Multimodality assessment of brain tumors and tumor recurrence

Brain metastasis: An insight into novel molecular targets for theranostic approaches

Brain metastases (BrM) are common malignant lesions in the central nervous system, and pose a significant threat in advanced-stage malignancies due to delayed diagnosis and limited therapeutic options. Their distinct genomic profiles underscore the need for molecular profiling to tailor effective treatments. Recent advances in cancer biology have uncovered molecular drivers underlying tumor initiation, progression, and metastasis. This, coupled with the advances in molecular imaging technology and radiotracer synthesis, has paved the way for the development of innovative radiopharmaceuticals with enhanced specificity and affinity for BrM specific targets. Despite the challenges posed by the blood-brain barrier to effective drug delivery, several radiolabeled compounds have shown promise in detecting and targeting BrM. This manuscript provides an overview of the recent advances in molecular biomarkers used in nuclear imaging and targeted radionuclide therapy in both clinical and preclinical settings. Additionally, it explores potential theranostic applications addressing the unique challenges posed by BrM.

Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology

PURPOSE

Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma.

METHODS

This study involved multi-parametric brain MRI scans (T1, T1-contrast enhanced, T2, FLAIR) acquired at pre-operative and follow-up time for 160 patients diagnosed with glioma, representing the BraTS-Reg 2022 challenge dataset. ConvUNet-DIR, a deep learning-based deformable registration workflow using 3D U-Net style architecture as a core, was developed to establish correspondence between the MRI scans. The workflow consists of three components: (1) the U-Net learns features from pairs of MRI scans and estimates a mapping between them, (2) the grid generator computes the sampling grid based on the derived transformation parameters, and (3) the spatial transformation layer generates a warped image by applying the sampling operation using interpolation. A similarity measure was used as a loss function for the network with a regularization parameter limiting the deformation. The model was trained via unsupervised learning using pairs of MRI scans on a training data set (n = 102) and validated on a validation data set (n = 26) to assess its generalizability. Its performance was evaluated on a test set (n = 32) by computing the Dice score and structural similarity index (SSIM) quantitative metrics. The model's performance also was compared with the baseline state-of-the-art VoxelMorph (VM1 and VM2) learning-based algorithms.

RESULTS

The ConvUNet-DIR model showed promising competency in performing accurate 3D deformable registration. It achieved a mean Dice score of 0.975 ± 0.003 and SSIM of 0.908 ± 0.011 on the test set (n = 32). Experimental results also demonstrated that ConvUNet-DIR outperformed the VoxelMorph algorithms concerning Dice (VM1: 0.969 ± 0.006 and VM2: 0.957 ± 0.008) and SSIM (VM1: 0.893 ± 0.012 and VM2: 0.857 ± 0.017) metrics. The time required to perform a registration for a pair of MRI scans is about 1 s on the CPU.

CONCLUSIONS

The developed deep learning-based model can perform an end-to-end deformable registration of a pair of 3D MRI scans for glioma patients without human intervention. The model could provide accurate, efficient, and robust deformable registration without needing pre-alignment and labeling. It outperformed the state-of-the-art VoxelMorph learning-based deformable registration algorithms and other supervised/unsupervised deep learning-based methods reported in the literature.

Impact of [11C]methionine PET with Bayesian penalized likelihood reconstruction on glioma grades based on new WHO 2021 classification

OBJECTIVE

The uptake of [11C]methionine in positron emission tomography (PET) imaging overlapped in earlier images of tumors. Bayesian penalized likelihood (BPL) reconstruction increases the quantitative values of tumors compared with conventional ordered subset-expectation maximization (OSEM). The present study aimed to grade glioma malignancy based on the new WHO 2021 classification using [11C]methionine PET images reconstructed using BPL.

METHODS

We categorized 32 gliomas in 28 patients as grades 2/3 (n = 15) and 4 (n = 17) based on the WHO 2021 classification. All [11C]methionine images were reconstructed using OSEM + time-of-flight (TOF) and BPL + TOF (β = 200). Maximum standardized uptake value (SUVmax) and tumor-to-normal tissue ratio (T/Nmax) were measured at each lesion.

RESULTS

The mean SUVmax was 4.65 and 4.93 in grade 2/3 and 6.38 and 7.11 in grade 4, and the mean T/Nmax was 7.08 and 7.22 in grade 2/3 and 9.30 and 10.19 in grade 4 for OSEM and BPL, respectively. The BPL significantly increased these values in grade 4 gliomas. The area under the receiver operator characteristic (ROC) curve (AUC) for SUVmax was the highest (0.792) using BPL.

CONCLUSIONS

The BPL increased mean SUVmax and mean T/Nmax in lesions with higher contrast such as grade 4 glioma. The discrimination power between grades 2/3 and 4 in SUVmax was also increased using [11C]methionine PET images reconstructed with BPL.

Aided Diagnosis Model Based on Deep Learning for Glioblastoma, Solitary Brain Metastases, and Primary Central Nervous System Lymphoma with Multi-Modal MRI

(1) Background: Diagnosis of glioblastoma (GBM), solitary brain metastases (SBM), and primary central nervous system lymphoma (PCNSL) plays a decisive role in the development of personalized treatment plans. Constructing a deep learning classification network to diagnose GBM, SBM, and PCNSL with multi-modal MRI is important and necessary. (2) Subjects: GBM, SBM, and PCNSL were confirmed by histopathology with the multi-modal MRI examination (study from 1225 subjects, average age 53 years, 671 males), 3.0 T T2 fluid-attenuated inversion recovery (T2-Flair), and Contrast-enhanced T1-weighted imaging (CE-T1WI). (3) Methods: This paper introduces MFFC-Net, a classification model based on the fusion of multi-modal MRIs, for the classification of GBM, SBM, and PCNSL. The network architecture consists of parallel encoders using DenseBlocks to extract features from different modalities of MRI images. Subsequently, an L1-norm feature fusion module is applied to enhance the interrelationships among tumor tissues. Then, a spatial-channel self-attention weighting operation is performed after the feature fusion. Finally, the classification results are obtained using the full convolutional layer (FC) and Soft-max. (4) Results: The ACC of MFFC-Net based on feature fusion was 0.920, better than the radiomics model (ACC of 0.829). There was no significant difference in the ACC compared to the expert radiologist (0.920 vs. 0.924, p = 0.774). (5) Conclusions: Our MFFC-Net model could distinguish GBM, SBM, and PCNSL preoperatively based on multi-modal MRI, with a higher performance than the radiomics model and was comparable to radiologists.

11 C-Acetate PET/CT for Reactive Astrogliosis Outperforms 11 C-Methionine PET/CT in Glioma Classification and Survival Prediction

PURPOSE

11 C-acetate (ACE) PET/CT visualizes reactive astrogliosis in tumor microenvironment. This study compared 11 C-ACE and 11 C-methionine (MET) PET/CT for glioma classification and predicting patient survival.

PATIENTS AND METHODS

In this prospective study, a total of 142 patients with cerebral gliomas underwent preoperative MRI, 11 C-MET PET/CT, and 11 C-ACE PET/CT. Tumor-to-contralateral cortex (TNR MET ) and tumor-to-choroid plexus ratios (TNR ACE ) were calculated for 11 C-MET and 11 C-ACE. The Kruskal-Wallis test and Bonferroni post hoc analysis were used to compare the differences in 11 C-TNR MET and 11 C-TNR ACE . The Cox proportional hazards regression analysis and classification and regression tree models were used to assess progression-free survival (PFS) and overall survival (OS).

RESULTS

The median 11 C-TNR MET and 11 C-TNR ACE for oligodendrogliomas (ODs), IDH1 -mutant astrocytomas, IDH1 -wildtype astrocytomas, and glioblastomas were 2.75, 1.40, 2.30, and 3.70, respectively, and 1.40, 1.20, 1.77, and 2.87, respectively. The median 11 C-TNR MET was significantly different among the groups, except between ODs and IDH1 -wildtype astrocytomas, whereas the median 11 C-TNR ACE was significantly different among all groups. The classification and regression tree model identified 4 risk groups ( IDH1 -mutant with 11 C-TNR ACE ≤ 1.4, IDH1 -mutant with 11 C-TNR ACE > 1.4, IDH1 -wildtype with 11 C-TNR ACE ≤ 1.8, and IDH1 -wildtype with 11 C-TNR ACE > 1.8), with median PFS of 52.7, 44.5, 25.9, and 8.9 months, respectively. Using a 11 C-TNR ACE cutoff of 1.4 for IDH1 -mutant gliomas and a 11 C-TNR ACE cutoff of 2.0 for IDH1 -wildtype gliomas, all gliomas were divided into 4 groups with median OS of 52.7, 46.8, 27.6, and 12.0 months, respectively. Significant differences in PFS and OS were observed among the 4 groups after correcting for multiple comparisons.

CONCLUSIONS

11 C-ACE PET/CT is better for glioma classification and survival prediction than 11 C-MET PET/CT, highlighting its potential role in cerebral glioma patients.

Blood-brain barrier disruption defines the extracellular metabolome of live human high-grade gliomas

The extracellular microenvironment modulates glioma behaviour. It remains unknown if blood-brain barrier disruption merely reflects or functionally supports glioma aggressiveness. We utilised intra-operative microdialysis to sample the extracellular metabolome of radiographically diverse regions of gliomas and evaluated the global extracellular metabolome via ultra-performance liquid chromatography tandem mass spectrometry. Among 162 named metabolites, guanidinoacetate (GAA) was 126.32x higher in enhancing tumour than in adjacent brain. 48 additional metabolites were 2.05-10.18x more abundant in enhancing tumour than brain. With exception of GAA, and 2-hydroxyglutarate in IDH-mutant gliomas, differences between non-enhancing tumour and brain microdialysate were modest and less consistent. The enhancing, but not the non-enhancing glioma metabolome, was significantly enriched for plasma-associated metabolites largely comprising amino acids and carnitines. Our findings suggest that metabolite diffusion through a disrupted blood-brain barrier may largely define the enhancing extracellular glioma metabolome. Future studies will determine how the altered extracellular metabolome impacts glioma behaviour.

Mapping glioma heterogeneity using multiparametric 18 F-choline PET/MRI in childhood and teenage-young adults

OBJECTIVE

The heterogeneity of post-treatment imaging remains a significant challenge in children and teenagers/young adults (TYA) diagnosed with glioma. The aim of this study was to evaluate the utility of 18 F-choline PET/MRI in determining intratumoural heterogeneity in paediatric and TYA gliomas.

METHODS

Twenty-six patients (mean age 16 years, range 8-22 years) with suspected glioma disease progression were evaluated with 18 F-choline PET/MRI. Relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC) and maximum standardised uptake values (SUV max ) in enhancing (enh) and non-enhancing (ne) tumour volumes and normal-appearing white matter (wm) were calculated (rCBV enh , rCBV ne , rCBV wm , ADC enh , ADC ne , ADC wm , SUV enh , SUV ne and SUV wm ).

RESULTS

Significantly higher SUV enh and SUV ne compared with SUV wm were observed [SUV enh 0.89 (0.23-1.90), SUV ne 0.36 (0.16-0.78) versus SUV wm 0.15 (0.04-1.19); P  < 0.001 and P  = 0.004, respectively]. Equivalent results were observed for ADV and rCBV (ADC enh , ADC ne : P  < 0.001 versus ADC wm ; rCBV enh , rCBV ne : P  < 0.001 versus rCBV wm ). The highest values for mean SUV max [0.89 (0.23-1.90)] and mean rCBV [2.1 (0.74-5.08)] were in the enhancing component, while the highest values for ADC [1780 mm 2 /s (863-2811)] were in the necrotic component.

CONCLUSION

18 F-choline PET/MRI is able map imaging heterogeneity in paediatric and TYA gliomas, detecting post-treatment enhancing, non-enhancing, and necrotic tumour components equivalent to ADC and DSC-derived rCBV. This offers potential in the response assessment of diffuse non-enhancing gliomas and in selected cases such as posterior fossa tumours where quantitative MRI is technically difficult.

Towards differentiation of brain tumor from radiation necrosis using multi-parametric MRI: Preliminary results at 4.7 T using rodent models

BACKGROUND

It remains a clinical challenge to differentiate brain tumors from radiation-induced necrosis in the brain. Despite significant improvements, no single MRI method has been validated adequately in the clinical setting.

METHODS

Multi-parametric MRI (mpMRI) was performed to differentiate 9L gliosarcoma from radiation necrosis in animal models. Five types of MRI methods probed complementary information on different scales i.e., T2 (relaxation), CEST based APT (probing mobile proteins/peptides) and rNOE (mobile macromolecules), qMT (macromolecules), diffusion based ADC (cell density) and SSIFT iAUC (cell size), and perfusion based DSC (blood volume and flow).

RESULTS

For single MRI parameters, iAUC and ADC provide the best discrimination of radiation necrosis and brain tumor. For mpMRI, a combination of iAUC, ADC, and APT shows the best classification performance based on a two-step analysis with the Lasso and Ridge regressions.

CONCLUSION

A general mpMRI approach is introduced to choosing candidate multiple MRI methods, identifying the most effective parameters from all the mpMRI parameters, and finding the appropriate combination of chosen parameters to maximize the classification performance to differentiate tumors from radiation necrosis.

Cluster Analysis of DSC MRI, Dynamic Contrast-Enhanced MRI, and DWI Parameters Associated with Prognosis in Patients with Glioblastoma after Removal of the Contrast-Enhancing Component: A Preliminary Study

BACKGROUND AND PURPOSE

No report has been published on the use of DSC MR imaging, DCE MR imaging, and DWI parameters in combination to create a prognostic prediction model in glioblastoma patients. The aim of this study was to develop a machine learning-based model to find preoperative multiparametric MR imaging parameters associated with prognosis in patients with glioblastoma. Normalized CBV, volume transfer constant, and ADC of the nonenhancing T2 high-signal-intensity lesions were evaluated using K-means clustering.

MATERIALS AND METHODS

A total of 142 patients with glioblastoma who underwent preoperative MR imaging and total resection were included in this retrospective study. From the normalized CBV, volume transfer constant, and ADC maps, the parametric data were sorted using the K-means clustering method. Patients were divided into training and test sets (ratio, 1:1), and the optimal number of clusters was determined using receiver operating characteristic analysis. Kaplan-Meier survival analysis and log-rank tests were performed to identify potential parametric predictors. A multivariate Cox proportional hazard model was conducted to adjust for clinical predictors.

RESULTS

The nonenhancing T2 high-signal-intensity lesions were divided into 6 clusters. The cluster (class 4) with the relatively low normalized CBV and volume transfer constant value and the lowest ADC values was most associated with predicting glioblastoma prognosis. The optimal cutoff of the class 4 volume fraction of nonenhancing T2 high-signal-intensity lesions predicting 1-year progression-free survival was 9.70%, below which the cutoff was associated with longer progression-free survival. Two Kaplan-Meier curves based on the cutoff value showed a statistically significant difference (P = .037). When we adjusted for all clinical predictors, the cluster with the relatively low normalized CBV and volume transfer constant values and the lowest ADC value was an independent prognostic marker (hazard ratio, 3.04; P = .048). The multivariate Cox proportional hazard model showed a concordance index of 0.699 for progression-free survival.

CONCLUSIONS

Our model showed that nonenhancing T2 high-signal-intensity lesions with the relatively low normalized CBV, low volume transfer constant values, and the lowest ADC values could serve as useful prognostic imaging markers for predicting survival outcomes in patients with glioblastoma.

Diffusion imaging could aid to differentiate between glioma progression and treatment-related abnormalities: a meta-analysis

Background

In a considerable subgroup of glioma patients treated with (chemo) radiation new lesions develop either representing tumor progression (TP) or treatment-related abnormalities (TRA). Quantitative diffusion imaging metrics such as the Apparent Diffusion Coefficient (ADC) and Fractional Anisotropy (FA) have been reported as potential metrics to noninvasively differentiate between these two phenomena. Variability in performance scores of these metrics and absence of a critical overview of the literature contribute to the lack of clinical implementation. This meta-analysis therefore critically reviewed the literature and meta-analyzed the performance scores.

Methods

Systematic searching was carried out in PubMed, EMBASE and The Cochrane Library. Using predefined criteria, papers were reviewed. Diagnostic accuracy values of suitable papers were meta-analyzed quantitatively.

Results

Of 1252 identified papers, 10 ADC papers, totaling 414 patients, and 4 FA papers, with 154 patients were eligible for meta-analysis. Mean ADC values of the patients in the TP/TRA groups were 1.13 × 10⁻³mm²/s (95% CI 0.912 × 10^–3–1.32 × 10⁻³mm²/s) and 1.38 × 10⁻³mm²/s (95% CI 1.33 × 10^–3–1.45 × 10⁻³mm²/s, respectively. Mean FA values of TP/TRA was 0.19 (95% CI 0.189–0.194) and 0.14 (95% CI 0.137–0.143) respectively. A significant mean difference between ADC and FA values in TP versus TRA was observed (p = 0.005).

Conclusions

Quantitative ADC and FA values could be useful for distinguishing TP from TRA on a meta-level. Further studies using serial imaging of individual patients are warranted to determine the role of diffusion imaging in glioma patients.

Role of Molecular Imaging with PET/MR Imaging in the Diagnosis and Management of Brain Tumors

Gliomas are the most common primary brain tumors. Hybrid PET/MR imaging has revolutionized brain tumor imaging, allowing for noninvasive, simultaneous assessment of morphologic, functional, metabolic, and molecular parameters within the brain. Molecular information obtained from PET imaging may aid in the detection, classification, prognostication, and therapeutic decision making for gliomas. ¹⁸F-fluorodeoxyglucose (FDG) has been widely used in the setting of brain tumor imaging, and multiple techniques may be employed to optimize this methodology. More recently, a number of non-¹⁸F-FDG-PET radiotracers have been applied toward brain tumor imaging and are used in clinical practice.

18F-Choline PET Detected the Third Ventricle Craniopharyngioma

ABSTRACT

Craniopharyngiomas are rare tumors of low histological malignancy (World Health Organization grade I) in the sellar and suprasellar region of the brain. We report a case of the third ventricular craniopharyngioma detected using 18F-choline PET/CT in a 43-year-old man. MRI of the brain revealed an intense gadolinium enhancement focus in the same area. Histology revealed characteristics of papillary craniopharyngioma. Our findings suggest that craniopharyngioma should be included in the differential diagnosis of 18F-choline-avid brain lesions.

Using deep learning models to analyze the cerebral edema complication caused by radiotherapy in patients with intracranial tumor

Using deep learning models to analyze patients with intracranial tumors, to study the image segmentation and standard results by clinical depiction complications of cerebral edema after receiving radiotherapy. In this study, patients with intracranial tumors receiving computer knife (CyberKnife M6) stereotactic radiosurgery were followed using the treatment planning system (MultiPlan 5.1.3) to obtain before-treatment and four-month follow-up images of patients. The TensorFlow platform was used as the core architecture for training neural networks. Supervised learning was used to build labels for the cerebral edema dataset by using Mask region-based convolutional neural networks (R-CNN), and region growing algorithms. The three evaluation coefficients DICE, Jaccard (intersection over union, IoU), and volumetric overlap error (VOE) were used to analyze and calculate the algorithms in the image collection for cerebral edema image segmentation and the standard as described by the oncologists. When DICE and IoU indices were 1, and the VOE index was 0, the results were identical to those described by the clinician.The study found using the Mask R-CNN model in the segmentation of cerebral edema, the DICE index was 0.88, the IoU index was 0.79, and the VOE index was 2.0. The DICE, IoU, and VOE indices using region growing were 0.77, 0.64, and 3.2, respectively. Using the evaluated index, the Mask R-CNN model had the best segmentation effect. This method can be implemented in the clinical workflow in the future to achieve good complication segmentation and provide clinical evaluation and guidance suggestions.

A rare brainstem anaplastic astrocytoma

BACKGROUND

Anaplastic astrocytoma (AA) is rarely observed in the brainstem and the clinical symptoms and imaging manifestations vary, which present a great challenge to accurate clinical diagnosis.

CASE DESCRIPTION

A 56-year-old woman, with a month-long history of nausea and vomiting, was first diagnosed with acute cerebral infarction and demyelinating disease. The patient showed negative results on enhanced magnetic resonance and ¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography, and the clinical symptoms were not typical, leading to early misdiagnosis.

CONCLUSION

Finally, the patient was diagnosed with AA by pathological biopsy.

Investigated diagnostic value of synthetic relaxometry, three-dimensional pseudo-continuous arterial spin labelling and diffusion-weighted imaging in the grading of glioma

BACKGROUND

To investigate the performance of synthetic relaxometry, three-dimensional pseudo-continuous arterial spin labelling (pCASL) and diffusion-weighted imaging (DWI) in differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs) and to compare with the conventional MRI.

METHODS

Seventy-two patients with gliomas (including 27 LGGs and 45 HGGs) were studied using synthetic magnetic resonance imaging (sy-MRI), pCASL, and DWI with a 3.0 T MR scanner. T1 relaxometry (T1), T2 relaxometry (T2), as well as proton density (PD) from sy-MRI, cerebral blood flow (CBF) from pCASL, apparent diffusion coefficient (ADC) from DWI and enhancement quality (EQ), proportion enhancing (PE) from conventional contrast enhanced image based Visually-Accessible-Rembrandt-Images (VASARI) scoring system, were all analyzed by two radiologists. The Student's t-test, Mann-Whitney U test or Fisher's exact test was used to compare the parameters between LGGs and HGGs. The diagnostic performance of each parameter and their combination for glioma grading were analyzed.

RESULTS

Significant statistical differences in T1, PD, CBF, ADC, EQ and PE are observed between LGGs and HGGs (all P < 0.001). The ADC values have higher discrimination abilities compared with other univariable parameters, with the AUC of 0.905. AUC values for conventional contrast-enhanced method, EQ and PE from VASARI, and conventional contrast-free method, CBF + ADC, are 0.873 and 0.912 respectively. The combined T1, PD, CBF and ADC model had the best performance for differentiating LGGs and HGGs with AUC, sensitivity and specificity of 0.993, 95.5%, 100%, respectively.

CONCLUSIONS

Relaxometry parameters derived from synthetic MRI contributed to the discrimination of low-grade gliomas from high-grade gliomas. Proposed contrast-free approach combining T1, PD, CBF and ADC showed a strong discriminative power, and outperformed conventional approaches.

Comparison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation

INTRODUCTION AND GOAL TO BACKGROUND

Due to the importance of segmentation of MRI images in identifying brain tumors, various methods including deep learning have been introduced for automatic brain tumor segmentation. On the other hand, using a combination of methods can improve their performance. Among them is the use of wavelet transform as an auxiliary element in deep networks. The analysis of the requirements of such combinations has been addressed in this study.

METHOD

In this developmental study, different wavelet functions were used to compress brain MRI images and finally as an auxiliary element in improving the performance of the convolutional neural network in brain tumor segmentation.

RESULTS

Based on the results of the tests performed, the Daubechies1 function was most effective in enhancing network performance in segmenting MRI images and was able to balance the performance and computational overload.

CONCLUSION

Choosing the wavelet function to optimize the performance of a convolutional neural network should be based on the requirements of the problem, also taking into account some considerations such as computational load, processing time, and performance of the wavelet function in optimizing CNN output in the intended task.

Neuro-Oncology Practice Clinical Debate: FDG PET to differentiate glioblastoma recurrence from treatment-related changes

The ability to accurately differentiate treatment-related changes (ie, pseudoprogression and radiation necrosis) from recurrent glioma remains a critical diagnostic problem in neuro-oncology. Because these entities are treated differently and have vastly different outcomes, accurate diagnosis is necessary to provide optimal patient care. In current practice, this diagnostic quandary commonly requires either serial imaging or histopathologic tissue confirmation. In this article, experts in the field debate the utility of 2-deoxy-2[18F]fluoro-d-glucose positron emission tomography (FDG PET) as an imaging tool to distinguish tumor recurrence from treatment-related changes in a patient with glioblastoma and progressive contrast enhancement on magnetic resonance (MR) following chemoradiotherapy.

Combined amino acid PET-MRI for identifying recurrence in post-treatment gliomas: together we grow

PURPOSE

The aim of this study is to compare the diagnostic accuracy of amino acid PET, MR perfusion and diffusion as stand-alone modalities and in combination in identifying recurrence in post-treatment gliomas and to qualitatively assess spatial concordance between the three modalities using simultaneous PET-MR acquisition.

METHODS

A retrospective review of 48 cases of post-treatment gliomas who underwent simultaneous PET-MRI using C11 methionine as radiotracer was performed. MR perfusion and diffusion sequences were acquired during the PET study. The following parameters were obtained: TBR_max, TBR_mean, SUV_max, and SUV_mean from the PET images; rCBV from perfusion; and ADC_mean and ADC_ratio from the diffusion images. The final diagnosis was based on clinical/imaging follow-up and histopathology when available. ROC curve analysis in combination with logistic regression analysis was used to compare the diagnostic performance. Spatial concordance between modalities was graded as 0, 1, and 2 representing discordance, < 50% and > 50% concordance respectively.

RESULTS

There were 35 cases of recurrence and 13 cases of post-treatment changes without recurrence. The highest area under curve (AUC) was obtained for TBR_max followed by rCBV and ADC_ratio. The AUC increased significantly with a combination of rCBV and TBR_max. Amino acid PET showed the highest diagnostic accuracy and maximum agreement with the final diagnosis. There was discordance between ADC and PET in 22.9%, between rCBV and PET in 16.7% and between PET and contrast enhancement in 14.6% cases.

CONCLUSION

Amino acid PET had the highest diagnostic accuracy in identifying recurrence in post-treatment gliomas. Combination of PET with MRI further increased the AUC thus improving the diagnostic performance.

Noninvasively Evaluating the Grading of Glioma by Multiparametric Magnetic Resonance Imaging

RATIONALE AND OBJECTIVE

To investigate the performance of multi-parametric magnetic resonance imaging (MRI) for glioma grading.

MATERIALS AND METHODS

Seventy consecutive patients with histopathologically confirmed glioma were retrospectively evaluated by conventional MRI, dynamic susceptibility-weighted contrast-enhanced, multiple diffusion-weighted imaging signal models including mono-exponential, bi-exponential, stretched exponential, and diffusion kurtosis imaging. One-way analysis of variance and independent-samples t test were used to compare the MR parameter values between low and high grades as well as among all grades of glioma. Receiver operating characteristic analysis, Spearman's correlation analysis, and binary logistic regression analysis were used to assess their diagnostic performance.

RESULTS

The diagnostic performance (the optimal thresholds, area under the receiver operating characteristic curve, sensitivity, and specificity) was achieved with normalized relative cerebral blood flow (rCBV) (2.240 ml/100 g, 0.844, 87.8%, and 75.9%, respectively), mean kurtosis (MK) (0.471, 0.873, 92.7%, and 79.3%), and water molecular diffusion heterogeneity index (α) (1.064, 0.847, 79.3% and 78.0%) for glioma grading. There were positive correlations between rCBV and MK and the tumor grades and negative correlations between α and the tumor grades (p < 0.01). The parameter of α yielded a diagnostic accuracy of 85.3%, the combination of MK and α yielded a diagnostic accuracy of 89.7%, while the combination of rCBV, MK, and α were more accurate (94.2%) in predicting tumor grade.

CONCLUSION

The most accurate parameters were rCBV, MK, and α in dynamic susceptibility-weighted contrast, diffusion kurtosis imaging, and Multi-b diffusion-weighted imaging for glioma grading, respectively. Multiparametric MRI can increase the accuracy of glioma grading.

The role of 11C-methionine PET in patients with negative diffusion-weighted magnetic resonance imaging: correlation with histology and molecular biomarkers in operated gliomas

OBJECTIVE

To compare 11C-methionine (11C-METH) PET with diffusion-weighted MRI (DWI-MRI) diagnostic accuracy and prognostic value in patients with glioma candidate to neurosurgery.

METHODS

We collected and analyzed data from 124 consecutive patients (n = 124) investigated during preoperative work-up. Both visual and semiquantitative parameters were utilized for image analysis. The reference standard was based on histopathology. The median follow-up was 14.3 months.

RESULTS

Overall, 47 high-grade gliomas (HGG) and 77 low-grade gliomas (LGG) were diagnosed. On visual assessment, sensitivity and specificity for differentiating HGG from LGG were 80.8 and 59.7% for DWI-MRI, versus 95.7 and 41.5% for 11C-METH PET, respectively. On semiquantitative analysis, the sensitivity, specificity, and area under the curve were 78.7, 71.4, and 80.4% for SUVmax, 78.7, 70.1, and 81.1% for SUVratio, and 74.5, 61, and 76.7% for MTB (metabolic tumor burden), respectively. In patients with negative DWI-MRI and IDH-wild type, SUVmax and SUVratio were higher compared to IDH-mutated (P = 0.025 and P = 0.01, respectively). In LGG, patients with 1p/19q codeletion showed higher SUVmax (P = 0.044). In all patients with negative DWI-MRI, median PFS was longer for SUVmax <3.9 (median not reached vs 34.2 months, P = 0.004), SUVratio <2.3 (median not reached vs 21.5 months, P < 0.001), and MTB <3.1 (median not reached vs 45.7 months, P = 0.05). In LGG patients with negative DWI-MRI, only SUVratio <2.3 and MTB <3.1 were associated with longer PFS (P = 0.016 and P = 0.024, respectively).

CONCLUSION

C-METH PET was found highly sensitive for glioma differentiation and molecular characterization. In DWI-negative patients, PET parameters correlated with molecular profile were associated with clinical outcome.

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas

OBJECTIVES

The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas.

METHODS

Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters.

RESULTS

Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10-3 mm2/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92.

CONCLUSION

The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

The use of hyperpolarised 13C-MRI in clinical body imaging to probe cancer metabolism

Metabolic reprogramming is one of the hallmarks of cancer and includes the Warburg effect, which is exhibited by many tumours. This can be exploited by positron emission tomography (PET) as part of routine clinical cancer imaging. However, an emerging and alternative method to detect altered metabolism is carbon-13 magnetic resonance imaging (MRI) following injection of hyperpolarised [1-13C]pyruvate. The technique increases the signal-to-noise ratio for the detection of hyperpolarised 13C-labelled metabolites by several orders of magnitude and facilitates the dynamic, noninvasive imaging of the exchange of 13C-pyruvate to 13C-lactate over time. The method has produced promising preclinical results in the area of oncology and is currently being explored in human imaging studies. The first translational studies have demonstrated the safety and feasibility of the technique in patients with prostate, renal, breast and pancreatic cancer, as well as revealing a successful response to treatment in breast and prostate cancer patients at an earlier stage than multiparametric MRI. This review will focus on the strengths of the technique and its applications in the area of oncological body MRI including noninvasive characterisation of disease aggressiveness, mapping of tumour heterogeneity, and early response assessment. A comparison of hyperpolarised 13C-MRI with state-of-the-art multiparametric MRI is likely to reveal the unique additional information and applications offered by the technique.

PET and SPECT Imaging of Brain Tumors

Neuroimaging plays a vital role in the diagnosis and post-treatment assessment of brain tumors, aiding in treatment optimization, prognostication, and patient management. New clinical treatments have resulted in increased complexity of imaging interpretation, thus integrating complementary information from multiple imaging modalities (computed tomography, magnetic resonance imaging, and nuclear medicine) contributes to a thorough and more accurate evaluation. In review, we discuss current strategies of brain tumor imaging, specifically detailing the role of nuclear medicine single-photon emission computed tomography and positron emission tomography with utilization of both common and uncommon radiotracers in tumor grading, diagnosis, and treatment response.

Surveillance imaging in pediatric ependymoma

BACKGROUND

Management of pediatric patients with ependymoma includes posttreatment surveillance imaging to identify asymptomatic recurrences. However, it is unclear whether early detection translates into improved survival. The objective was to determine whether detection of ependymoma relapses on surveillance imaging translates into a survival benefit.

PROCEDURE

Patients with ependymoma aged <21 years at diagnosis treated in the Nemours' Children's Health System between January 2003 and October 2016 underwent chart review. Relapsed patients' charts were assessed for details of initial therapy, surveillance imaging regimen, details of relapse including detection and therapy, and outcome. Median follow up of the entire cohort was 6.5 years from diagnosis and 3.5 years from relapse.

RESULTS

Ninety of 198 (45%) patients experienced relapse with 61 (68%) detected by surveillance imaging and 29 (32%) detected based on symptoms. Five-year OS in the surveillance group was 67% (confidence interval [CI] 55-82%, SE 0.1) versus 51% (CI 35-73%, SE 0.19) in the symptoms group (P = .073). From relapse, the 3-year OS in the surveillance group was 62% (CI 50-78%, SE 0.11) versus 55% (CI 39-76%, SE 0.17) in the symptoms group (P = .063) and the 3-year SPFS was 45% (CI 33-61%, SE 0.16) in the surveillance group versus 32% (CI 19-55%, SE 0.27) in the symptoms group (P = .028).

CONCLUSION

Surveillance imaging may identify recurrences in patients when they are more amenable to salvage therapy, resulting in superior 3-year SPFS, but given limited salvage options for children with recurrent ependymoma, the survival advantage of frequent surveillance imaging in asymptomatic patients remains ambiguous.

Correlation of multimodal 18F-DOPA PET and conventional MRI with treatment response and survival in children with diffuse intrinsic pontine gliomas

To evaluate the contribution of ¹⁸F-dihydroxyphenylalanine (DOPA) PET in association with conventional MRI in predicting treatment response and survival outcome of pediatric patients with diffuse intrinsic pontine gliomas (DIPGs).

Methods: We retrospectively analyzed 19 children with newly diagnosed DIPGs who underwent ¹⁸F-DOPA PET/CT and conventional MRI within one week of each other at admission and subsequent MRI follow-up. Following co-registration and fusion of PET and MRI, ¹⁸F-DOPA uptake avidity and extent (PET tumor volume and uniformity) at admission, along with MRI indices including presence of ring contrast-enhancement, tumor volume at admission and at maximum response following first-line treatment, were evaluated and correlated with overall survival (OS). The association between ¹⁸F-DOPA uptake tumor volume at admission and MRI tumor volume following treatment was evaluated. Statistics included Wilcoxon signed-rank and Mann-Whitney U tests, Kaplan-Meier OS curve and Cox analysis.

Results: DIPGs with a ¹⁸F-DOPA uptake Tumor/Striatum (T/S) ratio >1 presented an OS ≤ 12 months and lower degree of tumor volume reduction following treatment (p = 0.001). On multivariate analysis, T/S (p = 0.001), ring enhancement (p = 0.01) and the degree of MRI tumor volume reduction (p = 0.01) independently correlated with OS. In all patients, areas of increased ¹⁸F-DOPA uptake overlapped with regions demonstrating more prominent residual components/lack of response following treatment.

Conclusions:¹⁸F-DOPA PET provides useful information for evaluating the metabolism of DIPGs. T/S ratio is an independent predictor of outcome. ¹⁸F-DOPA uptake extent delineates tumoral regions with a more aggressive biological behaviour, less sensitive to first line treatment.

Selective, high-contrast detection of syngeneic glioblastoma in vivo

Glioblastoma is a highly malignant, largely therapy-resistant brain tumour. Deep infiltration of brain tissue by neoplastic cells represents the key problem of diffuse glioma. Much current research focuses on the molecular makeup of the visible tumour mass rather than the cellular interactions in the surrounding brain tissue infiltrated by the invasive glioma cells that cause the tumour’s ultimately lethal outcome. Diagnostic neuroimaging that enables the direct in vivo observation of the tumour infiltration zone and the local host tissue responses at a preclinical stage are important for the development of more effective glioma treatments. Here, we report an animal model that allows high-contrast imaging of wild-type glioma cells by positron emission tomography (PET) using [18 F]PBR111, a selective radioligand for the mitochondrial 18 kDa Translocator Protein (TSPO), in the Tspo^−/− mouse strain (C57BL/6-Tspo^{tm1GuMu(GuwiyangWurra)}). The high selectivity of [18 F]PBR111 for the TSPO combined with the exclusive expression of TSPO in glioma cells infiltrating into null-background host tissue free of any TSPO expression, makes it possible, for the first time, to unequivocally and with uniquely high biological contrast identify peri-tumoral glioma cell invasion at preclinical stages in vivo. Comparison of the in vivo imaging signal from wild-type glioma cells in a null background with the signal in a wild-type host tissue, where the tumour induces the expected TSPO expression in the host’s glial cells, illustrates the substantial extent of the peritumoral host response to the growing tumour. The syngeneic tumour (TSPO^+/+) in null background (TSPO^−/−) model is thus well suited to study the interaction of the tumour front with the peri-tumoral tissue, and the experimental evaluation of new therapeutic approaches targeting the invasive behaviour of glioblastoma.

An ultrasound based platform for image-guided radiotherapy in canine bladder cancer patients

Background and purpose

Ultrasound (US) is a non-invasive, non-radiographic imaging technique with high spatial and temporal resolution that can be used for localizing soft-tissue structures and tumors in real-time during radiotherapy (RT) (inter- and intra-fraction). A comprehensive approach incorporating an in-house 3D-US system within RT is presented. This system is easier to adopt into existing treatment protocols than current US based systems, with the aim of providing millimeter intra-fraction alignment errors and sensitivity to track intra-fraction bladder movement.

Materials and methods

An in-house integrated US manipulator and platform was designed to relate the computed tomographic (CT) scanner, 3D-US and linear accelerator coordinate systems. An agar-based phantom with measured speed of sound and densities consistent with tissues surrounding the bladder was rotated (0–45°) and translated (up to 55 mm) relative to the US and CT coordinate systems to validate this device. After acquiring and integrating CT and US images into the treatment planning system, US-to-US and US-to-CT images were co-registered to re-align the phantom relative to the linear accelerator.

Results

Statistical errors from US-to-US registrations for various patient orientations ranged from 0.1 to 1.7 mm for x, y, and z translation components, and 0.0–1.1° for rotational components. Statistical errors from US-to-CT registrations were 0.3–1.2 mm for the x, y and z translational components and 0.1–2.5° for the rotational components.

Conclusions

An ultrasound-based platform was designed, constructed and tested on a CT/US tissue-equivalent phantom to track bladder displacement with a statistical uncertainty to correct and track inter- and intra-fractional displacements of the bladder during radiation treatments.

Advanced MR imaging and 18F-DOPA PET characteristics of H3K27M-mutant and wild-type pediatric diffuse midline gliomas

PURPOSE

The aim of this study was to investigate MRI-derived diffusion weighted imaging (DWI), ¹H-MR spectroscopy (¹H-MRS) and arterial spin labeling (ASL) perfusion imaging in comparison with ¹⁸F-dihydroxyphenylalanine (DOPA) PET with respect to diagnostic evaluation of pediatric diffuse midline gliomas (DMG) H3K27M-mutant and wild-type.

METHODS

We retrospectively analyzed 22 pediatric patients with DMG histologically proved and molecularly classified as H3K27M-mutant (12 subjects) and wild-type (10 subjects) who underwent DWI, ¹H-MRS, and ASL performed within 2 weeks of ¹⁸F-DOPA PET. DWI-derived relative minimum apparent diffusion coefficient (rADC min), ¹H-MRS data [choline/N-acetylaspartate (Cho/NAA), choline/creatine (Cho/Cr), and presence of lactate] and relative ASL-derived cerebral blood flow max (rCBF max) were compared with ¹⁸F-DOPA uptake Tumor/Normal tissue (T/N) and Tumor/Striatum (T/S) ratios, and correlated with histological and molecular features of DMG. Statistics included Pearson's chi-square and Mann-Whitney U tests, Spearman's rank correlation and receiver operating characteristic (ROC) analysis.

RESULTS

The highest degrees of correlation among different techniques were found between T/S, rADC min and Cho/NAA ratio (p < 0.01), and between rCBF max and rADC min (p < 0.01). Significant differences between histologically classified low- and high-grade DMG, independently of H3K27M-mutation, were found among all imaging techniques (p ≤ 0.02). Significant differences in terms of rCBF max, rADC min, Cho/NAA and ¹⁸F-DOPA uptake were also found between molecularly classified mutant and wild-type DMG (p ≤ 0.02), even though wild-type DMG included low-grade astrocytomas, not present among mutant DMG. When comparing only histologically defined high-grade mutant and wild-type DMG, only the ¹⁸F-DOPA PET data T/S demonstrated statistically significant differences independently of histology (p < 0.003). ROC analysis demonstrated that T/S ratio was the best parameter for differentiating mutant from wild-type DMG (AUC 0.94, p < 0.001).

CONCLUSIONS

Advanced MRI and ¹⁸F-DOPA PET characteristics of DMG depend on histological features; however, ¹⁸F-DOPA PET-T/S was the only parameter able to discriminate H3K27M-mutant from wild-type DMG independently of histology.

Tc-99m Glucoheptonate Single Photon Emission Computed Tomography-Computed Tomography for Detection of Recurrent Glioma: A Prospective Comparison with N-13 Ammonia Positron Emission Tomography-Computed Tomography

Purpose of the Study:

To assess the efficacies of Tc-99m glucoheptonate single photon emission computed tomography-computed tomography (Tc-99m GHA SPECT-CT) and N-13 ammonia positron emission tomography-computed tomography (N-13 NH₃ PET-CT) in detecting recurrent glioma.

Materials and Methods:

Fifty-five consecutive, histologically proven, and previously treated glioma patients (age, 38.9 ± 12.2 years; 61.8% males) presenting with clinical suspicion of recurrence were evaluated with Tc-99m GHA SPECT-CT and N-13 NH₃ PET-CT. Images were evaluated both qualitatively and semiquantitatively. A combination of clinicoradiological follow-up, repeat imaging, and/or biopsy (when available) was considered as the reference standard.

Results:

Based on the reference standard, 28/55 (50.9%) patients had recurrence. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy of Tc-99m GHA SPECT-CT, and N-13 NH₃ PET-CT were 85.7%, 85.2%, 85.7%, 85.2%, 85.5% and 78.6%, 88.9%, 88.0%, 80.0%, 83.6%, respectively (concordant findings in 46 patients). The performances of the two modalities were equivalent both in overall and subgroup McNemar analyses (P = 0.508, overall; P = 0.687, low grade; P = 1.000, high grade).

Conclusion:

Tc-99m GHA SPECT-CT is an alternative imaging modality equally efficacious as N-13 NH₃ PET-CT in detecting recurrent glioma.

Amino acid tracers in PET imaging of diffuse low-grade gliomas: a systematic review of preoperative applications

Positron emission tomography (PET) imaging using amino acid tracers has in recent years become widely used in the diagnosis and prediction of disease course in diffuse low-grade gliomas (LGG). However, implications of preoperative PET for treatment and prognosis in this patient group have not been systematically studied. The aim of this systematic review was to evaluate the preoperative diagnostic and prognostic value of amino acid PET in suspected diffuse LGG. Medline, Cochrane Library, and Embase databases were systematically searched using keywords "PET," "low-grade glioma," and "amino acids tracers" with their respective synonyms. Out of 2137 eligible studies, 28 met the inclusion criteria. Increased amino acid uptake (lesion/brain) was consistently reported among included studies; in 25-92% of subsequently histopathology-verified LGG, in 83-100% of histopathology-verified HGG, and also in some non-neoplastic lesions. No consistent results were found in studies reporting hot spot areas on PET in MRI-suspected LGG. Thus, the diagnostic value of amino acid PET imaging in suspected LGG has proven difficult to interpret, showing clear overlap and inconsistencies among reported results. Similarly, the results regarding the prognostic value of PET in suspected LGG and the correlation between uptake ratios and the molecular tumor status of LGG were conflicting. This systematic review illustrates the difficulties with prognostic studies presenting data on group-level without adjustment for established clinical prognostic factors, leading to a loss of additional prognostic information. We conclude that the prognostic value of PET is limited to analysis of histological subgroups of LGG and is probably strongest when using kinetic analysis of dynamic FET uptake parameters.

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm

Purpose:

Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform.

Materials and methods:

In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation.

Results:

Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks.

Conclusion:

Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

Multimodal Imaging of Patients With Gliomas Confirms 11C-MET PET as a Complementary Marker to MRI for Noninvasive Tumor Grading and Intraindividual Follow-Up After Therapy

The value of combined L-( methyl-[¹¹C]) methionine positron-emitting tomography (MET-PET) and magnetic resonance imaging (MRI) with regard to tumor extent, entity prediction, and therapy effects in clinical routine in patients with suspicion of a brain tumor was investigated. In n = 65 patients with histologically verified brain lesions n = 70 MET-PET and MRI (T1-weighted gadolinium-enhanced [T1w-Gd] and fluid-attenuated inversion recovery or T2-weighted [FLAIR/T2w]) examinations were performed. The computer software "visualization and analysis framework volume rendering engine (Voreen)" was used for analysis of extent and intersection of tumor compartments. Binary logistic regression models were developed to differentiate between World Health Organization (WHO) tumor types/grades. Tumor sizes as defined by thresholding based on tumor-to-background ratios were significantly different as determined by MET-PET (21.6 ± 36.8 cm³), T1w-Gd-MRI (3.9 ± 7.8 cm³), and FLAIR/T2-MRI (64.8 ± 60.4 cm³; P < .001). The MET-PET visualized tumor activity where MRI parameters were negative: PET positive tumor volume without Gd enhancement was 19.8 ± 35.0 cm³ and without changes in FLAIR/T2 10.3 ± 25.7 cm³. FLAIR/T2-MRI visualized greatest tumor extent with differences to MET-PET being greater in posttherapy (64.6 ± 62.7 cm³) than in newly diagnosed patients (20.5 ± 52.6 cm³). The binary logistic regression model differentiated between WHO tumor types (fibrillary astrocytoma II n = 10 from other gliomas n = 16) with an accuracy of 80.8% in patients at primary diagnosis. Combined PET and MRI improve the evaluation of tumor activity, extent, type/grade prediction, and therapy-induced changes in patients with glioma and serve information highly relevant for diagnosis and management.

Contribution of susceptibility- and diffusion-weighted magnetic resonance imaging for grading gliomas

The aim of the present study was to assess the value of susceptibility-weighted imaging (SWI) and diffusion-weighted imaging (DWI) in the grading of gliomas and to evaluate the correlation between these quantitative parameters derived from SWI and DWI. A total of 49 patients with glioma were assessed by DWI and SWI. The evaluation included the ratio of apparent diffuse coefficient values between the solid portion of tumors and contralateral normal white matter (rADC) and the degree of intratumoral susceptibility signal intensity (ITSS) within tumors. Receiver operating characteristic curve (ROC) analyses were performed and the area under the ROC curve was calculated to compare the diagnostic performance, determine optimum thresholds for tumor grading, and calculate the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for identifying high-grade gliomas. The correlation between DWI- and SWI-derived parameters was also evaluated. The rADC and the degrees of ITSS within tumors were significantly higher in high-grade gliomas than those in low-grade gliomas. ROC curve analysis indicated that the rADC was a better index for grading gliomas than the ITSS degree. Statistical analysis demonstrated a threshold value of 1.497 for rADC to provide a sensitivity, specificity, PPV and NPV of 86.2, 85.0, 89.3 and 81.0%, respectively, for determining high-grade gliomas. A degree of ITSS of 1.5 was defined as the threshold to identify high-grade gliomas and sensitivity, specificity, PPV and NPV of 82.8, 75.0, 82.8 and 75.0% were obtained, respectively. Furthermore, a moderate inverse correlation between rADC and the ITSS degree was revealed. Combination of SWI with DWI may provide valuable information for glioma grading.

Folic acid-cysteamine modified gold nanoparticle as a nanoprobe for targeted computed tomography imaging of cancer cells

Development of various cost-effective multifunctional nanoprobes for efficient targeted molecular imaging of tumors remains a great challenge in medicine. Herein, we report a simple method of forming folic acid-targeted multifunctional gold nanoparticles via cost-effective cysteamine as a template for tumor molecular computed tomography (CT) imaging technique. The formed multifunctional cysteamine-folic acid conjugated gold nanoparticles (FA-Cys-AuNPs) were characterized via different techniques. Colony assay, hematoxylin and eosin (H&E), MTT, and flow cytometry analysis were used to evaluate the cytocompatibility of the particles. We showed that the formed FA-Cys-AuNPs with an Au core size of ~15 nm are non-cytotoxic in a given concentration range and revealed greater X-ray attenuation intensity than iodine-based contrast agent under the same concentration of the active element. At 80 kVp, FA-Cys-AuNPs enable 1.77-times greater contrast per unit mass compared with iodine at a concentration of 2000 μg/ml, and importantly, the developed FA-Cys-AuNPs can be used as a contrast media for targeted CT imaging of folic acid receptor-expressing cancer cells in vitro. CT values of the targeted cells were 2-times higher than that of non-targeted cells at 80 kVp. These findings propose that the designed FA-Cys-AuNPs can be used as a promising contrast agent for molecular CT imaging. This data can be also considered for the application of gold nanostructures in radiation dose enhancement where nanoparticles with high X-ray attenuation are applied.

Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging.

Magnetic resonance perfusion for differentiating low-grade from high-grade gliomas at first presentation

BACKGROUND

Gliomas are the most common primary brain tumour. They are graded using the WHO classification system, with Grade II-IV astrocytomas, oligodendrogliomas and oligoastrocytomas. Low-grade gliomas (LGGs) are WHO Grade II infiltrative brain tumours that typically appear solid and non-enhancing on magnetic resonance imaging (MRI) scans. People with LGG often have little or no neurologic deficit, so may opt for a watch-and-wait-approach over surgical resection, radiotherapy or both, as surgery can result in early neurologic disability. Occasionally, high-grade gliomas (HGGs, WHO Grade III and IV) may have the same MRI appearance as LGGs. Taking a watch-and-wait approach could be detrimental for the patient if the tumour progresses quickly. Advanced imaging techniques are increasingly used in clinical practice to predict the grade of the tumour and to aid clinical decision of when to intervene surgically. One such advanced imaging technique is magnetic resonance (MR) perfusion, which detects abnormal haemodynamic changes related to increased angiogenesis and vascular permeability, or "leakiness" that occur with aggressive tumour histology. These are reflected by changes in cerebral blood volume (CBV) expressed as rCBV (ratio of tumoural CBV to normal appearing white matter CBV) and permeability, measured by K^trans.

OBJECTIVES

To determine the diagnostic test accuracy of MR perfusion for identifying patients with primary solid and non-enhancing LGGs (WHO Grade II) at first presentation in children and adults. In performing the quantitative analysis for this review, patients with LGGs were considered disease positive while patients with HGGs were considered disease negative.To determine what clinical features and methodological features affect the accuracy of MR perfusion.

SEARCH METHODS

Our search strategy used two concepts: (1) glioma and the various histologies of interest, and (2) MR perfusion. We used structured search strategies appropriate for each database searched, which included: MEDLINE (Ovid SP), Embase (Ovid SP), and Web of Science Core Collection (Science Citation Index Expanded and Conference Proceedings Citation Index). The most recent search for this review was run on 9 November 2016.We also identified 'grey literature' from online records of conference proceedings from the American College of Radiology, European Society of Radiology, American Society of Neuroradiology and European Society of Neuroradiology in the last 20 years.

SELECTION CRITERIA

The titles and abstracts from the search results were screened to obtain full-text articles for inclusion or exclusion. We contacted authors to clarify or obtain missing/unpublished data.We included cross-sectional studies that performed dynamic susceptibility (DSC) or dynamic contrast-enhanced (DCE) MR perfusion or both of untreated LGGs and HGGs, and where rCBV and/or K^trans values were reported. We selected participants with solid and non-enhancing gliomas who underwent MR perfusion within two months prior to histological confirmation. We excluded studies on participants who received radiation or chemotherapy before MR perfusion, or those without histologic confirmation.

DATA COLLECTION AND ANALYSIS

Two review authors extracted information on study characteristics and data, and assessed the methodological quality using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We present a summary of the study characteristics and QUADAS-2 results, and rate studies as good quality when they have low risk of bias in the domains of reference standard of tissue diagnosis and flow and timing between MR perfusion and tissue diagnosis.In the quantitative analysis, LGGs were considered disease positive, while HGGs were disease negative. The sensitivity refers to the proportion of LGGs detected by MR perfusion, and specificity as the proportion of detected HGGs. We constructed two-by-two tables with true positives and false negatives as the number of correctly and incorrectly diagnosed LGG, respectively, while true negatives and false positives are the number of correctly and incorrectly diagnosed HGG, respectively.Meta-analysis was performed on studies with two-by-two tables, with further sensitivity analysis using good quality studies. Limited data precluded regression analysis to explore heterogeneity but subgroup analysis was performed on tumour histology groups.

MAIN RESULTS

Seven studies with small sample sizes (4 to 48) met our inclusion criteria. These were mostly conducted in university hospitals and mostly recruited adult patients. All studies performed DSC MR perfusion and described heterogeneous acquisition and post-processing methods. Only one study performed DCE MR perfusion, precluding quantitative analysis.Using patient-level data allowed selection of individual participants relevant to the review, with generally low risks of bias for the participant selection, reference standard and flow and timing domains. Most studies did not use a pre-specified threshold, which was considered a significant source of bias, however this did not affect quantitative analysis as we adopted a common rCBV threshold of 1.75 for the review. Concerns regarding applicability were low.From published and unpublished data, 115 participants were selected and included in the meta-analysis. Average rCBV (range) of 83 LGGs and 32 HGGs were 1.29 (0.01 to 5.10) and 1.89 (0.30 to 6.51), respectively. Using the widely accepted rCBV threshold of <1.75 to differentiate LGG from HGG, the summary sensitivity/specificity estimates were 0.83 (95% CI 0.66 to 0.93)/0.48 (95% CI 0.09 to 0.90). Sensitivity analysis using five good quality studies yielded sensitivity/specificity of 0.80 (95% CI 0.61 to 0.91)/0.67 (95% CI 0.07 to 0.98). Subgroup analysis for tumour histology showed sensitivity/specificity of 0.92 (95% CI 0.55 to 0.99)/0.42 (95% CI 0.02 to 0.95) in astrocytomas (6 studies, 55 participants) and 0.77 (95% CI 0.46 to 0.93)/0.53 (95% CI 0.14 to 0.88) in oligodendrogliomas+oligoastrocytomas (6 studies, 56 participants). Data were too sparse to investigate any differences across subgroups.

AUTHORS' CONCLUSIONS

The limited available evidence precludes reliable estimation of the performance of DSC MR perfusion-derived rCBV for the identification of grade in untreated solid and non-enhancing LGG from that of HGG. Pooled data yielded a wide range of estimates for both sensitivity (range 66% to 93% for detection of LGGs) and specificity (range 9% to 90% for detection of HGGs). Other clinical and methodological features affecting accuracy of the technique could not be determined from the limited data. A larger sample size of both LGG and HGG, preferably using a standardised scanning approach and with an updated reference standard incorporating molecular profiles, is required for a definite conclusion.

Bioluminescence Tomography Based on Gaussian Weighted Laplace Prior Regularization for In Vivo Morphological Imaging of Glioma

Bioluminescence tomography (BLT) is a powerful non-invasive molecular imaging tool for in vivo studies of glioma in mice. However, because of the light scattering and resulted ill-posed problems, it is challenging to develop a sufficient reconstruction method, which can accurately locate the tumor and define the tumor morphology in three-dimension. In this paper, we proposed a novel Gaussian weighted Laplace prior (GWLP) regularization method. It considered the variance of the bioluminescence energy between any two voxels inside an organ had a non-linear inverse relationship with their Gaussian distance to solve the over-smoothed tumor morphology in BLT reconstruction. We compared the GWLP with conventional Tikhonov and Laplace regularization methods through various numerical simulations and in vivo orthotopic glioma mouse model experiments. The in vivo magnetic resonance imaging and ex vivo green fluorescent protein images and hematoxylin-eosin stained images of whole head cryoslicing specimens were utilized as gold standards. The results demonstrated that GWLP achieved the highest accuracy in tumor localization and tumor morphology preservation. To the best of our knowledge, this is the first study that achieved such accurate BLT morphological reconstruction of orthotopic glioma without using any segmented tumor structure from any other structural imaging modalities as the prior for reconstruction guidance. This enabled BLT more suitable and practical for in vivo imaging of orthotopic glioma mouse models.

[18F] FDOPA standardized uptake values of brain tumors are not exclusively dependent on LAT1 expression

[18F]-FDOPA is a labeled amino acid (AA) analog used for positron emission tomography (PET) which is gaining increasing interest in the evaluation of brain tumors (BT). The AA-transporter LAT1 has been shown to be involved in [18F]-FDOPA uptake. The aim of this study was to determine whether the [18F]-FDOPA uptake was correlated with level of LAT1 expression in BT. Twenty-eight BT (including 19 gliomas and 9 metastases) were investigated by [18F]-FDOPA-PET prior to surgery and by anti-LAT1 immunohistochemistry on surgical specimens. The quantitative [18F]-FDOPA measured parameters were SUVmax, SUVmean and SUVpeak. LAT1 expression was quantified using a score (0 to 400). A significant [18F]-FDOPA uptake was associated with a LAT1 score ≥ 100 (p = 0.02) but there was no linear correlation between intensity of [18F]-FDOPA uptake and score of LAT1 expression whatever the parameters considered. LAT1 expression alone is not sufficient to explain variation of intensity of [18F]-FDOPA uptake in BT.

Grading and outcome prediction of pediatric diffuse astrocytic tumors with diffusion and arterial spin labeling perfusion MRI in comparison with 18F-DOPA PET

PURPOSE

The aim of this study was to investigate MRI-derived diffusion weighted imaging (DWI) and arterial spin labeling (ASL) perfusion imaging in comparison with ¹⁸F-dihydroxyphenylalanine (DOPA) PET with respect to diagnostic performance in tumor grading and outcome prediction in pediatric patients with diffuse astrocytic tumors (DAT).

METHODS

We retrospectively analyzed 26 children with histologically proven treatment naïve low and high grade DAT who underwent ASL and DWI performed within 2 weeks of ¹⁸F-DOPA PET. Relative ASL-derived cerebral blood flow max (rCBF max) and DWI-derived minimum apparent diffusion coefficient (rADC min) were compared with ¹⁸F-DOPA uptake tumor/normal tissue (T/N) and tumor/striatum (T/S) ratios, and correlated with World Health Organization (WHO) tumor grade and progression-free survival (PFS). Statistics included Pearson's chi-square and Mann-Whitney U tests, Spearman's rank correlation, receiver operating characteristic (ROC) analysis, discriminant function analysis (DFA), Kaplan-Meier survival curve, and Cox analysis.

RESULTS

A significant correlation was demonstrated between rCBF max, rADC min, and ¹⁸F-DOPA PET data (p < 0.001). Significant differences in terms of rCBF max, rADC min, and ¹⁸F-DOPA uptake were found between low- and high-grade DAT (p ≤ 0.001). ROC analysis and DFA demonstrated that T/S and T/N values were the best parameters for predicting tumor progression (AUC 0.93, p < 0.001). On univariate analysis, all diagnostic tools correlated with PFS (p ≤ 0.001); however, on multivariate analysis, only ¹⁸F-DOPA uptake remained significantly associated with outcome (p ≤ 0.03), while a trend emerged for rCBF max (p = 0.09) and rADC min (p = 0.08). The combination of MRI and PET data increased the predictive power for prognosticating tumor progression (AUC 0.97, p < 0.001).

CONCLUSIONS

DWI, ASL and ¹⁸F-DOPA PET provide useful complementary information for pediatric DAT grading. ¹⁸F-DOPA uptake better correlates with PFS prediction. Combining MRI and PET data provides the highest predictive power for prognosticating tumor progression suggesting a synergistic role of these diagnostic tools.

Multiparametric Evaluation in Differentiating Glioma Recurrence from Treatment-Induced Necrosis Using Simultaneous 18F-FDG-PET/MRI: A Single-Institution Retrospective Study

BACKGROUND AND PURPOSE

Differentiating glioma recurrence from treatment-induced necrosis can be a challenge on conventional imaging. This study aimed to assess the diagnostic performance of each functional MR imaging and PET parameter derived by using simultaneous FDG-PET/MR imaging individually and in combination in the evaluation of suspected glioma recurrence.

MATERIALS AND METHODS

Thirty-five treated glioma patients with 41 enhancing lesions (World Health Organization grade II = 9, III = 13, IV = 19) on MR imaging after an operation followed by radiation therapy and/or chemotherapy formed part of this study. Using PET/MR imaging, we calculated the normalized mean relative CBV, mean ADC, Cho/Cr, and maximum and mean target-to-background ratios. Statistical analysis was performed to determine the diagnostic performance of each parameter by receiver operating characteristic analysis individually and in combination with multivariate receiver operating characteristic analysis for the detection of glioma recurrence. Histopathology or clinicoradiologic follow-up was considered the criterion standard.

RESULTS

Of 35 patients, 25 (30 lesions) were classified as having a recurrence and 10 (11 lesions) patients as having treatment-induced necrosis. Parameters like rCBV_mean (mean relative CBV), ADC_mean, Cho/Cr, and maximum and mean target-to-background ratios were statistically significant in the detection of recurrent lesions with an accuracy of 77.5%, 78.0%, 90.9%, 87.8%, and 87.8%, respectively. On multivariate receiver operating characteristic analysis, the combination of all 3 MR imaging parameters resulted in an area under the curve of 0.913 ± 0.053. Furthermore, an area under the curve of 0.935 ± 0.046 was obtained when MR imaging parameters (ADC_mean and Cho/Cr) were combined with the PET parameter (mean target-to-background ratio), demonstrating an increase in diagnostic accuracy.

CONCLUSIONS

Simultaneous PET/MR imaging with FDG offers correlative and synergistic multiparametric assessment of glioma recurrence with increased accuracy and clinical utility.

Diapeutic cancer-targeting alkylphosphocholine analogs may advance management of brain malignancies

The following is a special report on alkylphosphocholine analogs as targeted imaging and therapy agents for cancer, and their potential role in diagnosis and treatment in glioblastoma and brain metastases. These novel cancer-targeting agents display impressive tumor avidity with low background in the normal brain, and multimodal diagnostic imaging and therapy capabilities. The use of these agents may significantly improve diagnosis, treatment and post-treatment follow-up in patients with brain malignancies.

Development of a transplantable glioma tumour model from genetically engineered mice: MRI/MRS/MRSI characterisation

The initial aim of this study was to generate a transplantable glial tumour model of low-intermediate grade by disaggregation of a spontaneous tumour mass from genetically engineered models (GEM). This should result in an increased tumour incidence in comparison to GEM animals. An anaplastic oligoastrocytoma (OA) tumour of World Health Organization (WHO) grade III was obtained from a female GEM mouse with the S100β-v-erbB/inK4a-Arf (+/-) genotype maintained in the C57BL/6 background. The tumour tissue was disaggregated; tumour cells from it were grown in aggregates and stereotactically injected into C57BL/6 mice. Tumour development was followed using Magnetic Resonance Imaging (MRI), while changes in the metabolomics pattern of the masses were evaluated by Magnetic Resonance Spectroscopy/Spectroscopic Imaging (MRS/MRSI). Final tumour grade was evaluated by histopathological analysis. The total number of tumours generated from GEM cells from disaggregated tumour (CDT) was 67 with up to 100 % penetrance, as compared to 16 % in the local GEM model, with an average survival time of 66 ± 55 days, up to 4.3-fold significantly higher than the standard GL261 glioblastoma (GBM) tumour model. Tumours produced by transplantation of cells freshly obtained from disaggregated GEM tumour were diagnosed as WHO grade III anaplastic oligodendroglioma (ODG) and OA, while tumours produced from a previously frozen sample were diagnosed as WHO grade IV GBM. We successfully grew CDT and generated tumours from a grade III GEM glial tumour. Freezing and cell culture protocols produced progression to grade IV GBM, which makes the developed transplantable model qualify as potential secondary GBM model in mice.

Proton MRS imaging in pediatric brain tumors

Magnetic resonance (MR) techniques offer a noninvasive, non-irradiating yet sensitive approach to diagnosing and monitoring pediatric brain tumors. Proton MR spectroscopy (MRS), as an adjunct to MRI, is being more widely applied to monitor the metabolic aspects of brain cancer. In vivo MRS biomarkers represent a promising advance and may influence treatment choice at both initial diagnosis and follow-up, given the inherent difficulties of sequential biopsies to monitor therapeutic response. When combined with anatomical or other types of imaging, MRS provides unique information regarding biochemistry in inoperable brain tumors and can complement neuropathological data, guide biopsies and enhance insight into therapeutic options. The combination of noninvasively acquired prognostic information and the high-resolution anatomical imaging provided by conventional MRI is expected to surpass molecular analysis and DNA microarray gene profiling, both of which, although promising, depend on invasive biopsy. This review focuses on recent data in the field of MRS in children with brain tumors.

Quantitative correlational study of microbubble-enhanced ultrasound imaging and magnetic resonance imaging of glioma and early response to radiotherapy in a rat model

PURPOSE

Radiotherapy remains a major treatment method for malignant tumors. Magnetic resonance imaging (MRI) is the standard modality for assessing glioma treatment response in the clinic. Compared to MRI, ultrasound imaging is low-cost and portable and can be used during intraoperative procedures. The purpose of this study was to quantitatively compare contrast-enhanced ultrasound (CEUS) imaging and MRI of irradiated gliomas in rats and to determine which quantitative ultrasound imaging parameters can be used for the assessment of early response to radiation in glioma.

METHODS

Thirteen nude rats with U87 glioma were used. A small thinned skull window preparation was performed to facilitate ultrasound imaging and mimic intraoperative procedures. Both CEUS and MRI with structural, functional, and molecular imaging parameters were performed at preradiation and at 1 day and 4 days postradiation. Statistical analysis was performed to determine the correlations between MRI and CEUS parameters and the changes between pre- and postradiation imaging.

RESULTS

Area under the curve (AUC) in CEUS showed significant difference between preradiation and 4 days postradiation, along with four MRI parameters, T2, apparent diffusion coefficient, cerebral blood flow, and amide proton transfer-weighted (APTw) (all p < 0.05). The APTw signal was correlated with three CEUS parameters, rise time (r = - 0.527, p < 0.05), time to peak (r = - 0.501, p < 0.05), and perfusion index (r = 458, p < 0.05). Cerebral blood flow was correlated with rise time (r = - 0.589, p < 0.01) and time to peak (r = - 0.543, p < 0.05).

CONCLUSIONS

MRI can be used for the assessment of radiotherapy treatment response and CEUS with AUC as a new technique and can also be one of the assessment methods for early response to radiation in glioma.

Pilot Preclinical and Clinical Evaluation of (4S)-4-(3-[18F]Fluoropropyl)-L-Glutamate (18F-FSPG) for PET/CT Imaging of Intracranial Malignancies

Purpose

(S)-4-(3-[¹⁸F]Fluoropropyl)-L-glutamic acid (18F-FSPG) is a novel radiopharmaceutical for Positron Emission Tomography (PET) imaging. It is a glutamate analogue that can be used to measure x_C^- transporter activity. This study was performed to assess the feasibility of 18F-FSPG for imaging orthotopic brain tumors in small animals and the translation of this approach in human subjects with intracranial malignancies.

Experimental Design

For the small animal study, GS9L glioblastoma cells were implanted into brains of Fischer rats and studied with 18F-FSPG, the 18F-labeled glucose derivative 18F-FDG and with the 18F-labeled amino acid derivative 18F-FET. For the human study, five subjects with either primary or metastatic brain cancer were recruited (mean age 50.4 years). After injection of 300 MBq of 18F-FSPG, 3 whole-body PET/Computed Tomography (CT) scans were obtained and safety parameters were measured. The three subjects with brain metastases also had an 18F-FDG PET/CT scan. Quantitative and qualitative comparison of the scans was performed to assess kinetics, biodistribution, and relative efficacy of the tracers.

Results

In the small animals, the orthotopic brain tumors were visualized well with 18F-FSPG. The high tumor uptake of 18F-FSPG in the GS9L model and the absence of background signal led to good tumor visualization with high contrast (tumor/brain ratio: 32.7). 18F-FDG and 18F-FET showed T/B ratios of 1.7 and 2.8, respectively. In the human pilot study, 18F-FSPG was well tolerated and there was similar distribution in all patients. All malignant lesions were positive with 18F-FSPG except for one low-grade primary brain tumor. In the 18F-FSPG-PET-positive tumors a similar T/B ratio was observed as in the animal model.

Conclusions

18F-FSPG is a novel PET radiopharmaceutical that demonstrates good uptake in both small animal and human studies of intracranial malignancies. Future studies on larger numbers of subjects and a wider array of brain tumors are planned.

Trial Registration

ClinicalTrials.gov NCT01186601

Monitoring the Bystander Killing Effect of Human Multipotent Stem Cells for Treatment of Malignant Brain Tumors

Tumor infiltrating stem cells have been suggested as a vehicle for the delivery of a suicide gene towards otherwise difficult to treat tumors like glioma. We have used herpes simplex virus thymidine kinase expressing human multipotent adult progenitor cells in two brain tumor models (hU87 and Hs683) in immune-compromised mice. In order to determine the best time point for the administration of the codrug ganciclovir, the stem cell distribution and viability were monitored in vivo using bioluminescence (BLI) and magnetic resonance imaging (MRI). Treatment was assessed by in vivo BLI and MRI of the tumors. We were able to show that suicide gene therapy using HSV-tk expressing stem cells can be followed in vivo by MRI and BLI. This has the advantage that (1) outliers can be detected earlier, (2) GCV treatment can be initiated based on stem cell distribution rather than on empirical time points, and (3) a more thorough follow-up can be provided prior to and after treatment of these animals. In contrast to rodent stem cell and tumor models, treatment success was limited in our model using human cell lines. This was most likely due to the lack of immune components in the immune-compromised rodents.