Tumour progression or pseudoprogression? A review of post-treatment radiological appearances of glioblastoma

Determinants of cerebral radionecrosis in animal models: A systematic review

BACKGROUND

Radionecrosis is a common complication in radiation oncology, while mechanisms and risk factors have yet to be fully explored. We therefore conducted a systematic review to understand the pathogenesis and identify factors that significantly affect the development.

METHODS

We performed a systematic literature search based on the PRISMA guidelines using PubMed, Ovid, and Web of Science databases. The complete search strategy can be found as a preregistered protocol on PROSPERO (CRD42023361662).

RESULTS

We included 83 studies, most involving healthy animals (n = 72, 86.75 %). High doses of hemispherical irradiation of 30 Gy in rats and 50 Gy in mice led repeatedly to radionecrosis among different studies and set-ups. Higher dose and larger irradiated volume were associated with earlier onset. Fractionated schedules proved limited effectiveness in the prevention of radionecrosis. Distinct anatomical brain structures respond to irradiation in various ways. White matter appears to be more vulnerable than gray matter. Younger age, more evolved animal species, and genetic background were also significant factors, whereas sex was irrelevant. Only 13.25 % of the studies were performed on primary brain tumor bearing animals, no studies on brain metastases are currently available.

CONCLUSION

This systematic review identified various factors that significantly affect the induction of radionecrosis. The current state of research neglects the utilization of animal models of brain tumors, even though patients with brain malignancies constitute the largest group receiving brain irradiation. This latter aspect should be primarily addressed when developing an experimental radionecrosis model for translational implementation.

Low rCBV values in glioblastoma tumor progression under chemoradiotherapy

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Radiation necrosis after radiation therapy treatment of brain metastases: A computational approach

Metastasis is the process through which cancer cells break away from a primary tumor, travel through the blood or lymph system, and form new tumors in distant tissues. One of the preferred sites for metastatic dissemination is the brain, affecting more than 20% of all cancer patients. This figure is increasing steadily due to improvements in treatments of primary tumors. Stereotactic radiosurgery (SRS) is one of the main treatment options for patients with a small or moderate number of brain metastases (BMs). A frequent adverse event of SRS is radiation necrosis (RN), an inflammatory condition caused by late normal tissue cell death. A major diagnostic problem is that RNs are difficult to distinguish from BM recurrences, due to their similarities on standard magnetic resonance images (MRIs). However, this distinction is key to choosing the best therapeutic approach since RNs resolve often without further interventions, while relapsing BMs may require open brain surgery. Recent research has shown that RNs have a faster growth dynamics than recurrent BMs, providing a way to differentiate the two entities, but no mechanistic explanation has been provided for those observations. In this study, computational frameworks were developed based on mathematical models of increasing complexity, providing mechanistic explanations for the differential growth dynamics of BMs relapse versus RN events and explaining the observed clinical phenomenology. Simulated tumor relapses were found to have growth exponents substantially smaller than the group in which there was inflammation due to damage induced by SRS to normal brain tissue adjacent to the BMs, thus leading to RN. ROC curves with the synthetic data had an optimal threshold that maximized the sensitivity and specificity values for a growth exponent β* = 1.05, very close to that observed in patient datasets.

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

Background

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

Methods

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

Results

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

Conclusions

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

Sequential and Hybrid PET/MRI Acquisition in Follow-Up Examination of Glioblastoma Show Similar Diagnostic Performance

Both positron emission tomography (PET) and magnetic resonance imaging (MRI), including dynamic susceptibility contrast perfusion (DSC-PWI), are crucial for treatment monitoring of patients with high-grade gliomas. In clinical practice, they are usually conducted at separate time points. Whether this affects their diagnostic performance is presently unclear. To this end, we retrospectively reviewed 38 patients with pathologically confirmed glioblastoma (IDH wild-type) and suspected tumor recurrence after radiotherapy. Only patients who received both a PET−MRI (where DSC perfusion was acquired simultaneously with a FET-PET) and a separate MRI exam (including DSC perfusion) were included. Tumors were automatically segmented into contrast-enhancing tumor (CET), necrosis, and edema. To compare the simultaneous as well as the sequential DSC perfusion to the FET-PET, we calculated Dice overlap, global mutual information as well as voxel-wise Spearman correlation of hotspot areas. For the joint assessment of PET and MRI, we computed logistic regression models for the differentiation between true progression (PD) and treatment-related changes (TRC) using simultaneously or sequentially acquired images as input data. We observed no significant differences between Dice overlap (p = 0.17; paired t-test), mutual information (p = 0.18; paired t-test) and Spearman correlation (p = 0.90; paired t-test) when comparing simultaneous PET−MRI and sequential PET/MRI acquisition. This also held true for the subgroup of patients with >14 days between exams. Importantly, for the diagnostic performance, ROC analysis showed similar AUCs for differentiation of PD and TRC (AUC simultaneous PET: 0.77; AUC sequential PET: 0.78; p = 0.83, DeLong’s test). We found no relevant differences between simultaneous and sequential acquisition of FET-PET and DSC perfusion, also regarding their diagnostic performance. Given the increasing attention to multi-parametric assessment of glioma treatment response, our results reassuringly suggest that sequential acquisition is clinically and scientifically acceptable.

Treatment of Radiation-Induced Brain Necrosis

Radiation-induced brain necrosis (RBN) is a serious complication of intracranial as well as skull base tumors after radiotherapy. In the past, due to the lack of effective treatment, radiation brain necrosis was considered to be progressive and irreversible. With better understanding in histopathology and neuroimaging, the occurrence and development of RBN have been gradually clarified, and new treatment methods are constantly emerging. In recent years, some scholars have tried to treat RBN with bevacizumab, nerve growth factor, and gangliosides and have achieved similar results. Some cases of brain necrosis can be repairable and reversible. We aimed to summarize the incidence, pathogenesis, and treatment of RBN.

In Vivo Evaluation of Indium-111-Labeled 800CW as a Necrosis-Avid Contrast Agent

Purpose

Current clinical measurements for tumor treatment efficiency rely often on changes in tumor volume measured as shrinkage by CT or MRI, which become apparent after multiple lines of treatment and pose a physical and psychological burden on the patient. Detection of therapy-induced cell death in the tumor can be a fast measure for treatment efficiency. However, there are no reliable clinical tools for detection of tumor necrosis. Previously, we studied the necrosis avidity of cyanine-based fluorescent dyes, which suffered long circulation times before tumor necrosis could be imaged due to low hydrophilicity. We now present the application of radiolabeled 800CW, a commercially available cyanine with high hydrophilicity, to image tumor necrosis in a mouse model.

Procedures

We conjugated 800CW to DOTA via a PEG linker, for labeling with single-photon emission-computed tomography isotope indium-111, yielding [¹¹¹In]In-DOTA-PEG₄-800CW. We then investigated specific [¹¹¹In]In-DOTA-PEG₄-800CW uptake by dead cells in vitro, using both fluorescence and radioactivity as detection modalities. Finally, we investigated [¹¹¹In]In-DOTA-PEG₄-800CW uptake into necrotic tumor regions of a 4T1 breast tumor model in mice.

Results

We successfully prepared a precursor and developed a reliable procedure for labeling 800CW with indium-111. We detected specific [¹¹¹In]In-DOTA-PEG₄-800CW uptake by dead cells, using both fluorescence and radioactivity. Albeit with a tumor uptake of only 0.37%ID/g at 6 h post injection, we were able to image tumor necrosis with a tumor to background ratio of 7:4. Fluorescence and radioactivity in cryosections from the dissected tumors were colocalized with tumor necrosis, confirmed by TUNEL staining.

Conclusions

[¹¹¹In]In-DOTA-PEG₄-800CW can be used to image tumor necrosis in vitro and in vivo. Further research will elucidate the application of [¹¹¹In]In-DOTA-PEG₄-800CW or other radiolabeled hydrophilic cyanines for the detection of necrosis caused by chemotherapy or other anti-cancer therapies. This can provide valuable prognostic information in treatment of solid tumors.

Electronic supplementary material

The online version of this article (10.1007/s11307-020-01511-x) contains supplementary material, which is available to authorized users.

Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression

Purpose

To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [¹⁸F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma.

Material and methods

At suspected tumor progression, MRI and [¹⁸F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [¹⁸F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier’s performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments.

Results

In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77–0.93 (sensitivity 0.91, 95% CI 0.81–0.97; specificity 0.71, 95% CI 0.44–0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72–0.9), 0.81 for [¹⁸F]-FET-PET (95% CI 0.7–0.89), and 0.81 for expert consensus (95% CI 0.7–0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [¹⁸F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities.

Conclusion

Automated, joint image analysis of [¹⁸F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05427-8.

The Longitudinal Imaging Tracker (BrICS-LIT):A Cloud Platform for Monitoring Treatment Response in Glioblastoma Patients

Glioblastoma is a common and aggressive form of brain cancer affecting up to 20,000 new patients in the US annually. Despite rigorous therapies, current median survival is only 15-20 months. Patients who complete initial treatment undergo follow-up imaging at routine intervals to assess for tumor recurrence. Imaging is a central part of brain tumor management, but MRI findings in patients with brain tumor can be challenging to interpret and are further confounded by interpretation variability. Disease-specific structured reporting attempts to reduce variability in imaging results by implementing well-defined imaging criteria and standardized language. The Brain Tumor Reporting and Data System (BT-RADS) is one such framework streamlined for clinical workflows and includes quantitative criteria for more objective evaluation of follow-up imaging. To facilitate accurate and objective monitoring of patients during the follow-up period, we developed a cloud platform, the Brain Imaging Collaborative Suite's Longitudinal Imaging Tracker (BrICS-LIT). BrICS-LIT uses semiautomated tumor segmentation algorithms of both T2-weighted FLAIR and contrast-enhanced T1-weighted MRI to assist clinicians in quantitative assessment of brain tumors. The LIT platform can ultimately guide clinical decision-making for patients with glioblastoma by providing quantitative metrics for BT-RADS scoring. Further, this platform has the potential to increase objectivity when measuring efficacy of novel therapies for patients with brain tumor during their follow-up. Therefore, LIT will be used to track patients in a dose-escalated clinical trial, where spectroscopic MRI has been used to guide radiation therapy (Clinicaltrials.gov NCT03137888), and compare patients to a control group that received standard of care.

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.

Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Part 2 - Radiological features and metric markers

After chemoradiotherapy for glioblastoma, pseudoprogression can occur and must be distinguished from true progression to correctly manage glioblastoma treatment and follow-up. Conventional treatment response assessment is evaluated via conventional MRI (contrast-enhanced T1-weighted and T2/FLAIR), which is unreliable. The emergence of advanced MRI techniques, MR spectroscopy, and PET tracers has improved pseudoprogression diagnostic accuracy. This review presents a literature review of the different imaging techniques and potential imaging biomarkers to differentiate 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.

A Comprehensive Proteomic SWATH-MS Workflow for Profiling Blood Extracellular Vesicles: A New Avenue for Glioma Tumour Surveillance

Improving outcomes for diffuse glioma patients requires methods that can accurately and sensitively monitor tumour activity and treatment response. Extracellular vesicles (EV) are membranous nanoparticles that can traverse the blood-brain-barrier, carrying oncogenic molecules into the circulation. Measuring clinically relevant glioma biomarkers cargoed in circulating EVs could revolutionise how glioma patients are managed. Despite their suitability for biomarker discovery, the co-isolation of highly abundant complex blood proteins has hindered comprehensive proteomic studies of circulating-EVs. Plasma-EVs isolated from pre-operative glioma grade II-IV patients (n = 41) and controls (n = 11) were sequenced by Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) and data extraction was performed by aligning against a custom 8662-protein library. Overall, 4054 proteins were measured in plasma-EVs. Differentially expressed proteins and putative circulating-EV markers were identified (adj. p-value < 0.05), including those reported in previous in-vitro and ex-vivo glioma-EV studies. Principal component analysis showed that plasma-EV protein profiles clustered according to glioma histological-subtype and grade, and plasma-EVs resampled from patients with recurrent tumour progression grouped with more aggressive glioma samples. The extensive plasma-EV proteome profiles achieved here highlight the potential for SWATH-MS to define circulating-EV biomarkers for objective blood-based measurements of glioma activity that could serve as ideal surrogate endpoints to assess tumour progression and allow more dynamic, patient-centred treatment protocols.

Extracellular Vesicles from Neurosurgical Aspirates Identifies Chaperonin Containing TCP1 Subunit 6A as a Potential Glioblastoma Biomarker with Prognostic Significance

Glioblastoma, WHO-grade IV glioma, carries a dismal prognosis owing to its infiltrative growth and limited treatment options. Glioblastoma-derived extracellular vesicles (EVs; 30-1000 nm membranous particles) influence the microenvironment to mediate tumor aggressiveness and carry oncogenic cargo across the blood-brain barrier into the circulation. As such, EVs are biomarker reservoirs with enormous potential for assessing glioblastoma tumors in situ. Neurosurgical aspirates are rich sources of EVs, isolated directly from glioma microenvironments. EV proteomes enriched from glioblastoma (n = 15) and glioma grade II-III (n = 7) aspirates are compared and 298 differentially-abundant proteins (p-value < 0.00496) are identified using quantitative LC-MS/MS. Along with previously reported glioblastoma-associated biomarkers, levels of all eight subunits of the key molecular chaperone, T-complex protein 1 Ring complex (TRiC), are higher in glioblastoma-EVs, including CCT2, CCT3, CCT5, CCT6A, CCT7, and TCP1 (p < 0.00496). Analogous increases in TRiC transcript levels and DNA copy numbers are detected in silico; CCT6A has the greatest induction of expression and amplification in glioblastoma and shows a negative association with survival (p = 0.006). CCT6A is co-localized with EGFR at 7p11.2, with a strong tendency for co-amplification (p < 0.001). Immunohistochemistry corroborates the CCT6A proteomics measurements and indicated a potential link between EGFR and CCT6A tissue expression. Putative EV-biomarkers described here should be further assessed in peripheral blood.

Diagnostic value of radiolabeled amino acid PET for detection of pseudoprogression of brain tumor after treatment: a meta-analysis

PURPOSE

The purpose of the current study was to investigate the diagnostic performance of radiolabeled amino acid PET for detection of pseudoprogression (PsP) of brain tumor after treatment through a systematic review and meta-analysis.

METHODS

The PubMed and EMBASE database, from the earliest available date of indexing through 15 February 2019, were searched for studies evaluating the diagnostic performance of radiolabeled amino acid PET for detection of PsP. We determined the sensitivities and specificities across studies, calculated positive and negative likelihood ratios, and constructed summary receiver operating characteristic (SROC) curves.

RESULTS

Across seven results from six studies (971 patients), the pooled sensitivity was 0.89 [95% confidence interval (CI): 0.82-0.94] without heterogeneity (I2 = 0.0) and a pooled specificity of 0.88 (95% CI: 0.76-0.94) without heterogeneity (I2=29.4). Likelihood ratio syntheses gave an overall positive likelihood ratio of 7.3 (95% CI: 3.6-14.7) and negative likelihood ratio of 0.12 (95% CI: 0.07-0.21). The pooled diagnostic odds ratio (DOR) was 60 (95% CI: 23-152). Hierarchical SROC curve indicates that the areas under the curve (AUC) was 0.92 (95% CI: 0.90-0.94).

CONCLUSION

The current meta-analysis showed the good sensitivity and specificity of radiolabeled amino acid PET for detection of PsP of brain tumor after treatment. Also, the DOR was high and SROC curve showed high AUC value.

Ki-67 and MCM6 labeling indices are correlated with overall survival in anaplastic oligodendroglioma, IDH1-mutant and 1p/19q-codeleted: a multicenter study from the French POLA network

Anaplastic oligodendroglioma (AO), IDH-mutant and 1p/19q codeleted (IDHmut+/1p19qcodel), is a high-grade glioma with only limited prognostic markers. The primary objective of this study was to evaluate, by immunohistochemistry, the prognostic value of two proliferation markers, MCM6 and Ki-67, in a large series of IDHmut+/1p19qcodel AO included in the POLA ("Prise en charge des Oligodendrogliomes Anaplasiques") French national multicenter network. We additionally examined the transcriptome obtained from this series to understand the functional pathways dysregulated with the mRNA overexpression of these two markers. The labeling indices (LI) of MCM6 and Ki-67 were obtained via computer-assisted color image analyses on immunostained AO tissues of the cohort (n = 220). Furthermore, a subgroup of AO (n = 68/220) was used to perform transcriptomic analyses. A high LI of either MCM6 (≥50%) or Ki-67 (≥15%) correlated with shorter overall survival, both in univariate (P = 0.013 and P = 0.004, respectively) and multivariate analyses (P = 0.027; multivariate Cox model including age, mitotic index, MCM6 and Ki-67). MCM6 and Ki-67 LI also correlated with overall survival in an additional retrospective cohort of 30 grade II IDHmut+/1p19qcodel oligodendrogliomas. The prognostic value of MCM6 mRNA level was confirmed in The Cancer Genome Atlas (TCGA) IDHmut+/1p19qcodel gliomas. The transcriptomic approach revealed that high transcriptional expressions of MCM6 and MKI67 were both linked positively with cell cycle progression, DNA replication, mitosis, pro-neural phenotype as well as neurogenesis, and negatively with microglial cell activation, immune response, positive regulation of myelination, oligodendrocyte development, beta-amyloid binding and postsynaptic specialization. In conclusion, the overexpression of MCM6 and/or Ki-67 is independently associated to shorter overall survival in IDHmut+/1p19qcodel AO. These two easy-to-use and cost-effective markers could thus be used concurrently in routine pathology practice. Additionally, the transcriptomic analyses showed that AO with high proliferation index have down-regulated immune response and lower microglial cells activation, and bears pro-neural phenotype.

Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme

RATIONALE AND OBJECTIVES

Glioblastoma multiforme (GBM) is the most common and deadly type of primary malignant tumor of the central nervous system. Accurate risk stratification is vital for a more personalized approach in GBM management. The purpose of this study is to develop and validate a MRI-based prognostic quantitative radiomics classifier in patients with newly diagnosed GBM and to evaluate whether the classifier allows stratification with improved accuracy over the clinical and qualitative imaging features risk models.

METHODS

Clinical and MR imaging data of 127 GBM patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive. Regions of interest corresponding to high signal intensity portions of tumor were drawn on postcontrast T1-weighted imaging (post-T1WI) on the 127 patients (allocated in a 2:1 ratio into a training [n = 85] or validation [n = 42] set), then 3824 radiomics features per patient were extracted. The dimension of these radiomics features were reduced using the minimum redundancy maximum relevance algorithm, then Cox proportional hazard regression model was used to build a radiomics classifier for predicting overall survival (OS). The value of the radiomics classifier beyond clinical (gender, age, Karnofsky performance status, radiation therapy, chemotherapy, and type of resection) and VASARI features for OS was assessed with multivariate Cox proportional hazards model. Time-dependent receiver operating characteristic curve analysis was used to assess the predictive accuracy.

RESULTS

A classifier using four post-T1WI-MRI radiomics features built on the training dataset could successfully separate GBM patients into low- or high-risk group with a significantly different OS in training (HR, 6.307 [95% CI, 3.475-11.446]; p < 0.001) and validation set (HR, 3.646 [95% CI, 1.709-7.779]; p < 0.001). The area under receiver operating characteristic curve of radiomics classifier (training, 0.799; validation, 0.815 for 12-month) was higher compared to that of the clinical risk model (Karnofsky performance status, radiation therapy; training, 0.749; validation, 0.670 for 12-month), and none of the qualitative imaging features was associated with OS. The predictive accuracy was further improved when combined the radiomics classifier with clinical data (training, 0.819; validation: 0.851 for 12-month).

CONCLUSION

A classifier using radiomics features allows preoperative prediction of survival and risk stratification of patients with GBM, and it shows improved performance compared to that of clinical and qualitative imaging features models.

Cerebral Radiation Necrosis: Incidence, Pathogenesis, Diagnostic Challenges, and Future Opportunities

PURPOSE OF REVIEW

Cerebral radiation necrosis (CRN) is a major dose-limiting adverse event of radiotherapy. The incidence rate of RN varies with the radiotherapy modality, total dose, dose fractionation, and the nature of the lesion being targeted. In addition to these known and controllable features, there is a stochastic component to the occurrence of CRN-the genetic profile of the host or the lesion and their role in the development of CRN.

RECENT FINDINGS

Recent studies provide some insight into the genetic mechanisms underlying radiation-induced brain injury. In addition to these incompletely understood host factors, the diagnostic criteria for CRN using structural and functional imaging are also not clear, though multiple structural and functional imaging modalities exist, a combination of which may prove to be the ideal diagnostic imaging approach. As the utilization of novel molecular therapies and immunotherapy increases, the incidence of CNR is expected to increase and its diagnosis will become more challenging. Tissue biopsies can be insensitive and suffer from sampling biases and procedural risks. Liquid biopsies represent a promising, accurate, and non-invasive diagnostic strategy, though this modality is currently in its infancy. A better understanding of the pathogenesis of CRN will expand and optimize the diagnosis and management of CRN by better utilizing existing treatment options including bevacizumab, pentoxifylline, hyperbaric oxygen therapy, and laser interstitial thermal therapy.

Glioblastoma with brainstem leptomeningeal pseudoprogression following radiation therapy

In brain tumor patients, worsening of imaging findings in the first 6 months after surgical debulking and chemoradiation can occur in the absence of tumor growth, a phenomenon known as pseudoprogression. Awareness of pseudoprogression is important as it can lead to unnecessary additional changes in patient management. In this case, a patient with bilateral frontal glioblastoma presented with new post-treatment brainstem leptomeningeal enhancement which was distant from the original tumor site, concerning for disease progression. However, the patient was asymptomatic and correlation of leptomeningeal enhancement locations with radiation therapy dose maps revealed high doses at the affected site, supporting a diagnosis of treatment effect which was confirmed by resolution on follow-up imaging after treatment with steroids. Parenchymal pseudoprogression in brain tumor patients is well-documented, but worsening leptomeningeal enhancement following therapy may also represent treatment effects. If spatially remote leptomeningeal enhancement occurs, correlation with radiation dose maps may be useful in suggesting a diagnosis of treatment effect over tumor progression.

Blood-Based Biomarkers in High Grade Gliomas: a Systematic Review

High-grade gliomas (HGG) are the most common malignant primary brain tumor in adults. During the course of disease, several challenges occur, like measuring tumor burden, monitoring of treatment response, estimating the patient's prognosis, and distinguishing between true progression and pseudo-progression. So far, no blood-based biomarker has been established in the clinical routine to address these challenges. The aim of this systematic review was to analyze the present evidence on blood-based biomarkers for HGG. We systematically searched in PubMed, Web of Sciences, Scopus, and Cochrane Library databases for publications before 30th of March 2018 reporting on associations of blood-based biomarkers in HGG patients with different endpoints as overall survival, progression-free survival, and postoperative monitoring. Quality assessment of the studies according to QUIPS and STARD guidelines was performed. In accordance with the GRADE guidelines, level of evidence (I-IV) for each of the tested biomarkers was assessed. One thousand six hundred eighty unique records were identified. Of these, 170 original articles were included to this review. Four hundred fifteen different blood-based biomarkers analyzed in 15.041 patients with HGG as also their corresponding recurrent tumors. Ten predictive biomarkers reached level II of evidence. No biomarker achieved level I of evidence. In this review, 10 blood-based biomarkers were selected as most promising biomarkers for HGG: α2-Heremans-Schmid glycoprotein (AHSG), albumin, glucose, insulin-like growth factor- binding protein 2 (IGFBP-2), macrophage inflammatory protein 1δ (MIP-1 δ), macrophage inflammatory protein 3ß (MIP-3ß), neutrophil-lymphocyte ratio (NLR), red blood cell distribution width (RDW), soluble glycoprotein 130 (Sgp130), and chitinase-3-like protein 1 (YKL-40). To further assess the clinical significance of these biomarkers, the evaluation in a larger cohort of HGG and their corresponding subgroups would be necessary.

Emerging Functional Imaging Biomarkers of Tumour Responses to Radiotherapy

Tumour responses to radiotherapy are currently primarily assessed by changes in size. Imaging permits non-invasive, whole-body assessment of tumour burden and guides treatment options for most tumours. However, in most tumours, changes in size are slow to manifest and can sometimes be difficult to interpret or misleading, potentially leading to prolonged durations of ineffective treatment and delays in changing therapy. Functional imaging techniques that monitor biological processes have the potential to detect tumour responses to treatment earlier and refine treatment options based on tumour biology rather than solely on size and staging. By considering the biological effects of radiotherapy, this review focusses on emerging functional imaging techniques with the potential to augment morphological imaging and serve as biomarkers of early response to radiotherapy.

Measurement of Plasma Cell-Free Mitochondrial Tumor DNA Improves Detection of Glioblastoma in Patient-Derived Orthotopic Xenograft Models

The factors responsible for the low detection rate of cell-free tumor DNA (ctDNA) in the plasma of patients with glioblastoma (GBM) are currently unknown. In this study, we measured circulating nucleic acids in patient-derived orthotopically implanted xenograft (PDOX) models of GBM (n = 64) and show that tumor size and cell proliferation, but not the integrity of the blood-brain barrier or cell death, affect the release of ctDNA in treatment-naïve GBM PDOX. Analysis of fragment length profiles by shallow genome-wide sequencing (<0.2× coverage) of host (rat) and tumor (human) circulating DNA identified a peak at 145 bp in the human DNA fragments, indicating a difference in the origin or processing of the ctDNA. The concentration of ctDNA correlated with cell death only after treatment with temozolomide and radiotherapy. Digital PCR detection of plasma tumor mitochondrial DNA (tmtDNA), an alternative to detection of nuclear ctDNA, improved plasma DNA detection rate (82% vs. 24%) and allowed detection in cerebrospinal fluid and urine. Mitochondrial mutations are prevalent across all cancers and can be detected with high sensitivity, at low cost, and without prior knowledge of tumor mutations via capture-panel sequencing. Coupled with the observation that mitochondrial copy number increases in glioma, these data suggest analyzing tmtDNA as a more sensitive method to detect and monitor tumor burden in cancer, specifically in GBM, where current methods have largely failed. SIGNIFICANCE: These findings show that detection of tumor mitochondrial DNA is more sensitive than circulating tumor DNA analysis to detect and monitor tumor burden in patient-derived orthotopic xenografts of glioblastoma.

Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma

We aimed to investigate the feasibility of machine learning (ML) algorithm to distinguish pseudoprogression (PsPD) from progression (PD) in patients with glioblastoma (GBM). We recruited the patients diagnosed as primary GBM who received gross total resection (GTR) and concurrent chemoradiotherapy in two institutions from April 2010 to April 2017 and presented suspicious contrast-enhanced lesion on brain magnetic resonance imaging (MRI) during follow-up. Patients from two institutions were allocated to training (N = 59) and testing (N = 19) datasets, respectively. We developed a convolutional neural network combined with a long short-term memory ML structure. MRI data, which was 9 axial post-contrast T1-weighted images in our study, and clinical features were incorporated (Model 1). In the testing set, the trained Model 1 resulted in AUC of 0.83, AUPRC of 0.87, and F1-score of 0.74 using optimal threshold. The performance was superior to that of Model 2 (CNN-LSTM model with MRI data alone) and Model 3 (random forest model with clinical feature alone). The developed algorithm involving MRI data and clinical features could help making decision during follow-up of patients with GBM treated with GTR and concurrent CCRT.

Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab

Background

Anti-angiogenic therapy with bevacizumab is the most widely used treatment option for recurrent glioblastoma, but therapeutic response varies substantially and effective biomarkers for patient selection are not available. To this end, we determine whether novel quantitative radiomic strategies on the basis of MRI have the potential to noninvasively stratify survival and progression in this patient population.

Methods

In an initial cohort of 126 patients, we identified a distinct set of features representative of the radiographic phenotype on baseline (pretreatment) MRI. These selected features were evaluated on a second cohort of 165 patients from the multicenter BRAIN trial with prospectively acquired clinical and imaging data. Features were evaluated in terms of prognostic value for overall survival (OS), progression-free survival (PFS), and progression within 3, 6, and 9 months using baseline imaging and first follow-up imaging at 6 weeks posttreatment initiation.

Results

Multivariable analysis of features derived at baseline imaging resulted in significant stratification of OS (hazard ratio [HR] = 2.5; log-rank P = 0.001) and PFS (HR = 4.5; log-rank P = 2.1 × 10-5) in validation data. These stratifications were stronger compared with clinical or volumetric covariates (permutation test false discovery rate [FDR] <0.05). Univariable analysis of a prognostic textural heterogeneity feature (information correlation) derived from postcontrast T1-weighted imaging revealed significantly higher scores for patients who progressed within 3 months (Wilcoxon test P = 8.8 × 10-8). Generally, features derived from postcontrast T1-weighted imaging yielded higher prognostic power compared with precontrast enhancing T2-weighted imaging.

Conclusion

Radiomics provides prognostic value for survival and progression in patients with recurrent glioblastoma receiving bevacizumab treatment. These results could lead to the development of quantitative pretreatment biomarkers to predict benefit from bevacizumab using standard of care imaging.

Retrospective Analysis of Radiological Recurrence Patterns in Glioblastoma, Their Prognostic Value And Association to Postoperative Infarct Volume

Recent studies suggested that postoperative hypoxia might trigger invasive tumor growth, resulting in diffuse/multifocal recurrence patterns. Aim of this study was to analyze distinct recurrence patterns and their association to postoperative infarct volume and outcome. 526 consecutive glioblastoma patients were analyzed, of which 129 met our inclusion criteria: initial tumor diagnosis, surgery, postoperative diffusion-weighted imaging and tumor recurrence during follow-up. Distinct patterns of contrast-enhancement at initial diagnosis and at first tumor recurrence (multifocal growth/progression, contact to dura/ventricle, ependymal spread, local/distant recurrence) were recorded by two blinded neuroradiologists. The association of radiological patterns to survival and postoperative infarct volume was analyzed by uni-/multivariate survival analyses and binary logistic regression analysis. With increasing postoperative infarct volume, patients were significantly more likely to develop multifocal recurrence, recurrence with contact to ventricle and contact to dura. Patients with multifocal recurrence (Hazard Ratio (HR) 1.99, P = 0.010) had significantly shorter OS, patients with recurrent tumor with contact to ventricle (HR 1.85, P = 0.036), ependymal spread (HR 2.97, P = 0.004) and distant recurrence (HR 1.75, P = 0.019) significantly shorter post-progression survival in multivariate analyses including well-established prognostic factors like age, Karnofsky Performance Score (KPS), therapy, extent of resection and patterns of primary tumors. Postoperative infarct volume might initiate hypoxia-mediated aggressive tumor growth resulting in multifocal and diffuse recurrence patterns and impaired survival.

Differentiation of residual/recurrent gliomas from postradiation necrosis with arterial spin labeling and diffusion tensor magnetic resonance imaging-derived metrics

PURPOSE

The aim of this study is to differentiate recurrent/residual gliomas from postradiation changes using arterial spin labeling (ASL) perfusion and diffusion tensor imaging (DTI)-derived metrics.

METHODS

Prospective study was conducted upon 42 patients with high-grade gliomas after radiotherapy only or prior to other therapies that underwent routine MR imaging, ASL, and DTI. The tumor blood flow (TBF), fractional anisotropy (FA), and mean diffusivity (MD) of the enhanced lesion and related edema were calculated. The lesion was categorized as recurrence/residual or postradiation changes.

RESULTS

There was significant differences between residual/recurrent gliomas and postradiation changes of TBF (P = 0.001), FA (P = 0.001 and 0.04), and MD (P = 0.001) of enhanced lesion and related edema respectively. The area under the curve (AUC) of TBF of enhanced lesion and related edema used to differentiate residual/recurrent gliomas from postradiation changes were 0.95 and 0.93 and of MD were 0.95 and 0.81 and of FA were 0.81 and 0.695, respectively. Combined ASL and DTI metrics of the enhanced lesion revealed AUC of 0.98, accuracy of 95%, sensitivity of 93.8%, specificity of 95.8%, positive predictive value (PPV) of 93.8%, and negative predictive value (NPV) of 95.8%. Combined metrics of ASL and DTI of related edema revealed AUC of 0.97, accuracy of 92.5%, sensitivity of 93.8%, specificity of 91.7%, PPV of 88.2%, and NPV of 95.7.

CONCLUSION

Combined ASL and DTI metrics of enhanced lesion and related edema are valuable noninvasive tools in differentiating residual/recurrent gliomas from postradiation changes.

Brain Tumor Imaging

Modern imaging techniques, particularly functional imaging techniques that interrogate some specific aspect of underlying tumor biology, have enormous potential in neuro-oncology for disease detection, grading, and tumor delineation to guide biopsy and resection; monitoring treatment response; and targeting radiotherapy. This brief review considers the role of magnetic resonance imaging and spectroscopy, and positron emission tomography in these areas and discusses the factors that limit translation of new techniques to the clinic, in particular, the cost and difficulties associated with validation in multicenter clinical trials.

Diagnostic accuracy of proton magnetic resonance spectroscopy and perfusion-weighted imaging in brain gliomas follow-up: a single institutional experience

Objectives The objective of this study was to evaluate whether proton magnetic resonance spectroscopy and perfusion magnetic resonance imaging (MRI) are able to increase diagnostic accuracy in the follow-up of brain gliomas, identifying the progression of disease before it becomes evident in the standard MRI; also to evaluate which of the two techniques has the best diagnostic accuracy. Methods Eighty-three patients with cerebral glioma (50 high-grade gliomas (HGGs), 33 low-grade gliomas (LGGs)) were retrospectively enrolled. All patients underwent standard MRI, H spectroscopic and perfusion echo-planar imaging MRI. For spectroscopy variations of choline/creatine, choline/N-acetyl-aspartate ratio, and lipids and lactates peak were considered. For perfusion 2.0 was considered the cerebral blood volume cut-off for progression. The combination of functional parameters gave a multiparametric score (0-2) to predict outcome. Diagnostic performance was determined by the receiver operating characteristic curve, with sensitivity, specificity, positive predictive and negative predictive values. Results In patients with LGGs a combined score of at least 1 was the best predictor for progression (odds ratio (OR) 3.91) with 8.4 months median anticipation of diagnosis compared to standard MRI. The individual advanced magnetic resonance technique did not show a diagnostic accuracy comparable to the combination of the two. Overall diagnostic accuracy area under the curve (AUC) was 0.881. In patients with HGGs the multiparametric score did not improve diagnostic accuracy significantly. Perfusion MRI was the best predictor of progression (OR 3.65), with 6.7 months median anticipation of diagnosis. Overall diagnostic accuracy AUC was 0.897. Then spectroscopy and perfusion MRI are able to identify tumour progression during follow-up earlier than standard MRI. Conclusion In patients with LGGs the combination of the functional parameters seems to be the best method for diagnosis of progression. In patients with HGGs perfusion is the best diagnostic method.

MRI in Glioma Immunotherapy: Evidence, Pitfalls, and Perspectives

Pseudophenomena, that is, imaging alterations due to therapy rather than tumor evolution, have an important impact on the management of glioma patients and the results of clinical trials. RANO (response assessment in neurooncology) criteria, including conventional MRI (cMRI), addressed the issues of pseudoprogression after radiotherapy and concomitant chemotherapy and pseudoresponse during antiangiogenic therapy of glioblastomas (GBM) and other gliomas. The development of cancer immunotherapy forced the identification of further relevant response criteria, summarized by the iRANO working group in 2015. In spite of this, the unequivocal definition of glioma progression by cMRI remains difficult particularly in the setting of immunotherapy approaches provided by checkpoint inhibitors and dendritic cells. Advanced MRI (aMRI) may in principle address this unmet clinical need. Here, we discuss the potential contribution of different aMRI techniques and their indications and pitfalls in relation to biological and imaging features of glioma and immune system interactions.

Combination of IVIM-DWI and 3D-ASL for differentiating true progression from pseudoprogression of Glioblastoma multiforme after concurrent chemoradiotherapy: study protocol of a prospective diagnostic trial

Background

Standard therapy for Glioblastoma multiforme (GBM) involves maximal safe tumor resection followed with radiotherapy and concurrent adjuvant temozolomide. About 20 to 30% patients undergoing their first post-radiation MRI show increased contrast enhancement which eventually recovers without any new treatment. This phenomenon is referred to as pseudoprogression. Differentiating tumor progression from pseudoprogression is critical for determining tumor treatment, yet this capacity remains a challenge for conventional magnetic resonance imaging (MRI). Thus, a prospective diagnostic trial has been established that utilizes multimodal MRI techniques to detect tumor progression at its early stage. The purpose of this trial is to explore the potential role of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and three-dimensional arterial spin labeling imaging (3D-ASL) in differentiating true progression from pseudoprogression of GBM. In addition, the diagnostic performance of quantitative parameters obtained from IVIM-DWI and 3D-ASL, including apparent diffusion coefficient (ADC), slow diffusion coefficient (D), fast diffusion coefficient (D*), perfusion fraction (f), and cerebral blood flow (CBF), will be evaluated.

Methods

Patients that recently received a histopathological diagnosis of GBM at our hospital are eligible for enrollment. The patients selected will receive standard concurrent chemoradiotherapy and adjuvant temozolomide after surgery, and then will undergo conventional MRI, IVIM-DWI, 3D-ASL, and contrast-enhanced MRI. The quantitative parameters, ADC, D, D*, f, and CBF, will be estimated for newly developed enhanced lesions. Further comparisons will be made with unpaired t-tests to evaluate parameter performance in differentiating true progression from pseudoprogression, while receiver-operating characteristic (ROC) analyses will determine the optimal thresholds, as well as sensitivity and specificity. Finally, relationships between these parameters will be assessed with Pearson’s correlation and partial correlation analyses.

Discussion

The results of this study may demonstrate the potential value of using multimodal MRI techniques to differentiate true progression from pseudoprogression in its early stages to help decision making in early intervention and improve the prognosis of GBM.

Trial registration

This study has been registered at ClinicalTrials.gov (NCT02622620) on November 18, 2015 and published on March 28, 2016.

A state-of-the-art review and guidelines for tumor treating fields treatment planning and patient follow-up in glioblastoma

Tumor treating fields (TTFields) are an integral treatment modality in the management of glioblastoma and extend overall survival when combined with maintenance temozolomide in newly diagnosed patients. Complexities exist regarding correct selection of imaging sequences with which to perform TTFields treatment planning. Guidelines are warranted first, to facilitate treatment planning standardization across medical disciplines and institutions, to ensure optimal TTFields delivery to the tumor and peritumoral brain zone while maximizing patient safety, and also to mitigate the risk of premature cessation of a potentially beneficial treatment. This summary guideline outlines methods for starting patients on TTFields, for monitoring patient response to therapy and provides a framework for evaluating when therapy should be re-planned, based on the extent of sequential imaging changes.

Patterns and Time Dependence of Unspecific Enhancement in Postoperative Magnetic Resonance Imaging After Glioblastoma Resection

OBJECTIVE

Postoperative magnetic resonance imaging (MRI) is recommended soon after glioma surgery to avoid reactive nonneoplastic contrast enhancement indistinguishable from tumor. The purpose of this study was to analyze these patterns of postoperative contrast enhancement at 3 T to define the optimal time frame for postoperative MRI.

METHODS

MRI for 206 glioblastoma surgeries in 173 patients who underwent pre- and postoperative and at least 1 follow-up 3T MRI for each surgery were analyzed retrospectively. Postoperative MRI was assessed in consensus by 2 neuroradiologists, blinded to the time after surgery. Postoperative contrast enhancement marginal to the resection cavity was analyzed and classified as vascular, linear, or nodular. The cause of the contrast enhancement (ie, reactive vs. tumor) was assessed by comparing pre-, postoperative, and follow-up MRI.

RESULTS

Within 45 hours after surgery, reactive enhancement appeared in 17.9% of cases. After 45 hours, the fraction of reactive changes increased to 34.1%. Linear enhancement was more often reactive (66.1%, 39/59 cases), whereas nodular enhancement was mainly residual tumor (93.2%, 68/73 cases). Specificity of nodular enhancement was high for tumor recurrence/tumor progression (91.5%).

CONCLUSIONS

To avoid an increasing number of MRIs with reactive contrast enhancement, postoperative MRI at 3 T should be performed within 45 hours after surgery. However, reactive contrast enhancement can occur at all time points. In these cases, the pattern of the contrast enhancement may help to differentiate its cause.

Late Pseudoprogression in Glioblastoma: Diagnostic Value of Dynamic O-(2-[18F]fluoroethyl)-L-Tyrosine PET

PURPOSE

Pseudoprogression (PsP) is characterized by therapy-associated but not tumor growth-associated increases of contrast-enhancing glioblastoma lesions on MRI. Although typically occurring during the first 3 months after radiochemotherapy, PsP may occur later in the course of the disease and may then be particularly difficult to distinguish from true tumor progression. We explored PET using O-(2-[(18)F]fluoroethyl)-L-tyrosine ((18)F-FET-PET) to approach the diagnostic dilemma.

EXPERIMENTAL DESIGN

Twenty-six patients with glioblastoma that presented with increasing contrast-enhancing lesions later than 3 months after completion of radiochemotherapy underwent (18)F-FET-PET. Maximum and mean tumor/brain ratios (TBRmax and TBRmean) of (18)F-FET uptake as well as time-to-peak (TTP) and patterns of the time-activity curves were determined. The final diagnosis of true progression versus late PsP was based on follow-up MRI using RANO criteria.

RESULTS

Late PsP occurred in 7 patients with a median time from radiochemotherapy completion of 24 weeks while the remaining patients showed true tumor progression. TBRmax and TBRmean were significantly higher in patients with true progression than in patients with late PsP (TBRmax 2.4 ± 0.1 vs. 1.5 ± 0.2, P = 0.003; TBRmean 2.1 ± 0.1 vs. 1.5 ± 0.2, P = 0.012) whereas TTP was significantly shorter (mean TTP 25 ± 2 vs. 40 ± 2 min, P < 0.001). ROC analysis yielded an optimal cutoff value of 1.9 for TBRmax to differentiate between true progression and late PsP (sensitivity 84%, specificity 86%, accuracy 85%, P = 0.015).

CONCLUSIONS

O-(2-[(18)F]fluoroethyl)-L-tyrosine PET provides valuable information in assessing the elusive phenomenon of late PsP. Clin Cancer Res; 22(9); 2190-6. ©2015 AACR.