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

Advanced Neuroimaging Approaches to Pediatric Brain Tumors

Simple Summary

After leukemias, brain tumors are the most common cancers in children, and early, accurate diagnosis is critical to improve patient outcomes. Beyond the conventional imaging methods of computed tomography (CT) and magnetic resonance imaging (MRI), advanced neuroimaging techniques capable of both structural and functional imaging are moving to the forefront to improve the early detection and differential diagnosis of tumors of the central nervous system. Here, we review recent developments in neuroimaging techniques for pediatric brain tumors.

Abstract

Central nervous system tumors are the most common pediatric solid tumors; they are also the most lethal. Unlike adults, childhood brain tumors are mostly primary in origin and differ in type, location and molecular signature. Tumor characteristics (incidence, location, and type) vary with age. Children present with a variety of symptoms, making early accurate diagnosis challenging. Neuroimaging is key in the initial diagnosis and monitoring of pediatric brain tumors. Conventional anatomic imaging approaches (computed tomography (CT) and magnetic resonance imaging (MRI)) are useful for tumor detection but have limited utility differentiating tumor types and grades. Advanced MRI techniques (diffusion-weighed imaging, diffusion tensor imaging, functional MRI, arterial spin labeling perfusion imaging, MR spectroscopy, and MR elastography) provide additional and improved structural and functional information. Combined with positron emission tomography (PET) and single-photon emission CT (SPECT), advanced techniques provide functional information on tumor metabolism and physiology through the use of radiotracer probes. Radiomics and radiogenomics offer promising insight into the prediction of tumor subtype, post-treatment response to treatment, and prognostication. In this paper, a brief review of pediatric brain cancers, by type, is provided with a comprehensive description of advanced imaging techniques including clinical applications that are currently utilized for the assessment and evaluation of pediatric brain tumors.

In Vivo Quantitative Imaging of Glioma Heterogeneity Employing Positron Emission Tomography

Glioblastoma is the most common primary brain tumor, highly aggressive by being proliferative, neovascularized and invasive, heavily infiltrated by immunosuppressive glioma-associated myeloid cells (GAMs), including glioma-associated microglia/macrophages (GAMM) and myeloid-derived suppressor cells (MDSCs). Quantifying GAMs by molecular imaging could support patient selection for GAMs-targeting immunotherapy, drug target engagement and further assessment of clinical response. Magnetic resonance imaging (MRI) and amino acid positron emission tomography (PET) are clinically established imaging methods informing on tumor size, localization and secondary phenomena but remain quite limited in defining tumor heterogeneity, a key feature of glioma resistance mechanisms. The combination of different imaging modalities improved the in vivo characterization of the tumor mass by defining functionally distinct tissues probably linked to tumor regression, progression and infiltration. In-depth image validation on tracer specificity, biological function and quantification is critical for clinical decision making. The current review provides a comprehensive overview of the relevant experimental and clinical data concerning the spatiotemporal relationship between tumor cells and GAMs using PET imaging, with a special interest in the combination of amino acid and translocator protein (TSPO) PET imaging to define heterogeneity and as therapy readouts.

FLAIRectomy: Resecting beyond the Contrast Margin for Glioblastoma

The standard of care for isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) is maximal resection followed by chemotherapy and radiation. Studies investigating the resection of GBM have primarily focused on the contrast enhancing portion of the tumor on magnetic resonance imaging. Histopathological studies, however, have demonstrated tumor infiltration within peri-tumoral fluid-attenuated inversion recovery (FLAIR) abnormalities, which is often not resected. The histopathology of FLAIR and local recurrence patterns of GBM have prompted interest in the resection of peri-tumoral FLAIR, or FLAIRectomy. To this point, recent studies have suggested a significant survival benefit associated with safe peri-tumoral FLAIR resection. In this review, we discuss the evidence surrounding the composition of peri-tumoral FLAIR, outcomes associated with FLAIRectomy, future directions of the field, and potential implications for patients.

The Role of PET in Supratentorial and Infratentorial Pediatric Brain Tumors

OBJECTIVE

This review aims to provide a summary of the clinical indications and limitations of PET imaging with different radiotracers, including 18F-fluorodeoxyglucose (18F-FDG) and other radiopharmaceuticals, in pediatric neuro-oncology, discussing both supratentorial and infratentorial tumors, based on recent literature (from 2010 to present).

METHODS

A literature search of the PubMed/MEDLINE database was carried out searching for articles on the use of PET in pediatric brain tumors. The search was updated until December 2020 and limited to original studies published in English after 1 January 2010.

RESULTS

18F-FDG PET continues to be successfully employed in different settings in pediatric neuro-oncology, including diagnosis, grading and delineation of the target for stereotactic biopsy, estimation of prognosis, evaluation of recurrence, treatment planning and assessment of treatment response. Nevertheless, non-18F-FDG tracers, especially amino acid analogues seem to show a better performance in each clinical setting.

CONCLUSIONS

PET imaging adds important information in the diagnostic work-up of pediatric brain tumors. International or national multicentric studies are encouraged in order to collect larger amount of data.

[Dynamic11C-methionine PET/CT in differential diagnosis of brain gliomas]

OBJECTIVE

To evaluate the possibilities of dynamic preoperative ¹¹C-methionine (MET) PET/CT in differential diagnosis of various types of brain gliomas in adults.

MATERIAL AND METHODS

The study included 74 patients aged 48±14 years with supratentorial gliomas: Grade IV - glioblastoma (GB, n=33), Grade III - anaplastic oligodendroglioma (AOD, n=10) and anaplastic astrocytoma (AA, n=12), Grade II - diffuse astrocytoma (DA, n=13) and oligodendroglioma (OD, n=6). All patients underwent standard MRI and dynamic MET PET/CT within 20 minutes after intravenous injection of radiopharmaceutical. Then, we compared MRI and PET/CT data and comprehensively analyzed the early stages of time-activity curve using 2 parameters: the first pass peak (FPP) and the first peak of maximum uptake (Pmax).

RESULTS

We have significantly distinguished high-grade tumors (GB and AA+AOD) and certain benign gliomas (DA and OD) (p<0.05). AUC was over 0.7 and 0.8 for FPP and Pmax in differential diagnosis of various gliomas, respectively. We found that difficulties in differential diagnosis of gliomas arise mainly if oligodendrogliomas are included in the control group.

CONCLUSION

Dynamic PET/CT with analysis of FPP and Pmax increases specificity of differential diagnosis of various gliomas compared to standard static imaging. These data are valuable for choice of optimal treatment strategy, as well as fundamental research of metabolic processes and vascularization of various tumors.

TSPO imaging-guided characterization of the immunosuppressive myeloid tumor microenvironment in patients with malignant glioma

BACKGROUND

Tumor-associated microglia and macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs) are potent immunosuppressors in the glioma tumor microenvironment (TME). Their infiltration is associated with tumor grade, progression, and therapy resistance. Specific tools for image-guided analysis of spatiotemporal changes in the immunosuppressive myeloid tumor compartments are missing. We aimed (i) to evaluate the role of fluorodeoxyglucose (18F)DPA-714* (translocator protein [TSPO]) PET-MRI in the assessment of the immunosuppressive TME in glioma patients, and (ii) to cross-correlate imaging findings with in-depth immunophenotyping.

METHODS

To characterize the glioma TME, a mixed collective of 9 glioma patients underwent [18F]DPA-714-PET-MRI in addition to [18F]fluoro-ethyl-tyrosine (FET)-PET-MRI. Image-guided biopsy samples were immunophenotyped by multiparametric flow cytometry and immunohistochemistry. In vitro autoradiography was performed for image validation and assessment of tracer binding specificity.

RESULTS

We found a strong relationship (r = 0.84, P = 0.009) between the [18F]DPA-714 uptake and the number and activation level of glioma-associated myeloid cells (GAMs). TSPO expression was mainly restricted to human leukocyte antigen D related-positive (HLA-DR+) activated GAMs, particularly to tumor-infiltrating HLA-DR+ MDSCs and TAMs. [18F]DPA-714-positive tissue volumes exceeded [18F]FET-positive volumes and showed a differential spatial distribution.

CONCLUSION

[18F]DPA-714-PET may be used to non-invasively image the glioma-associated immunosuppressive TME in vivo. This imaging paradigm may also help to characterize the heterogeneity of the glioma TME with respect to the degree of myeloid cell infiltration at various disease stages. [18F]DPA-714 may also facilitate the development of new image-guided therapies targeting the myeloid-derived TME.

Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model

BACKGROUND AND PURPOSE

Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a deep learning model for fully automated detection and segmentation of brain metastases in patients with malignant melanoma using clinical routine MR imaging.

MATERIALS AND METHODS

Sixty-nine patients with melanoma with a total of 135 brain metastases at initial diagnosis and available multiparametric MR imaging datasets (T1-/T2-weighted, T1-weighted gadolinium contrast-enhanced, FLAIR) were included. A previously established deep learning model architecture (3D convolutional neural network; DeepMedic) simultaneously operating on the aforementioned MR images was trained on a cohort of 55 patients with 103 metastases using 5-fold cross-validation. The efficacy of the deep learning model was evaluated using an independent test set consisting of 14 patients with 32 metastases. Manual segmentations of metastases in a voxelwise manner (T1-weighted gadolinium contrast-enhanced imaging) performed by 2 radiologists in consensus served as the ground truth.

RESULTS

After training, the deep learning model detected 28 of 32 brain metastases (mean volume, 1.0 [SD, 2.4] cm³) in the test cohort correctly (sensitivity of 88%), while false-positive findings of 0.71 per scan were observed. Compared with the ground truth, automated segmentations achieved a median Dice similarity coefficient of 0.75.

CONCLUSIONS

Deep learning-based automated detection and segmentation of brain metastases in malignant melanoma yields high detection and segmentation accuracy with false-positive findings of <1 per scan.

Image-Guided Brain Surgery

In neurosurgery, the extent of resection plays a critical role, especially in the management of malignant gliomas. These tumors are characterized through a diffuse infiltration into the surrounding brain parenchyma. Delineation between tumor and normal brain parenchyma can therefore often be challenging. During the recent years, several techniques, aiming at better intraoperative tumor visualization, have been developed and implemented in the field of brain tumor surgery. In this chapter, we discuss current strategies for intraoperative imaging in brain tumor surgery, comprising conventional techniques such as neuronavigation, techniques using fluorescence-guided surgery, and further highly precise developments such as targeted fluorescence spectroscopy or Raman spectroscopy.

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy. In this paper, we propose a new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction. Specifically, we propose a multi-task convolutional neural network (CNN) to accomplish both tumor genotype and OS prediction tasks jointly. As the network can benefit from learning tumor genotype-related features for genotype prediction, the accuracy of predicting OS time can be prominently improved. In the experiments, multimodal MRI brain dataset of 120 GBM patients, with as many as four different genotypic/molecular biomarkers, are used to evaluate our method. Our method achieves the highest OS prediction accuracy compared to other state-of-the-art methods.

Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation

PURPOSE

Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicated meningioma deep learning model based on routine magnetic resonance imaging (MRI) data and evaluated its performance for automated tumor segmentation.

METHODS

The MRI datasets included T1-weighted/T2-weighted, T1-weighted contrast-enhanced (T1CE) and FLAIR of 126 patients with intracranial meningiomas (grade I: 97, grade II: 29). For automated segmentation, an established deep learning model architecture (3D deep convolutional neural network, DeepMedic, BioMedIA) operating on all four MR sequences was used. Segmentation included the following two components: (i) contrast-enhancing tumor volume in T1CE and (ii) total lesion volume (union of lesion volume in T1CE and FLAIR, including solid tumor parts and surrounding edema). Preprocessing of imaging data included registration, skull stripping, resampling, and normalization. After training of the deep learning model using manual segmentations by 2 independent readers from 70 patients (training group), the algorithm was evaluated on 56 patients (validation group) by comparing automated to ground truth manual segmentations, which were performed by 2 experienced readers in consensus.

RESULTS

Of the 56 meningiomas in the validation group 55 were detected by the deep learning model. In these patients the comparison of the deep learning model and manual segmentations revealed average dice coefficients of 0.91 ± 0.08 for contrast-enhancing tumor volume and 0.82 ± 0.12 for total lesion volume. In the training group, interreader variabilities of the 2 manual readers were 0.92 ± 0.07 for contrast-enhancing tumor and 0.88 ± 0.05 for total lesion volume.

CONCLUSION

Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.

The beginning of the end for conventional RECIST - novel therapies require novel imaging approaches

Owing to improvements in our understanding of the biological principles of tumour initiation and progression, a wide variety of novel targeted therapies have been developed. Developments in biomedical imaging, however, have not kept pace with these improvements and are still mainly designed to determine lesion size alone, which is reflected in the Response Evaluation Criteria in Solid Tumors (RECIST). Imaging approaches currently used for the evaluation of treatment responses in patients with solid tumours, therefore, often fail to detect successful responses to novel targeted agents and might even falsely suggest disease progression, a scenario known as pseudoprogression. The ability to differentiate between responders and nonresponders early in the course of treatment is essential to allowing the early adjustment of treatment regimens. Various imaging approaches targeting a single dedicated tumour feature, as described in the hallmarks of cancer, have been successful in preclinical investigations, and some have been evaluated in pilot clinical trials. However, these approaches have largely not been implemented in clinical practice. In this Review, we describe current biomedical imaging approaches used to monitor responses to treatment in patients receiving novel targeted therapies, including a summary of the most promising future approaches and how these might improve clinical practice.

The biology and mathematical modelling of glioma invasion: a review

Adult gliomas are aggressive brain tumours associated with low patient survival rates and limited life expectancy. The most important hallmark of this type of tumour is its invasive behaviour, characterized by a markedly phenotypic plasticity, infiltrative tumour morphologies and the ability of malignant progression from low- to high-grade tumour types. Indeed, the widespread infiltration of healthy brain tissue by glioma cells is largely responsible for poor prognosis and the difficulty of finding curative therapies. Meanwhile, mathematical models have been established to analyse potential mechanisms of glioma invasion. In this review, we start with a brief introduction to current biological knowledge about glioma invasion, and then critically review and highlight future challenges for mathematical models of glioma invasion.

Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI

Objectives

Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations.

Methods

We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE.

Results

The DLM detected meningiomas in 55 of 56 cases. Further, automated segmentations correlated strongly with manual segmentations: average Dice coefficients were 0.81 ± 0.10 (range, 0.46-0.93) for the total tumour volume (union of tumour volume in FLAIR and T1CE) and 0.78 ± 0.19 (range, 0.27-0.95) for contrast-enhancing tumour volume in T1CE.

Conclusions

The DLM yielded accurate automated detection and segmentation of meningioma tissue despite diverse scanner data and thereby may improve and facilitate therapy planning as well as monitoring of this highly frequent tumour entity.

Key Points

• Deep learning allows for accurate meningioma detection and segmentation

• Deep learning helps clinicians to assess patients with meningiomas

• Meningioma monitoring and treatment planning can be improved

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5595-8) contains supplementary material, which is available to authorized users.

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.