of patients with gliomas following standard-of-care chemoradiation therapy and other treatment regimens such as immunotherapy.

Velocity-Selective Arterial Spin Labeling Perfusion in Monitoring High Grade Gliomas Following Therapy: Clinical Feasibility at 1.5T and Comparison with Dynamic Susceptibility Contrast Perfusion

MR perfusion imaging is important in the clinical evaluation of primary brain tumors, particularly in differentiating between true progression and treatment-induced change. The utility of velocity-selective ASL (VSASL) compared to the more commonly utilized DSC perfusion technique was assessed in routine clinical surveillance MR exams of 28 patients with high-grade gliomas at 1.5T. Using RANO criteria, patients were assigned to two groups, one with detectable residual/recurrent tumor ("RT", n = 9), and the other with no detectable residual/recurrent tumor ("NRT", n = 19). An ROI was drawn to encompass the largest dimension of the lesion with measures normalized against normal gray matter to yield rCBF and tSNR from VSASL, as well as rCBF and leakage-corrected relative CBV (lc-rCBV) from DSC. VSASL (rCBF and tSNR) and DSC (rCBF and lc-rCBV) metrics were significantly higher in the RT group than the NRT group allowing adequate discrimination (p < 0.05, Mann-Whitney test). Lin's concordance analyses showed moderate to excellent concordance between the two methods, with a stronger, moderate correlation between VSASL rCBF and DSC lc-rCBV (r = 0.57, p = 0.002; Pearson's correlation). These results suggest that VSASL is clinically feasible at 1.5T and has the potential to offer a noninvasive alternative to DSC perfusion in monitoring high-grade gliomas following therapy.

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.

Reproducibility of APT-weighted CEST-MRI at 3T in healthy brain and tumor across sessions and scanners

Amide proton transfer (APT)-weighted chemical exchange saturation transfer (CEST) imaging is a recent MRI technique making its way into clinical application. In this work, we investigated whether APT-weighted CEST imaging can provide reproducible measurements across scan sessions and scanners. Within-session, between-session and between scanner reproducibility was calculated for 19 healthy volunteers and 7 patients with a brain tumor on two 3T MRI scanners. The APT-weighted CEST effect was evaluated by calculating the Lorentzian Difference (LD), magnetization transfer ratio asymmetry (MTRasym), and relaxation-compensated inverse magnetization transfer ratio (MTRREX) averaged in whole brain white matter (WM), enhancing tumor and necrosis. Within subject coefficient of variation (COV) calculations, Bland-Altman plots and mixed effect modeling were performed to assess the repeatability and reproducibility of averaged values. The group median COVs of LD APT were 0.56% (N = 19), 0.84% (N = 6), 0.80% (N = 9) in WM within-session, between-session and between-scanner respectively. The between-session COV of LD APT in enhancing tumor (N = 6) and necrotic core (N = 3) were 4.57% and 5.67%, respectively. There were no significant differences in within session, between session and between scanner comparisons of the APT effect. The COVs of LD and MTRREX were consistently lower than MTRasym in all experiments, both in healthy tissues and tumor. The repeatability and reproducibility of APT-weighted CEST was clinically acceptable across scan sessions and scanners. Although MTRasym is simple to acquire and compute and sufficient to provide robust measurement, it is beneficial to include LD and MTRREX to obtain higher reproducibility for detecting minor signal difference in different tissue types.

APT weighted imaging in diffuse gliomas

Amide proton transfer-weighted (APTw) imaging is a non-invasive molecular MRI technique with a wide range of applications in neuroradiology and particularly neuro-oncology imaging. More than 15 years of pre-clinical experiments and clinical studies have demonstrated that APTw metrics are reproducible and reliable, leading to large-scale clinical acceptance. At present, major vendors of MRI scanners provide APTw sequences upon request. However, most neuroradiologists are unfamiliar with this advanced MRI contrast, its related metrics, and its established added value to patient care. In this manuscript, we present the APTw contrast and illustrate its clinical potential for glioma patients, before and after tumor therapy. We also show common artifacts of APTw imaging and discuss potential limitations and future refinements. Our goal is to suggest how this emerging technique can aid in diffuse gliomas work-up.

Multimodality MRI Radiomics Based on Machine Learning for Identifying True Tumor Recurrence and Treatment-Related Effects in Patients with Postoperative Glioma

INTRODUCTION

Conventional magnetic resonance imaging (MRI) features have difficulty distinguishing glioma true tumor recurrence (TuR) from treatment-related effects (TrE). We aimed to develop a machine-learning model based on multimodality MRI radiomics to help improve the efficiency of identifying glioma TuR.

METHODS

A total of 131 patients were enrolled and randomly divided into the training set (n = 91) and the test set (n = 40). Radiomic features were extracted from the postoperative enhancement (PoE) region and edema (ED) region from four routine MRI sequences. After analyses of Spearman's rank correlation coefficient, and least absolute shrinkage and selection operator, the key radiomic features were selected to construct support vector machine (SVM) and k-nearest neighbor (KNN) models. Decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were used to analyze the performance.

RESULTS

The PoE model had a significantly higher area under curve (AUC) than the ED model (p < 0.05). Among the models constructed with a single sequence, the model using PoE regional features from CE-T1WI was superior to other models, with an AUC of 0.905 for SVM and 0.899 for KNN. In multimodality models, the PoE model outperformed the ED model with an AUC of 0.931 for SVM and 0.896 for KNN. The multimodality model, which combined routine sequences and the whole regional features, showed a slightly better performance with an AUC of 0.965 for SVM and 0.955 for KNN. Decision curve analysis showed the good clinical utility of multimodal radiomics models.

CONCLUSIONS

Multimodality radiomics can identify glioma TuR and TrE, potentially aiding clinical decision-making for individualized treatment. And edematous regions may provide useful information for recognizing recurrence.

RETROSPECTIVELY REGISTERED

2021.04.15, No:2020039.

CEST2022: Amide proton transfer-weighted MRI improves the diagnostic performance of multiparametric non-contrast-enhanced MRI techniques in patients with post-treatment high-grade gliomas

New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional-and even some advanced MRI techniques-are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting. In this study, we evaluated and compared the diagnostic performances of APTw MRI with several non-contrast-enhanced MRI sequences, such as diffusion-weighted imaging, susceptibility-weighted imaging, and pseudo-continuous arterial spin labeling. Thirty-nine scans from 28 glioma patients were obtained on a 3 T MRI scanner. A histogram analysis approach was employed to extract parameters from each tumor area. Statistically significant parameters (P < 0.05) were selected to train multivariate logistic regression models to evaluate the performance of MRI sequences. Multiple histogram parameters, particularly from APTw and pseudo-continuous arterial spin labeling images, demonstrated significant differences between treatment effect and recurrent tumor. The regression model trained on the combination of all significant histogram parameters achieved the best result (area under the curve = 0.89). We found that APTw images added value to other advanced MR images for the differentiation of treatment effect and tumor recurrence.

An overview of Twist1 in glioma progression and recurrence

Glioma cells are characterized by high migration ability, resulting in the aggressive growth of the tumors and poor prognosis of patients. Epithelial-to-mesenchymal transition (EMT) is one of the most important steps for tumor migration and metastasis and be elevated during glioma progression and recurrence. Twist1 is a basic helix-loop-helix transcription factor and a key transcription factor involved in the process of EMT. Twist1 is related to glioma mesenchymal change, invasion, heterogeneity, self-renewal of tumor stem cells, angiogenesis, etc., and may be used as a prognostic indicator and therapeutic target for glioma patients. This paper mainly reviews the structural characteristics, regulatory mechanisms, and apparent regulation of Twist1, as well as the roles of Twist1 during glioma progression and recurrence, providing new revelations for its use as a potential drug target and glioma treatment research.

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.

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.

Three-Dimensional Amide Proton Transfer-Weighted Imaging for Differentiating between Glioblastoma, IDH-Wildtype and Primary Central Nervous System Lymphoma

Distinguishing primary central nervous system lymphoma (PCNSL) from glioblastoma, isocitrate dehydrogenase (IDH)-wildtype is sometimes hard. Because the role of operation on them varies, accurate preoperative diagnosis is crucial. In this study, we evaluated whether a specific kind of chemical exchange saturation transfer imaging, i.e., amide proton transfer-weighted (APTw) imaging, was useful to distinguish PCNSL from glioblastoma, IDH-wildtype. A total of 14 PCNSL and 27 glioblastoma, IDH-wildtype cases were evaluated. There was no significant difference in the mean APTw signal values between the two groups. However, the percentile values from the 1st percentile to the 20th percentile APTw signals and the width1-100 APTw signals significantly differed. The highest area under the curve was 0.796, which was obtained from the width1-100 APTw signal values. The sensitivity and specificity values were 64.3% and 88.9%, respectively. APTw imaging was useful to distinguish PCNSL from glioblastoma, IDH-wildtype. To avoid unnecessary aggressive surgical resection, APTw imaging is recommended for cases in which PCNSL is one of the differential diagnoses.

Differentiation of true progression from treatment response in high-grade glioma treated with chemoradiation: a comparison study of 3D-APTW and 3D-PcASL imaging and DWI

PURPOSE

To assess and compare the diagnostic performance of 3D amide proton-transfer-weighted (3D-APTW) imaging, 3D pseudocontinuous arterial spin-labeling (3D-PcASL) imaging, and diffusion-weighted imaging in distinguishing true progression (TP) from treatment response (TR) in posttreatment malignant glioma patients.

MATERIALS AND METHODS

Forty-eight patients with suspected tumor recurrence were prospectively enrolled. Histological or longitudinal routine MRI follow-up over six months was assessed to confirm lesion type. The apparent diffusion coefficient (ADC), relative APTW_max (rAPTW), and relative CBF_max values (rCBF) were measured in lesions with enhancing regions on post-gadolinium T₁ -weighted MRI. MRI parameters between the TP and TR groups were compared using Student's t tests. In addition, a receiver operating characteristic (ROC) curve was constructed, and the area under the ROC curve (AUC) was calculated to assess the differentiation diagnostic performance of each parameter.

RESULTS

The TP group showed a significantly higher rAPTW and rCBF than the TR group; the AUCs of rAPTW and rCBF to distinguish between TP and TR were 0.911 (with sensitivity of 90.3% and specificity of 82.4%) and 0.852 (with sensitivity of 80.6% and specificity of 82.4%), respectively. By adding the rAPTW values to rCBF values, the diagnostic ability was improved from 0.852 to 0.951. ADC showed no significant differences between the TP and TR groups, with an AUC lower than 0.70.

CONCLUSION

Both 3D-PcASL and 3D-APTW imaging could distinguish TP from TR, and 3D-APTW had a better diagnostic performance. Combining the rAPTW values and rCBF values achieved a better diagnostic performance.

The value of arterial spin labelling (ASL) perfusion MRI in the assessment of post-treatment progression in adult glioma: A systematic review and meta-analysis

Background

The distinction between viable tumor and therapy-induced changes is crucial for the clinical management of patients with gliomas. This study aims to quantitatively assess the efficacy of arterial spin labeling (ASL) biomarkers, including relative cerebral blood flow (rCBF) and absolute cerebral blood flow (CBF), for the discrimination of progressive disease (PD) and treatment-related effects.

Methods

Eight articles were included in the synthesis after searching the literature systematically. Data have been extracted and a meta-analysis using the random-effect model was subsequently carried out. Diagnostic accuracy assessment was also performed.

Results

This study revealed that there is a significant difference in perfusion measurements between groups with PD and therapy-induced changes. The rCBF yielded a standardized mean difference (SMD) of 1.25 [95% CI 0.75, 1.75] (p < .00001). The maximum perfusion indices (rCBFmax and CBFmax) both showed equivalent discriminatory ability, with SMD of 1.35 [95% CI 0.78, 1.91] (p < .00001) and 1.56 [95% CI 0.79, 2.33] (p < .0001), respectively. Similarly, accuracy estimates were comparable among ASL-derived metrices. Pooled sensitivities [95% CI] were 0.85 [0.67, 0.94], 0.88 [0.71, 0.96], and 0.93 [0.73, 0.98], and pooled specificities [95% CI] were 0.83 [0.71, 0.91], 0.83 [0.67, 0.92], 0.84 [0.67, 0.93], for rCBF, rCBFmax and CBFmax, respectively. Corresponding HSROC area under curve (AUC) [95% CI] were 0.90 [0.87, 0.92], 0.92 [0.89, 0.94], and 0.93 [0.90, 0.95].

Conclusion

These results suggest that ASL quantitative biomarkers, particularly rCBFmax and CBFmax, have the potential to discriminate between glioma progression and therapy-induced changes.

Radiomics analysis of amide proton transfer-weighted and structural MR images for treatment response assessment in malignant gliomas

The purpose of this study was to evaluate the value of amide proton transfer-weighted (APTw) MRI radiomic features for the differentiation of tumor recurrence from treatment effect in malignant gliomas. Eighty-six patients who had suspected tumor recurrence after completion of chemoradiation or radiotherapy, and who had APTw-MRI data acquired at 3 T, were retrospectively analyzed. Using a fluid-attenuated inversion recovery (FLAIR) image-based mask, radiomics analysis was applied to the processed APTw and structural MR images. A chi-square automatic interaction detector decision tree was used for classification analysis. Models with and without APTw features were built using the same strategy. Tenfold cross-validation was applied to obtain the overall classification performance of each model. Sixty patients were confirmed as having tumor recurrence, and the remainder were confirmed as having treatment effect, at median time points of 190 and 171 days after therapy, respectively. There were 525 radiomic features extracted from each of the processed APTw and structural MR images. Based on these, the APTw-based model yielded the highest accuracy (86.0%) for the differentiation of tumor recurrence from treatment effect, compared with 74.4%, 76.7%, 83.7%, and 76.7% for T1 w, T2 w, FLAIR, and Gd-T1 w, respectively. Model classification accuracy was 82.6% when using the combined structural MR images (T1 w, T2 w, FLAIR, Gd-T1 w), and increased to 89.5% when using these structural plus APTw images. The corresponding sensitivity and specificity were 85.0% and 76.9% for the combination of structural MR images, and 85.0% and 100% after adding APTw image features. Adding APTw-based radiomic features increased MRI accuracy in the assessment of the treatment response in post-treatment malignant gliomas.

[Influence of the Imaging Method on Regional Cerebral Blood Flow Value in Arterial Spin Labeling (ASL): Comparison of Pulsed-ASL with Two-dimensional Acquisition and Pseudo-continuous-ASL with 3D Spiral Acquisition]

PURPOSE

The purposes of this study were to compare regional cerebral blood flow (rCBF) images acquired by the pulsed arterial spin labeling with two-dimensional acquisition (PASL-2D) and the pseudo-continuous-ASL with three-dimensional spiral acquisition (pCASL-3D spiral), and to clarify the characteristics of rCBF values in both ASL methods.

METHODS

PASL-2D and pCASL-3D spiral were performed in five healthy volunteers with no history of brain disease using 3T scanners from two venders in the same center. 3D T₁-weighted images and rCBF images were acquired by both ASL methods for a total of 3 sessions: twice at the initial visit (1st and 2nd), and 1 hour and 1 week later. The rCBF images calculated by each MR machine were anatomically standardized using SPM12. The regions of interest (ROIs) were set on the territory of the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). Mean and relative rCBF values were calculated at each arterial territory in each session. Reproducibility for rCBF value in each method was analyzed using Bland-Altman plots, the coefficient of repeatability (CR), and the repeatability index (RI).

RESULTS

In all sessions, mean values of rCBF were the highest at PCA for PASL-2D and at MCA for pCASL-3D spiral. RIs of pCASL-3D spiral were lower than those of PASL-2D in all arterial territories.

CONCLUSION

In the PASL-2D and the pCASL-3D spiral, we confirmed the characteristics of the mean and reproducibility of rCBF values in each arterial territory.

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.

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors

Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics

Objective

To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments.

Methods

The current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perfusion, permeability, and microstructure imaging were similarly analyzed in Part 1 of this two-part review article and are valuable reading as background to this article. We appraise the clinic readiness of all the individual modalities and consider methodologies involving machine learning (radiomics) and the combination of MRI approaches (multiparametric imaging).

Results

The biochemical composition of high-grade gliomas is markedly different from healthy brain tissue. Magnetic resonance spectroscopy allows the simultaneous acquisition of an array of metabolic alterations, with choline-based ratios appearing to be consistently discriminatory in treatment response assessment, although challenges remain despite this being a mature technique. Promising directions relate to ultra-high field strengths, 2-hydroxyglutarate analysis, and the use of non-proton nuclei. Labile protons on endogenous proteins can be selectively targeted with chemical exchange saturation transfer to give high resolution images. The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring biomarker. Multiparametric methodologies, including the incorporation of nuclear medicine techniques, combine probes measuring different tumor properties. Although potentially synergistic, the limitations of each individual modality also can be compounded, particularly in the absence of standardization. Machine learning requires large datasets with high-quality annotation; there is currently low-level evidence for monitoring biomarker clinical application.

Conclusion

Advanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic.

Molecular Imaging of Brain Tumors and Drug Delivery Using CEST MRI: Promises and Challenges

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) detects molecules in their natural forms in a sensitive and non-invasive manner. This makes it a robust approach to assess brain tumors and related molecular alterations using endogenous molecules, such as proteins/peptides, and drugs approved for clinical use. In this review, we will discuss the promises of CEST MRI in the identification of tumors, tumor grading, detecting molecular alterations related to isocitrate dehydrogenase (IDH) and O-6-methylguanine-DNA methyltransferase (MGMT), assessment of treatment effects, and using multiple contrasts of CEST to develop theranostic approaches for cancer treatments. Promising applications include (i) using the CEST contrast of amide protons of proteins/peptides to detect brain tumors, such as glioblastoma multiforme (GBM) and low-grade gliomas; (ii) using multiple CEST contrasts for tumor stratification, and (iii) evaluation of the efficacy of drug delivery without the need of metallic or radioactive labels. These promising applications have raised enthusiasm, however, the use of CEST MRI is not trivial. CEST contrast depends on the pulse sequences, saturation parameters, methods used to analyze the CEST spectrum (i.e., Z-spectrum), and, importantly, how to interpret changes in CEST contrast and related molecular alterations in the brain. Emerging pulse sequence designs and data analysis approaches, including those assisted with deep learning, have enhanced the capability of CEST MRI in detecting molecules in brain tumors. CEST has become a specific marker for tumor grading and has the potential for prognosis and theranostics in brain tumors. With increasing understanding of the technical aspects and associated molecular alterations detected by CEST MRI, this young field is expected to have wide clinical applications in the near future.

Multimodal imaging in the differential diagnosis of glioma recurrence from treatment-related effects: A protocol for systematic review and network meta-analysis

BACKGROUND

Glioma is the most common malignant primary brain tumor and it will always recur. To date, various multimodal imaging including magnetic resonance imaging (MRI) and positron emission tomography computed tomography (PET/CT) was used to differentiate the diagnosis of true tumor recurrent (TuR) and treatment-related effects (TrE) in glioma patient but with no overall conclusion. In this study, SROC curve and Bayesian network meta-analysis will be used to conduct a comprehensive analysis of the results of different clinical reports, and assess the efficacy of multimodal imaging in difference TuR and TrE.

METHODS

To find more comprehensive information about the application of multimodal imaging in glioma patients, we searched the EMBASE, Pubmed, and Cochrane Central Register of Controlled Trials for relevant clinical trials. We also reviewed their reference lists to avoid omissions. QUADAS-2, RevMan software, Stata, and R software will be used.

RESULTS

This study will provide reliable evidence for the efficacy of multimodal imaging in the differential diagnosis of TuR and TrE in glioma patients.

CONCLUSION

We will evaluate the effectiveness of different and rank each imaging method in glioma patients to provide a decision-making reference on which method to choose for clinicians. Protocol registration number: CRD42020217861.

Identify glioma recurrence and treatment effects with triple-tracer PET/CT

Background

Differential diagnosis of tumour recurrence (TuR) from treatment effects (TrE), mostly induced by radiotherapy and chemotherapy, is still difficult by using conventional computed tomography (CT) or magnetic resonance (MR) imaging. We have investigated the diagnostic performance of PET/CT with 3 tracers, ¹³N-NH₃, ¹⁸F-FDOPA, and ¹⁸F-FDG, to identify TuR and TrE in glioma patients following treatment.

Methods

Forty-three patients with MR-suspected recurrent glioma were included. The maximum and mean standardized uptake values (SUVmax and SUVmean) of the lesion and the lesion-to-normal grey-matter cortex uptake (L/G) ratio were obtained from each tracer PET/CT. TuR or TrE was determined by histopathology or clinical MR follow-up for at least 6 months.

Results

In this cohort, 34 patients were confirmed to have TuR, and 9 patients met the diagnostic standard of TrE. The SUVmax and SUVmean of ¹³N-NH₃ and ¹⁸F-FDOPA PET/CT at TuR lesions were significantly higher compared with normal brain tissue (¹³N-NH₃ 0.696 ± 0.558, 0.625 ± 0.507 vs 0.486 ± 0.413; ¹⁸F-FDOPA 0.455 ± 0.518, 0.415 ± 0.477 vs 0.194 ± 0.203; both P < 0.01), but there was no significant difference in ¹⁸F-FDG (6.918 ± 3.190, 6.016 ± 2.807 vs 6.356 ± 3.104, P = 0.290 and 0.493). L/G ratios of ¹³N-NH₃ and ¹⁸F-FDOPA were significantly higher in TuR than in TrE group (¹³N-NH_3, 1.573 ± 0.099 vs 1.025 ± 0.128, P = 0.008; ¹⁸F-FDOPA, 2.729 ± 0.131 vs 1.514 ± 0.141, P < 0.001). The sensitivity, specificity and AUC (area under the curve) by ROC (receiver operating characteristic) analysis were 57.7%, 100% and 0.803, for ¹³N-NH₃; 84.6%, 100% and 0.938, for ¹⁸F-FDOPA; and 80.8%, 100%, and 0.952, for the combination, respectively.

Conclusion

Our results suggest that although multiple tracer PET/CT may improve differential diagnosis efficacy, for glioma TuR from TrE, ¹⁸F-FDOPA PET-CT is the most reliable. The combination of ¹⁸F-FDOPA and ¹³N-NH₃ does not increase the diagnostic efficiency, while ¹⁸F-FDG is not worthy for differential diagnosis of glioma TuR and TrE.

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.

Diagnostic performance between MR amide proton transfer (APT) and diffusion kurtosis imaging (DKI) in glioma grading and IDH mutation status prediction at 3 T

PURPOSE

Accurate glioma grading and IDH mutation status prediction are critically essential for individualized preoperative treatment decisions. This study aims to compare the diagnostic performance of magnetic resonance (MR) amide proton transfer (APT) and diffusion kurtosis imaging (DKI) in glioma grading and IDH mutation status prediction.

METHOD

Fifty-one glioma patients without treatment were retrospectively included. APT-weighted (APTw) effect and DKI indices, including mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK), and kurtosis FA (KFA) were obtained from APT and diffusion-weighted images, respectively. DKI indices in tumors were normalized to that in contralateral normal appearing white matter (CNAWM) and the APTw difference (ΔAPTw) between the two regions was calculated. Student's t-test, one-way ANOVA and ROC analyses were conducted.

RESULTS

Among the enrolled 51 patients, 13 had glioma-II, 17 had glioma-III and 21 had glioma-IV. 25 patients were diagnosed as IDH-mutant, and 26 as IDH-wild type. MD and MK differed significantly between glioma-IV and glioma II/III (P < 0.05), but not between glioma-II and glioma-III. FA and KFA showed no significant difference among the three groups (P > 0.05). IDH-mutant group exhibited significantly higher MD and lower FA, MK and ΔAPTw than IDH-wild type (P < 0.05), whereas the two groups showed comparable KFA values. In contrast, ΔAPTw differed significantly across tumor grades and IDH mutation status (P < 0.05), with consistently better discriminatory performance than DKI indices in glioma grading and IDH mutation status prediction.

CONCLUSIONS

APT imaging was superior to DKI in glioma grading and IDH mutation status prediction, benefiting accurate diagnoses and treatment decisions.

Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study

Purpose

We propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence.

Materials and Methods

Fifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected. Each pre-contrast image was voxel-wise subtracted from the co-registered post-contrast image. Dataset was randomly split into training, and testing on a 7:3 ratio, accordingly subjected to a five fold cross validation. Best feature subsets were selected by Pearson correlation coefficient and recursive feature elimination, whereupon a radiomics classifier was built with SVM. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Results

In all, 186 features were extracted on each subtraction map. Top nine T1WI subtraction features, top thirteen T2 FLAIR subtraction features and top thirteen combination features were selected to build optimal SVM classifiers accordingly. The accuracies/AUCs/sensitivity/specificity/PPV/NPV of SVM based on sole T1WI subtraction were 80.00%/80.00% (CI: 0.5370–1.0000)/100%/70.00%/62.50%/100%. Those results of SVM based on sole T2 FLAIR subtraction were 86.67%/84.00% (CI: 0.5962–1.0000)/100%/80%/71.43%/100%. Those results of SVM based on both T1WI subtraction and T2 FLAIR subtraction were 93.33%/94.00% (CI: 0.7778–1.0000)/100%/90%/83.33%/100%, respectively.

Conclusion

Pre- and post-contrast T2 FLAIR subtraction provided added value for diagnosis between recurrence and TRE. SVM based on a combination of T1WI and T2 FLAIR subtraction maps was superior to the sole use of T1WI or T2 FLAIR for differentiating TRE from recurrence. The SVM classifier based on combination of pre-and post-contrast subtraction T2 FLAIR and T1WI imaging allowed for the accurate differential diagnosis of TRE from recurrence, which is of paramount importance for treatment management of postoperative glioma patients after radiation therapy.