Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials

Revealing the biology behind MRI signatures in high grade glioma

Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.

Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration

Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different "ideal" and "minimum requirements" brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials.

Investigating cerebral perfusion with high resolution hyperpolarized [1-13 C]pyruvate MRI

PURPOSE

To investigate high-resolution hyperpolarized (HP) 13 C pyruvate MRI for measuring cerebral perfusion in the human brain.

METHODS

HP [1-13 C]pyruvate MRI was acquired in five healthy volunteers with a multi-resolution EPI sequence with 7.5 × 7.5 mm2 resolution for pyruvate. Perfusion parameters were calculated from pyruvate MRI using block-circulant singular value decomposition and compared to relative cerebral blood flow calculated from arterial spin labeling (ASL). To examine regional perfusion patterns, correlations between pyruvate and ASL perfusion were performed for whole brain, gray matter, and white matter voxels.

RESULTS

High resolution 7.5 × 7.5 mm2 pyruvate images were used to obtain relative cerebral blood flow (rCBF) values that were significantly positively correlated with ASL rCBF values (r = 0.48, 0.20, 0.28 for whole brain, gray matter, and white matter voxels respectively). Whole brain voxels exhibited the highest correlation between pyruvate and ASL perfusion, and there were distinct regional patterns of relatively high ASL and low pyruvate normalized rCBF found across subjects.

CONCLUSION

Acquiring HP 13 C pyruvate metabolic images at higher resolution allows for finer spatial delineation of brain structures and can be used to obtain cerebral perfusion parameters. Pyruvate perfusion parameters were positively correlated to proton ASL perfusion values, indicating a relationship between the two perfusion measures. This HP 13 C study demonstrated that hyperpolarized pyruvate MRI can assess cerebral metabolism and perfusion within the same study.

Nanotechnology for brain tumor imaging and therapy based on π-conjugated materials: state-of-the-art advances and prospects

In contemporary biomedical research, the development of nanotechnology has brought forth numerous possibilities for brain tumor imaging and therapy. Among these, π-conjugated materials have garnered significant attention as a special class of nanomaterials in brain tumor-related studies. With their excellent optical and electronic properties, π-conjugated materials can be tailored in structure and nature to facilitate applications in multimodal imaging, nano-drug delivery, photothermal therapy, and other related fields. This review focuses on presenting the cutting-edge advances and application prospects of π-conjugated materials in brain tumor imaging and therapeutic nanotechnology.

Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms

This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals' data. All models' median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74-0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring.

Modern preoperative imaging and functional mapping in patients with intracranial glioma

Magnetic resonance imaging (MRI) in therapy-naïve intracranial glioma is paramount for neuro-oncological diagnostics, and it provides images that are helpful for surgery planning and intraoperative guidance during tumor resection, including assessment of the involvement of functionally eloquent brain structures. This study reviews emerging MRI techniques to depict structural information, diffusion characteristics, perfusion alterations, and metabolism changes for advanced neuro-oncological imaging. In addition, it reflects current methods to map brain function close to a tumor, including functional MRI and navigated transcranial magnetic stimulation with derived function-based tractography of subcortical white matter pathways. We conclude that modern preoperative MRI in neuro-oncology offers a multitude of possibilities tailored to clinical needs, and advancements in scanner technology (e. g., parallel imaging for acceleration of acquisitions) make multi-sequence protocols increasingly feasible. Specifically, advanced MRI using a multi-sequence protocol enables noninvasive, image-based tumor grading and phenotyping in patients with glioma. Furthermore, the add-on use of preoperatively acquired MRI data in combination with functional mapping and tractography facilitates risk stratification and helps to avoid perioperative functional decline by providing individual information about the spatial location of functionally eloquent tissue in relation to the tumor mass. KEY POINTS::   · Advanced preoperative MRI allows for image-based tumor grading and phenotyping in glioma.. · Multi-sequence MRI protocols nowadays make it possible to assess various tumor characteristics (incl. perfusion, diffusion, and metabolism).. · Presurgical MRI in glioma is increasingly combined with functional mapping to identify and enclose individual functional areas.. · Advancements in scanner technology (e. g., parallel imaging) facilitate increasing application of dedicated multi-sequence imaging protocols.. CITATION FORMAT: · Sollmann N, Zhang H, Kloth C et al. Modern preoperative imaging and functional mapping in patients with intracranial glioma. Fortschr Röntgenstr 2023; 195: 989 - 1000.

Parallel detection of multi-contrast MRI and Deuterium Metabolic Imaging (DMI) for time-efficient characterization of neurological diseases

Deuterium Metabolic Imaging (DMI) is a novel method that can complement traditional anatomical magnetic resonance imaging (MRI) of the brain. DMI relies on the MR detection of metabolites that become labeled with deuterium (2H) after administration of a deuterated substrate and can provide images with highly specific metabolic information. However, clinical adoption of DMI is complicated by its relatively long scan time. Here, we demonstrate a strategy to interleave DMI data acquisition with MRI that results in a comprehensive neuro-imaging protocol without adding scan time. The interleaved MRI-DMI routine includes four essential clinical MRI scan types, namely T1-weighted MP-RAGE, FLAIR, T2-weighted Imaging (T2W) and susceptibility weighted imaging (SWI), interwoven with DMI data acquisition. Phantom and in vivo human brain data show that MR image quality, DMI sensitivity, as well as information content are preserved in the MRI-DMI acquisition method. The interleaved MRI-DMI technology provides full flexibility to upgrade traditional MRI protocols with DMI, adding unique metabolic information to existing types of anatomical image contrast, without extra scan time.

Activity of a first-in-class oral HIF2-alpha inhibitor, PT2385, in patients with first recurrence of glioblastoma

INTRODUCTION

Hypoxia inducible factor 2-alpha (HIF2α) mediates cellular responses to hypoxia and is over-expressed in glioblastoma (GBM). PT2385 is an oral HIF2α inhibitor with in vivo activity against GBM.

METHODS

A two-stage single-arm open-label phase II study of adults with GBM at first recurrence following chemoradiation with measurable disease was conducted through the Adult Brain Tumor Consortium. PT2385 was administered at the phase II dose (800 mg b.i.d.). The primary outcome was objective radiographic response (ORR = complete response + partial response, CR + PR); secondary outcomes were safety, overall survival (OS), and progression free survival (PFS). Exploratory objectives included pharmacokinetics (day 15 Cmin), pharmacodynamics (erythropoietin, vascular endothelial growth factor), and pH-weighted amine- chemical exchange saturation transfer (CEST) MRI to quantify tumor acidity at baseline and explore associations with drug response. Stage 1 enrolled 24 patients with early stoppage for ≤ 1 ORR.

RESULTS

Of the 24 enrolled patients, median age was 62.1 (38.7-76.7) years, median KPS 80, MGMT promoter was methylated in 46% of tumors. PT2385 was well tolerated. Grade ≥ 3 drug-related adverse events were hypoxia (n = 2), hyponatremia (2), lymphopenia (1), anemia (1), and hyperglycemia (1). No objective radiographic responses were observed; median PFS was 1.8 months (95% CI 1.6-2.5) and OS was 7.7 months (95% CI 4.9-12.6). Drug exposure varied widely and did not differ by corticosteroid use (p = 0.12), antiepileptics (p = 0.09), or sex (p = 0.37). Patients with high systemic exposure had significantly longer PFS (6.7 vs 1.8 months, p = 0.009). Baseline acidity by pH-weighted CEST MRI correlated significantly with treatment duration (R2 = 0.49, p = 0.017). Non-enhancing infiltrative disease with high acidity gave rise to recurrence.

CONCLUSIONS

PT2385 monotherapy had limited activity in first recurrent GBM. Drug exposure was variable. Signals of activity were observed in GBM patients with high systemic exposure and acidic lesions on CEST imaging. A second-generation HIF2α inhibitor is being studied.

Residual diffusion-weighted imaging hyperintense signal in primary central nervous system lymphoma can predict early recurrence

BACKGROUND

The treatment response of primary central nervous system lymphomas (PCNSLs) is mainly evaluated using postcontrast T1-weighted imaging (T1WI). Because poorly enhanced lesions may contain residual tumors, the combination of evaluation methods will potentially improve the accuracy of determining treatment effectiveness. In this study, we evaluated the usefulness of diffusion-weighted imaging (DWI) in predicting recurrence among patients with PCNSL who achieved complete response (CR)/unconfirmed CR (CRu).

METHODS

Fifty-four patients newly diagnosed with PCNSL who were treated at our institution and achieved CR/CRu at the end of treatment were included in this study. The patients were divided into two groups according to the presence or absence of residual DWI hyperintense signal at the tumor site at the end of treatment. Kaplan-Meier analysis was performed to analyze the median overall survival (OS) and progression-free survival (PFS).

RESULTS

The mean age of the 54 patients was 66.4 ± 13.3 years. The induction therapies were HD-MTX in 20 patients, R-MPV in 29 patients, and other chemotherapies in five patients. Radiotherapy was performed in 35 patients, high-dose cytarabine therapy in 14 patients, and autologous hematopoietic stem cell transplantation in one patient, and of the 54 patients, 10 had no consolidation therapy. The residual DWI hyperintense signal sign was observed in 18 patients. The R-MPV regimen was statistically associated with a lower rate of residual DWI hyperintense signal (p = 0.0453). The median PFS was statistically shorter in the residual DWI hyperintense signal group than in the non-residual DWI hyperintense signal group (14.0 months vs. 85.1 months) (p < 0.0001, log-rank test).

CONCLUSION

A residual DWI hyperintense signal at the end of treatment was statistically associated with shorter PFS. Among patients who achieved CR/CRu evaluated based on postcontrast T1WI, DWI could be a valuable additional sequence to predict the early recurrence of PCNSL.

Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule

Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging technique but suffers from long scan times and complicated processing. CEST was recently combined with magnetic resonance fingerprinting (MRF) to address these shortcomings. However, the CEST-MRF signal depends on multiple acquisition and tissue parameters so selecting an optimal acquisition schedule is challenging. In this work, we propose a novel dual-network deep learning framework to optimize the CEST-MRF acquisition schedule. The quality of the optimized schedule was assessed in a digital brain phantom and compared with alternate deep learning optimization approaches. The effect of schedule length on the reconstruction error was also investigated. A healthy subject was scanned with optimized and random schedules and with a conventional CEST sequence for comparison. The optimized schedule was also tested in a subject with metastatic renal cell carcinoma. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter (WM) and grey matter (GM). The optimized schedule was 12% shorter but yielded equal or lower normalized root mean square error for all parameters. The proposed optimization also provided a lower error compared with alternate methodologies. Longer schedules generally yielded lower error. In vivo maps obtained with the optimized schedule showed reduced noise and improved delineation of GM and WM. CEST curves synthesized from the optimized parameters were highly correlated (r = 0.99) with measured conventional CEST. The mean concordance correlation coefficient in WM/GM for all tissue parameters was 0.990/0.978 for the optimized schedule but only 0.979/0.975 for the random schedule. The proposed schedule optimization is widely applicable to MRF pulse sequences and provides accurate and reproducible tissue maps with reduced noise at a shorter scan time than a randomly generated schedule.

International EANM-SNMMI-ISMRM consensus recommendation for PET/MRI in oncology

PREAMBLE

The Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and professional organization founded in 1954 to promote the science, technology, and practical application of nuclear medicine. The European Association of Nuclear Medicine (EANM) is a professional non-profit medical association that facilitates communication worldwide between individuals pursuing clinical and research excellence in nuclear medicine. The EANM was founded in 1985. The merged International Society for Magnetic Resonance in Medicine (ISMRM) is an international, nonprofit, scientific association whose purpose is to promote communication, research, development, and applications in the field of magnetic resonance in medicine and biology and other related topics and to develop and provide channels and facilities for continuing education in the field.The ISMRM was founded in 1994 through the merger of the Society of Magnetic Resonance in Medicine and the Society of Magnetic Resonance Imaging. SNMMI, ISMRM, and EANM members are physicians, technologists, and scientists specializing in the research and practice of nuclear medicine and/or magnetic resonance imaging. The SNMMI, ISMRM, and EANM will periodically define new guidelines for nuclear medicine practice to help advance the science of nuclear medicine and/or magnetic resonance imaging and to improve the quality of service to patients throughout the world. Existing practice guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated. Each practice guideline, representing a policy statement by the SNMMI/EANM/ISMRM, has undergone a thorough consensus process in which it has been subjected to extensive review. The SNMMI, ISMRM, and EANM recognize that the safe and effective use of diagnostic nuclear medicine imaging and magnetic resonance imaging requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guideline by those entities not providing these services is not authorized. These guidelines are an educational tool designed to assist practitioners in providing appropriate care for patients. They are not inflexible rules or requirements of practice and are not intended, nor should they be used, to establish a legal standard of care. For these reasons and those set forth below, the SNMMI, the ISMRM, and the EANM caution against the use of these guidelines in litigation in which the clinical decisions of a practitioner are called into question. The ultimate judgment regarding the propriety of any specific procedure or course of action must be made by the physician or medical physicist in light of all the circumstances presented. Thus, there is no implication that an approach differing from the guidelines, standing alone, is below the standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the guidelines when, in the reasonable judgment of the practitioner, such course of action is indicated by the condition of the patient, limitations of available resources, or advances in knowledge or technology subsequent to publication of the guidelines. The practice of medicine includes both the art and the science of the prevention, diagnosis, alleviation, and treatment of disease. The variety and complexity of human conditions make it impossible to always reach the most appropriate diagnosis or to predict with certainty a particular response to treatment. Therefore, it should be recognized that adherence to these guidelines will not ensure an accurate diagnosis or a successful outcome. All that should be expected is that the practitioner will follow a reasonable course of action based on current knowledge, available resources, and the needs of the patient to deliver effective and safe medical care. The sole purpose of these guidelines is to assist practitioners in achieving this objective.

Dendritic Cell Vaccination in Conjunction with a TLR Agonist Polarizes Interferon Immune Responses in Malignant Glioma Patients

Autologous tumor lysate-pulsed dendritic cell (ATL-DC) vaccination is a promising immunotherapy for patients with high grade gliomas, but responses have not been demonstrated in all patients. To determine the most effective combination of autologous tumor lysate-pulsed DC vaccination, with or without the adjuvant toll-like receptor (TLR) agonists poly-ICLC or resiquimod, we conducted a Phase 2 clinical trial in 23 patients with newly diagnosed or recurrent WHO Grade III-IV malignant gliomas. We then performed deep, high-dimensional immune profiling of these patients to better understand how TLR agonists may influence the systemic immune responses induced by ATL-DC vaccination. Bulk RNAseq data demonstrated highly significant upregulation of type 1 and type 2 interferon gene expression selectively in patients who received adjuvant a TLR agonist together with ATL-DC. CyTOF analysis of patient peripheral blood mononuclear cells (PBMCs) showed increased expression of PD-1 on CD4+ T-cells, decreases in CD38 and CD39 on CD8+ T cells and elevated proportion of monocytes after ATL-DC + TLR agonist administration. In addition, scRNA-seq demonstrated a higher expression fold change of IFN-induced genes with poly-ICLC treatment in both peripheral blood monocytes and T lymphocytes. Patients who had higher expression of interferon response genes lived significantly longer and had longer time to progression compared to those with lower expression. The results suggest that ATL-DC in conjunction with adjuvant poly-ICLC induces a polarized interferon response in circulating monocytes and specific activation of a CD8+ T cell population, which may represent an important blood biomarker for immunotherapy in this patient population.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01204684.

Mining multi-center heterogeneous medical data with distributed synthetic learning

Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the protection of sensitive personal information. DSL enables the building of a homogeneous dataset with entirely synthetic medical images via a form of GAN-based synthetic learning. The proposed DSL architecture has the following key functionalities: multi-modality learning, missing modality completion learning, and continual learning. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider by the use of an ideal synthetic quality metric called Dist-FID. We show that DSL can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55% and the temporal datasets segmentation model by 8%.

Defining subventricular zone involvement to predict the survival of patients in isocitrate dehydrogenase-wild type glioblastoma: validation in a prospective registry

OBJECTIVES

The prognostic value of subventricular zone distance (SVD) is unclear because of different definitions and lack of evaluation of clinical survival models. The aim of this study was to define SVD and evaluate its prognostic value in a survival nomogram for glioblastoma.

METHODS

This retrospective study included 158 (SVD biomarker) from historical glioblastoma patients and 187 (survival modeling) with IDH-wild type glioblastoma from a prospective registry (NCT02619890). SVD was assessed by two radiologists: definition 1, the distance between the tumor edge to subventricular zone (SVZ); definition 2, the distance between the tumor centroid to SVZ; definition 3, enhancement at the ventricular wall. The associations between SVD and overall survival (OS) were evaluated using multivariable Cox proportional hazards regression analysis. Performance of an updated SVD survival model was compared with that of the Radiation Therapy Oncology Group (RTOG) 0525 nomogram.

RESULTS

SVD according to both definition 1 (hazard ratio [HR]: 0.97, 95% CI: 0.94-0.99; p = .011) and definition 2 (HR: 0.96, 0.94-0.98, p < .001) was adversely associated with OS. Definition 1 was adversely associated with PFS (HR: 0.96, 0.94-0.99, p = .008) and showed the highest reproducibility (intraclass correlation coefficient, 0.90). The SVD-updated model showed similar to better performance than the RTOG model for predicting OS of up to 3 years (AUC: 0.735-0.738 vs. 0.687-0.708), with higher time-dependent specificity for 1-year (89.9% vs. 70.6%) and 3-year OS (93.3% vs. 80.0%).

CONCLUSION

SVZ distance is an independent adverse prognostic factor in patients with IDH-wild type glioblastoma. Updating the survival model with SVZ provides better time-dependent specificity and reproducibility.

KEY POINTS

• Subventricular zone distance (SVD) measurement from tumor edge showed high reproducibility. • Longer SVD was independently associated with longer overall survival. • Adding SVD improved time-dependent specificity for survival model in a prospective registry.

Relationship between apparent diffusion coefficient and survival as a function of distance from gross tumor volume on radiation planning MRI in newly diagnosed glioblastoma

PURPOSE

To investigate the changes in apparent diffusion coefficient (ADC) within incrementally-increased margins beyond the gross tumor volume (GTV) on post-operative radiation planning MRI and their prognostic utility in glioblastoma.

METHODS

Radiation planning MRIs of adult patients with newly diagnosed glioblastoma from 2017 to 2020 were assessed. The ADC values were normalized to contralateral normal white matter (nADC). Using 1 mm isotropic incremental margin increases from the GTV, the nADC values were calculated at each increment. Age, ECOG performance status, extent of resection and MGMT promoter methylation status were obtained from medical records. Using univariate and multivariable Cox regression analysis, association of nADC to progression-free and overall survival (PFS, OS) was assessed at each increment.

RESULTS

Seventy consecutive patients with mean age of 53.6 ± 10.3 years, were evaluated. The MGMT promoter was methylated in 31 (44.3%), unmethylated in 36 (51.6%) and unknown in 3 (4.3%) patients. 11 (16%) underwent biopsy, 41 (44%) subtotal resection and 18 (26%) gross total resection. For each 1 mm increase in distance from GTV, the nADC decreased by 0.16% (p < 0.0001). At 1-5 mm increment, the nADC was associated with OS (p < 0.01). From 6 to 11 mm increment the nADC was associated with OS with the p-value gradually increasing from 0.018 to 0.046. nADC was not associated with PFS.

CONCLUSION

The nADC values at 1-11 mm increments from the GTV margin were associated with OS. Future prospective multicenter studies are needed to validate the findings and to pave the way for the utilization of ADC for margin reduction in radiation planning.

Growth exponents reflect evolutionary processes and treatment response in brain metastases

Tumor growth is the result of the interplay of complex biological processes in huge numbers of individual cells living in changing environments. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or simple animal models with bounded-growth dynamics accurately. However, results for the growth of human cancers in patients are scarce. Our study mined a large dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up to find growth laws for untreated BMs and recurrent treated BMs. Untreated BMs showed high growth exponents, most likely related to the underlying evolutionary dynamics, with experimental tumors in mice resembling accurately the disease. Recurrent BMs growth exponents were smaller, most probably due to a reduction in tumor heterogeneity after treatment, which may limit the tumor evolutionary capabilities. In silico simulations using a stochastic discrete mesoscopic model with basic evolutionary dynamics led to results in line with the observed data.

Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics

Diffusion-weighted imaging (DWI) and its numerical expression via apparent diffusion coefficient (ADC) values are commonly utilized in non-invasive assessment of various brain pathologies. Although numerous studies have confirmed that ADC values could be pathognomic for various ring-enhancing lesions (RELs), their true potential is yet to be exploited in full. The article was designed to introduce an image analysis method allowing REL recognition independently of either absolute ADC values or specifically defined regions of interest within the evaluated image. For this purpose, the line of interest (LOI) was marked on each ADC map to cross all of the RELs' compartments. Using a machine learning approach, we analyzed the LOI between two representatives of the RELs, namely, brain abscess and glioblastoma (GBM). The diagnostic ability of the selected parameters as predictors for the machine learning algorithms was assessed using two models, the k-NN model and the SVM model with a Gaussian kernel. With the k-NN machine learning method, 80% of the abscesses and 100% of the GBM were classified correctly at high accuracy. Similar results were obtained via the SVM method. The proposed assessment of the LOI offers a new approach for evaluating ADC maps obtained from different RELs and contributing to the standardization of the ADC map assessment.

Primary central nervous system lymphoma

Primary central nervous system lymphoma (PCNSL) is a diffuse large B cell lymphoma in which the brain, spinal cord, leptomeninges and/or eyes are exclusive sites of disease. Pathophysiology is incompletely understood, although a central role seems to comprise immunoglobulins binding to self-proteins expressed in the central nervous system (CNS) and alterations of genes involved in B cell receptor, Toll-like receptor and NF-κB signalling. Other factors such as T cells, macrophages or microglia, endothelial cells, chemokines, and interleukins, probably also have important roles. Clinical presentation varies depending on the involved regions of the CNS. Standard of care includes methotrexate-based polychemotherapy followed by age-tailored thiotepa-based conditioned autologous stem cell transplantation and, in patients unsuitable for such treatment, consolidation with whole-brain radiotherapy or single-drug maintenance. Personalized treatment, primary radiotherapy and only supportive care should be considered in unfit, frail patients. Despite available treatments, 15-25% of patients do not respond to chemotherapy and 25-50% relapse after initial response. Relapse rates are higher in older patients, although the prognosis of patients experiencing relapse is poor independent of age. Further research is needed to identify diagnostic biomarkers, treatments with higher efficacy and less neurotoxicity, strategies to improve the penetration of drugs into the CNS, and roles of other therapies such as immunotherapies and adoptive cell therapies.

Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool

PURPOSE

The quantification of radiotherapy (RT)-induced functional and morphological brain alterations is fundamental to guide therapeutic decisions in patients with brain tumors. The magnetic resonance imaging (MRI) allows to define structural RT-brain changes, but it is unable to evaluate early injuries and to objectively quantify the volume tissue loss. Artificial intelligence (AI) tools extract accurate measurements that permit an objective brain different region quantification. In this study, we assessed the consistency between an AI software (Quibim Precision^® 2.9) and qualitative neruroradiologist evaluation, and its ability to quantify the brain tissue changes during RT treatment in patients with glioblastoma multiforme (GBM).

METHODS

GBM patients treated with RT and subjected to MRI assessment were enrolled. Each patient, pre- and post-RT, undergoes to a qualitative evaluation with global cerebral atrophy (GCA) and medial temporal lobe atrophy (MTA) and a quantitative assessment with Quibim Brain screening and hippocampal atrophy and asymmetry modules on 19 extracted brain structures features.

RESULTS

A statistically significant strong negative association between the percentage value of the left temporal lobe and the GCA score and the left temporal lobe and the MTA score was found, while a moderate negative association between the percentage value of the right hippocampus and the GCA score and the right hippocampus and the MTA score was assessed. A statistically significant strong positive association between the CSF percentage value and the GCA score and a moderate positive association between the CSF percentage value and the MTA score was found. Finally, quantitative feature values showed that the percentage value of the cerebro-spinal fluid (CSF) statistically differences between pre- and post-RT.

CONCLUSIONS

AI tools can support a correct evaluation of RT-induced brain injuries, allowing an objective and earlier assessment of the brain tissue modifications.

Application of radiomics to meningiomas: A systematic review

BACKGROUND

Quantitative imaging analysis through radiomics is a powerful technology to non-invasively assess molecular correlates and guide clinical decision-making. There has been growing interest in image-based phenotyping for meningiomas given the complexities in management.

METHODS

We systematically reviewed meningioma radiomics analyses published in PubMed, Embase, and Web of Science until December 20, 2021. We compiled performance data and assessed publication quality using the radiomics quality score (RQS).

RESULTS

A total of 170 publications were grouped into 5 categories of radiomics applications to meningiomas: Tumor detection and segmentation (21%), classification across neurologic diseases (54%), grading (14%), feature correlation (3%), and prognostication (8%). A majority focused on technical model development (73%) versus clinical applications (27%), with increasing adoption of deep learning. Studies utilized either private institutional (50%) or public (49%) datasets, with only 68% using a validation dataset. For detection and segmentation, radiomic models had a mean accuracy of 93.1 ± 8.1% and a dice coefficient of 88.8 ± 7.9%. Meningioma classification had a mean accuracy of 95.2 ± 4.0%. Tumor grading had a mean area-under-the-curve (AUC) of 0.85 ± 0.08. Correlation with meningioma biological features had a mean AUC of 0.89 ± 0.07. Prognostication of the clinical course had a mean AUC of 0.83 ± 0.08. While clinical studies had a higher mean RQS compared to technical studies, quality was low overall with a mean RQS of 6.7 ± 5.9 (possible range -8 to 36).

CONCLUSIONS

There has been global growth in meningioma radiomics, driven by data accessibility and novel computational methodology. Translatability toward complex tasks such as prognostication requires studies that improve quality, develop comprehensive patient datasets, and engage in prospective trials.

Objective response rate targets for recurrent glioblastoma clinical trials based on the historic association between objective response rate and median overall survival

Durable objective response rate (ORR) remains a meaningful endpoint in recurrent cancer; however, the target ORR for single-arm recurrent glioblastoma trials has not been based on historic information or tied to patient outcomes. The current study reviewed 68 treatment arms comprising 4793 patients in past trials in recurrent glioblastoma in order to judiciously define target ORRs for use in recurrent glioblastoma trials. ORR was estimated at 6.1% [95% CI 4.23; 8.76%] for cytotoxic chemothera + pies (ORR = 7.59% for lomustine, 7.57% for temozolomide, 0.64% for irinotecan, and 5.32% for other agents), 3.37% for biologic agents, 7.97% for (select) immunotherapies, and 26.8% for anti-angiogenic agents. ORRs were significantly correlated with median overall survival (mOS) across chemotherapy (R2= 0.4078, P < .0001), biologics (R2= 0.4003, P = .0003), and immunotherapy trials (R2= 0.8994, P < .0001), but not anti-angiogenic agents (R2= 0, P = .8937). Pooling data from chemotherapy, biologics, and immunotherapy trials, a meta-analysis indicated a strong correlation between ORR and mOS (R2= 0.3900, P < .0001; mOS [weeks] = 1.4xORR + 24.8). Assuming an ineffective cytotoxic (control) therapy has ORR = 7.6%, the average ORR for lomustine and temozolomide trials, a sample size of ≥40 patients with target ORR>25% is needed to demonstrate statistical significance compared to control with a high level of confidence (P < .01) and adequate power (>80%). Given this historic data and potential biases in patient selection, we recommend that well-controlled, single-arm phase II studies in recurrent glioblastoma should have a target ORR >25% (which translates to a median OS of approximately 15 months) and a sample size of ≥40 patients, in order to convincingly demonstrate antitumor activity. Crucially, this response needs to have sufficient durability, which was not addressed in the current study.

Multi-nuclear sodium, diffusion, and perfusion MRI in human gliomas

PURPOSE

There is limited knowledge about the associations between sodium and proton MRI measurements in brain tumors. The purpose of this study was to quantify intra- and intertumoral correlations between sodium, diffusion, and perfusion MRI in human gliomas.

METHODS

Twenty glioma patients were prospectively studied on a 3T MRI system with multinuclear capabilities. Three mutually exclusive tumor volumes of interest (VOIs) were segmented: contrast-enhancing tumor (CET), T2/FLAIR hyperintense non-enhancing tumor (NET), and necrosis. Median and voxel-wise associations between apparent diffusion coefficient (ADC), normalized relative cerebral blood volume (nrCBV), and normalized sodium measurements were quantified for each VOI.

RESULTS

Both relative sodium concentration and ADC were significantly higher in areas of necrosis compared to NET (P = 0.003 and P = 0.008, respectively) and CET (P = 0.02 and P = 0.02). Sodium concentration was higher in CET compared to NET (P = 0.04). Sodium and ADC were higher in treated compared to treatment-naïve gliomas within NET (P = 0.006 and P = 0.01, respectively), and ADC was elevated in CET (P = 0.03). Median ADC and sodium concentration were positively correlated across patients in NET (r = 0.77, P < 0.0001) and CET (r = 0.84, P < 0.0001), but not in areas of necrosis (r = 0.45, P = 0.12). Median nrCBV and sodium concentration were negatively correlated across patients in areas of NET (r=-0.63, P = 0.003). Similar associations were observed when examining voxel-wise correlations within VOIs.

CONCLUSION

Sodium MRI is positively correlated with proton diffusion MRI measurements in gliomas, likely reflecting extracellular water. Unique areas of multinuclear MRI contrast may be useful in future studies to understand the chemistry of the tumor microenvironment.

Imaging of pediatric brain tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee/ASPNR White Paper

Tumors of the central nervous system are the most common solid malignancies in children and the most common cause of pediatric cancer-related mortality. Imaging plays a central role in diagnosis, staging, treatment planning, and response assessment of pediatric brain tumors. However, the substantial variability in brain tumor imaging protocols across institutions leads to variability in patient risk stratification and treatment decisions, and complicates comparisons of clinical trial results. This White Paper provides consensus-based imaging recommendations for evaluating pediatric patients with primary brain tumors. The proposed brain magnetic resonance imaging protocol recommendations balance advancements in imaging techniques with the practicality of deployment across most imaging centers.

Prediction of response to lomustine-based chemotherapy in glioma patients at recurrence using MRI and FET PET

BACKGROUND

We evaluated O-(2-[18F]fluoroethyl)-l-tyrosine (FET) PET and MRI for early response assessment in recurrent glioma patients treated with lomustine-based chemotherapy.

METHODS

Thirty-six adult patients with WHO CNS grade 3 or 4 gliomas (glioblastoma, 69%) at recurrence (median number of recurrences, 1; range, 1-3) were retrospectively identified. Besides MRI, serial FET PET scans were performed at baseline and early after chemotherapy initiation (not later than two cycles). Tumor-to-brain ratios (TBR), metabolic tumor volumes (MTV), the occurrence of new distant hotspots with a mean TBR >1.6 at follow-up, and the dynamic parameter time-to-peak were derived from all FET PET scans. PET parameter thresholds were defined using ROC analyses to predict PFS of ≥6 months and OS of ≥12 months. MRI response assessment was based on RANO criteria. The predictive values of FET PET parameters and RANO criteria were subsequently evaluated using univariate and multivariate survival estimates.

RESULTS

After treatment initiation, the median follow-up time was 11 months (range, 3-71 months). Relative changes of TBR, MTV, and RANO criteria predicted a significantly longer PFS (all P ≤ .002) and OS (all P ≤ .045). At follow-up, the occurrence of new distant hotspots (n ≥ 1) predicted a worse outcome, with significantly shorter PFS (P = .005) and OS (P < .001). Time-to-peak changes did not predict a significantly longer survival. Multivariate survival analyses revealed that new distant hotspots at follow-up FET PET were most potent in predicting non-response (P < .001; HR, 8.578).

CONCLUSIONS

Data suggest that FET PET provides complementary information to RANO criteria for response evaluation of lomustine-based chemotherapy early after treatment initiation.

Validation of a highly accelerated post-contrast wave-controlled aliasing in parallel imaging (CAIPI) 3D-T1 MPRAGE compared to standard 3D-T1 MPRAGE for detection of intracranial enhancing lesions on 3-T MRI

OBJECTIVES

High-resolution post-contrast T1-weighted imaging is a workhorse sequence in the evaluation of neurological disorders. The T1-MPRAGE sequence has been widely adopted for the visualization of enhancing pathology in the brain. However, this three-dimensional (3D) acquisition is lengthy and prone to motion artifact, which often compromises diagnostic quality. The goal of this study was to compare a highly accelerated wave-controlled aliasing in parallel imaging (CAIPI) post-contrast 3D T1-MPRAGE sequence (Wave-T1-MPRAGE) with the standard 3D T1-MPRAGE sequence for visualizing enhancing lesions in brain imaging at 3 T.

METHODS

This study included 80 patients undergoing contrast-enhanced brain MRI. The participants were scanned with a standard post-contrast T1-MPRAGE sequence (acceleration factor [R] = 2 using GRAPPA parallel imaging technique, acquisition time [TA] = 5 min 18 s) and a prototype post-contrast Wave-T1-MPRAGE sequence (R = 4, TA = 2 min 32 s). Two neuroradiologists performed a head-to-head evaluation of both sequences and rated the visualization of enhancement, sharpness, noise, motion artifacts, and overall diagnostic quality. A 15% noninferiority margin was used to test whether post-contrast Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE. Inter-rater and intra-rater agreement were calculated. Quantitative assessment of CNR/SNR was performed.

RESULTS

Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE for delineating enhancing lesions with unanimous agreement in all cases between raters. Wave-T1-MPRAGE was noninferior in the perception of noise (p < 0.001), motion artifact (p < 0.001), and overall diagnostic quality (p < 0.001).

CONCLUSION

High-accelerated post-contrast Wave-T1-MPRAGE enabled a two-fold reduction in acquisition time compared to the standard sequence with comparable performance for visualization of enhancing pathology and equivalent perception of noise, motion artifacts and overall diagnostic quality without loss of clinically important information.

KEY POINTS

• Post-contrast wave-controlled aliasing in parallel imaging (CAIPI) T1-MPRAGE accelerated the acquisition of three-dimensional (3D) high-resolution post-contrast images by more than two-fold. • Post-contrast Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE with unanimous agreement between reviewers (100% in 80 cases) for the visualization of intracranial enhancing lesions. • Wave-T1-MPRAGE was equivalent to the standard sequence in the perception of noise in 94% (75 of 80) of cases and was preferred in 16% (13 of 80) of cases for decreased motion artifact.

Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives

Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.

The patients' experience of neuroimaging of primary brain tumors: a cross-sectional survey study

PURPOSE

To gain insight into how patients with primary brain tumors experience MRI, follow-up protocols, and gadolinium-based contrast agent (GBCA) use.

METHODS

Primary brain tumor patients answered a survey after their MRI exam. Questions were analyzed to determine trends in patients' experience regarding the scan itself, follow-up frequency, and the use of GBCAs. Subgroup analysis was performed on sex, lesion grade, age, and the number of scans. Subgroup comparison was made using the Pearson chi-square test and the Mann-Whitney U-test for categorical and ordinal questions, respectively.

RESULTS

Of the 100 patients, 93 had a histopathologically confirmed diagnosis, and seven were considered to have a slow-growing low-grade tumor after multidisciplinary assessment and follow-up. 61/100 patients were male, with a mean age ± standard deviation of 44 ± 14 years and 46 ± 13 years for the females. Fifty-nine patients had low-grade tumors. Patients consistently underestimated the number of their previous scans. 92% of primary brain tumor patients did not experience the MRI as bothering and 78% would not change the number of follow-up MRIs. 63% of the patients would prefer GBCA-free MRI scans if diagnostically equally accurate. Women found the MRI and receiving intravenous cannulas significantly more uncomfortable than men (p = 0.003). Age, diagnosis, and the number of previous scans had no relevant impact on the patient experience.

CONCLUSION

Patients with primary brain tumors experienced current neuro-oncological MRI practice as positive. Especially women would, however, prefer GBCA-free imaging if diagnostically equally accurate. Patient knowledge of GBCAs was limited, indicating improvable patient information.

Surgical management of Glioma Grade 4: technical update from the neuro-oncology section of the Italian Society of Neurosurgery (SINch®): a systematic review

PURPOSE

The extent of resection (EOR) is an independent prognostic factor for overall survival (OS) in adult patients with Glioma Grade 4 (GG4). The aim of the neuro-oncology section of the Italian Society of Neurosurgery (SINch®) was to provide a general overview of the current trends and technical tools to reach this goal.

METHODS

A systematic review was performed. The results were divided and ordered, by an expert team of surgeons, to assess the Class of Evidence (CE) and Strength of Recommendation (SR) of perioperative drugs management, imaging, surgery, intraoperative imaging, estimation of EOR, surgery at tumor progression and surgery in elderly patients.

RESULTS

A total of 352 studies were identified, including 299 retrospective studies and 53 reviews/meta-analysis. The use of Dexamethasone and the avoidance of prophylaxis with anti-seizure medications reached a CE I and SR A. A preoperative imaging standard protocol was defined with CE II and SR B and usefulness of an early postoperative MRI, with CE II and SR B. The EOR was defined the strongest independent risk factor for both OS and tumor recurrence with CE II and SR B. For intraoperative imaging only the use of 5-ALA reached a CE II and SR B. The estimation of EOR was established to be fundamental in planning postoperative adjuvant treatments with CE II and SR B and the stereotactic image-guided brain biopsy to be the procedure of choice when an extensive surgical resection is not feasible (CE II and SR B).

CONCLUSIONS

A growing number of evidences evidence support the role of maximal safe resection as primary OS predictor in GG4 patients. The ongoing development of intraoperative techniques for a precise real-time identification of peritumoral functional pathways enables surgeons to maximize EOR minimizing the post-operative morbidity.

Outcomes of intraventricular 131-I-omburtamab and external beam radiotherapy in patients with recurrent medulloblastoma and ependymoma

PURPOSE

Intraventricular compartmental radioimmunotherapy (cRIT) with 131-I-omburtamab is a potential therapy for recurrent primary brain tumors that can seed the thecal space. These patients often previously received external beam radiotherapy (EBRT) to a portion or full craniospinal axis (CSI) as part of upfront therapy. Little is known regarding outcomes after re-irradiation as part of multimodality therapy including cRIT. This study evaluates predictors of response, patterns of failure, and radiologic events after cRIT.

METHODS

Patients with recurrent medulloblastoma or ependymoma who received 131-I-omburtamab on a prospective clinical trial were included. Extent of disease at cRIT initiation (no evidence of disease [NED] vs measurable disease [MD]) was assessed as associated with progression-free (PFS) and overall survival (OS) by Kaplan-Meier analysis.

RESULTS

All 27 patients (20 medulloblastoma, 7 ependymoma) had EBRT preceding cRIT: most (22, 81%) included CSI (median dose 2340 cGy, boost to 5400 cGy). Twelve (44%) also received EBRT at relapse as bridging to cRIT. There were no cases of radionecrosis. At cRIT initiation, 11 (55%) medulloblastoma and 3 (43%) ependymoma patients were NED, associated with improved PFS (p = 0.002) and OS (p = 0.048) in medulloblastoma. Most relapses were multifocal. With medium follow-up of 3.0 years (95% confidence interval, 1.8-7.4), 6 patients remain alive with NED.

CONCLUSION

For patients with medulloblastoma, remission at time of cRIT was associated with significantly improved survival outcomes. Relapses are often multifocal, particularly in the setting of measurable disease at cRIT initiation. EBRT is a promising tool to achieve NED status at cRIT initiation, with no cases of radiation necrosis.

A multi-reader comparison of normal-appearing white matter normalization techniques for perfusion and diffusion MRI in brain tumors

PURPOSE

There remains no consensus normal-appearing white matter (NAWM) normalization method to compute normalized relative cerebral blood volume (nrCBV) and apparent diffusion coefficient (nADC) in brain tumors. This reader study explored nrCBV and nADC differences using different NAWM normalization methods.

METHODS

Thirty-five newly diagnosed glioma patients were studied. For each patient, two readers created four NAWM regions of interests: (1) a single plane in the centrum semiovale (CSOp), (2) 3 spheres in the centrum semiovale (CSOs), (3) a single plane in the slice of the tumor center (TUMp), and (4) 3 spheres in the slice of the tumor center (TUMs). Readers repeated NAWM segmentations 1 month later. Differences in nrCBV and nADC of the FLAIR hyperintense tumor, inter-/intra-reader variability, and time to segment NAWM were assessed. As a validation step, the diagnostic performance of each method for IDH-status prediction was evaluated.

RESULTS

Both readers obtained significantly different nrCBV (P < .001), nADC (P < .001), and time to segment NAWM (P < .001) between the four normalization methods. nrCBV and nADC were significantly different between CSO and TUM methods, but not between planar and spherical methods in the same NAWM region. Broadly, CSO methods were quicker than TUM methods, and spherical methods were quicker than planar methods. For all normalization techniques, inter-reader reproducibility and intra-reader repeatability were excellent (intraclass correlation coefficient > 0.9), and the IDH-status predictive performance remained similar.

CONCLUSION

The selected NAWM region significantly impacts nrCBV and nADC values. CSO methods, particularly CSOs, may be preferred because of time reduction, similar reader variability, and similar diagnostic performance compared to TUM methods.

Glioblastoma and Other Primary Brain Malignancies in Adults: A Review

Importance

Malignant primary brain tumors cause more than 15 000 deaths per year in the United States. The annual incidence of primary malignant brain tumors is approximately 7 per 100 000 individuals and increases with age. Five-year survival is approximately 36%.

Observations

Approximately 49% of malignant brain tumors are glioblastomas, and 30% are diffusely infiltrating lower-grade gliomas. Other malignant brain tumors include primary central nervous system (CNS) lymphoma (7%) and malignant forms of ependymomas (3%) and meningiomas (2%). Symptoms of malignant brain tumors include headache (50%), seizures (20%-50%), neurocognitive impairment (30%-40%), and focal neurologic deficits (10%-40%). Magnetic resonance imaging before and after a gadolinium-based contrast agent is the preferred imaging modality for evaluating brain tumors. Diagnosis requires tumor biopsy with consideration of histopathological and molecular characteristics. Treatment varies by tumor type and often includes a combination of surgery, chemotherapy, and radiation. For patients with glioblastoma, the combination of temozolomide with radiotherapy improved survival when compared with radiotherapy alone (2-year survival, 27.2% vs 10.9%; 5-year survival, 9.8% vs 1.9%; hazard ratio [HR], 0.6 [95% CI, 0.5-0.7]; P < .001). In patients with anaplastic oligodendroglial tumors with 1p/19q codeletion, probable 20-year overall survival following radiotherapy without vs with the combination of procarbazine, lomustine, and vincristine was 13.6% vs 37.1% (80 patients; HR, 0.60 [95% CI, 0.35-1.03]; P = .06) in the EORTC 26951 trial and 14.9% vs 37% in the RTOG 9402 trial (125 patients; HR, 0.61 [95% CI, 0.40-0.94]; P = .02). Treatment of primary CNS lymphoma includes high-dose methotrexate-containing regimens, followed by consolidation therapy with myeloablative chemotherapy and autologous stem cell rescue, nonmyeloablative chemotherapy regimens, or whole brain radiation.

Conclusions and Relevance

The incidence of primary malignant brain tumors is approximately 7 per 100 000 individuals, and approximately 49% of primary malignant brain tumors are glioblastomas. Most patients die from progressive disease. First-line therapy for glioblastoma is surgery followed by radiation and the alkylating chemotherapeutic agent temozolomide.

Synthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets

OBJECTIVE

Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically.

METHODS

In this work, we present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional network (FCN), which maintains high resolution information through processing and aggregates multi-scale information in parallel, is designed to map pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are utilized as inputs and the post-contrast T1 sequences are utilized as target output. To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors.

RESULTS

Extensive quantitative and visual assessments are performed, with our proposed model achieving a PSNR of 28.24 dB in the brain and 21.2 dB in tumor regions.

CONCLUSION AND SIGNIFICANCE

Our results suggest the potential of substituting GBCAs with synthetic contrast images generated via deep learning.

Recurrent Glioblastoma: Ongoing Clinical Challenges and Future Prospects

Virtually all glioblastomas treated in the first-line setting will recur in a short period of time, and the search for alternative effective treatments has so far been unsuccessful. Various obstacles remain unresolved, and no effective salvage therapy for recurrent glioblastoma can be envisaged in the short term. One of the main impediments to progress is the low incidence of the disease itself in comparison with other pathologies, which will be made even lower by the recent WHO classification of gliomas, which includes molecular alterations. This new classification helps refine patient prognosis but does not clarify the most appropriate treatment. Other impediments are related to clinical trials: glioblastoma patients are often excluded from trials due to their advanced age and limiting neurological symptoms; there is also the question of how best to measure treatment efficacy, which conditions the design of trials and can affect the acceptance of results by oncologists and medicine agencies. Other obstacles are related to the drugs themselves: most treatments cannot cross the blood-brain-barrier or the brain-to-tumor barrier to reach therapeutic drug levels in the tumor without producing toxicity; the drugs under study may have adverse metabolic interactions with those required for symptom control; identifying the target of the drug can be a complex issue. Additionally, the optimal method of treatment - local vs systemic therapy, the choice of chemotherapy, irradiation, targeted therapy, immunotherapy, or a combination thereof - is not yet clear in glioblastoma in comparison with other cancers. Finally, in addition to curing or stabilizing the disease, glioblastoma therapy should aim at maintaining the neurological status of the patients to enable them to return to their previous lifestyle. Here we review currently available treatments, obstacles in the search for new treatments, and novel lines of research that show promise for the future.

Challenges and opportunities for advanced neuroimaging of glioblastoma

Glioblastoma is the most aggressive of glial tumours in adults. On conventional magnetic resonance (MR) imaging, these tumours are observed as irregular enhancing lesions with areas of infiltrating tumour and cortical expansion. More advanced imaging techniques including diffusion-weighted MRI, perfusion-weighted MRI, MR spectroscopy and positron emission tomography (PET) imaging have found widespread application to diagnostic challenges in the setting of first diagnosis, treatment planning and follow-up. This review aims to educate readers with regard to the strengths and weaknesses of the clinical application of these imaging techniques. For example, this review shows that the (semi)quantitative analysis of the mentioned advanced imaging tools was found useful for assessing tumour aggressiveness and tumour extent, and aids in the differentiation of tumour progression from treatment-related effects. Although these techniques may aid in the diagnostic work-up and (post-)treatment phase of glioblastoma, so far no unequivocal imaging strategy is available. Furthermore, the use and further development of artificial intelligence (AI)-based tools could greatly enhance neuroradiological practice by automating labour-intensive tasks such as tumour measurements, and by providing additional diagnostic information such as prediction of tumour genotype. Nevertheless, due to the fact that advanced imaging and AI-diagnostics is not part of response assessment criteria, there is no harmonised guidance on their use, while at the same time the lack of standardisation severely hampers the definition of uniform guidelines.

Pre-contrast MAGiC in treated gliomas: a pilot study of quantitative MRI

Quantitative MR imaging is becoming more feasible to be used in clinical work since new approaches have been proposed in order to substantially accelerate the acquisition and due to the possibility of synthetically deriving weighted images from the parametric maps. However, their applicability has to be thoroughly validated in order to be included in clinical practice. In this pilot study, we acquired Magnetic Resonance Image Compilation scans to obtain T1, T2 and PD maps in 14 glioma patients. Abnormal tissue was segmented based on conventional images and using a deep learning segmentation technique to define regions of interest (ROIs). The quantitative T1, T2 and PD values inside ROIs were analyzed using the mean, the standard deviation, the skewness and the kurtosis and compared to the quantitative T1, T2 and PD values found in normal white matter. We found significant differences in pre-contrast T1 and T2 values between abnormal tissue and healthy tissue, as well as between T1w-enhancing and non-enhancing regions. ROC analysis was used to evaluate the potential of quantitative T1 and T2 values for voxel-wise classification of abnormal/normal tissue (AUC = 0.95) and of T1w enhancement/non-enhancement (AUC = 0.85). A cross-validated ROC analysis found high sensitivity (73%) and specificity (73%) with AUCs up to 0.68 on the a priori distinction between abnormal tissue with and without T1w-enhancement. These results suggest that normal tissue, abnormal tissue, and tissue with T1w-enhancement are distinguishable by their pre-contrast quantitative values but further investigation is needed.

Longitudinal MR spectroscopy to detect progression in patients with lower-grade glioma in the surveillance phase

Background

Monitoring lower-grade gliomas (LrGGs) for disease progression is made difficult by the limits of anatomical MRI to distinguish treatment related tissue changes from tumor progression. MR spectroscopic imaging (MRSI) offers additional metabolic information that can help address these challenges. The goal of this study was to compare longitudinal changes in multiparametric MRI, including diffusion weighted imaging, perfusion imaging, and 3D MRSI, for LrGG patients who progressed at the final time-point and those who remained clinically stable.

Methods

Forty-one patients with LrGG who were clinically stable were longitudinally assessed for progression. Changes in anatomical, diffusion, perfusion and MRSI data were acquired and compared between patients who remained clinically stable and those who progressed.

Results

Thirty-one patients remained stable, and 10 patients progressed. Over the study period, progressed patients had a significantly greater increase in normalized choline, choline-to-N-acetylaspartic acid index (CNI), normalized creatine, and creatine-to-N-acetylaspartic acid index (CRNI), than stable patients. CRNI was significantly associated with progression status and WHO type. Progressed astrocytoma patients had greater increases in CRNI than stable astrocytoma patients.

Conclusions

LrGG patients in surveillance with tumors that progressed had significantly increasing choline and creatine metabolite signals on MRSI, with a trend of increasing T2 FLAIR volumes, compared to LrGG patients who remained stable. These data show that MRSI can be used in conjunction with anatomical imaging studies to gain a clearer picture of LrGG progression, especially in the setting of clinical ambiguity.

Imaging biomarkers of TERT or GABPB1 silencing in TERT-positive glioblastoma

BACKGROUND

TERT promoter mutations are observed in 80% of wild-type IDH glioblastoma (GBM). Moreover, the upstream TERT transcription factor GABPB1 was recently identified as a cancer-specific therapeutic target for tumors harboring a TERT promoter mutation. In that context, noninvasive imaging biomarkers are needed for the detection of TERT modulation.

METHODS

Multiple GBM models were investigated as cells and in vivo tumors and the impact of TERT silencing, either directly or by targeting GABPB1, was determined using 1H and hyperpolarized 13C magnetic resonance spectroscopy (MRS). Changes in associated metabolic enzymes were also investigated.

RESULTS

1H-MRS revealed that lactate and glutathione (GSH) were the most significantly altered metabolites when either TERT or GABPB1 was silenced, and lactate and GSH levels were correlated with cellular TERT expression. Consistent with the drop in lactate, 13C-MRS showed that hyperpolarized [1-13C]lactate production from [1-13C]pyruvate was also reduced when TERT was silenced. Mechanistically, the reduction in GSH was associated with a reduction in pentose phosphate pathway flux, reduced activity of glucose-6-phosphate dehydrogenase, and reduced NADPH. The drop in lactate and hyperpolarized lactate were associated with reductions in glycolytic flux, NADH, and expression/activity of GLUT1, monocarboxylate transporters, and lactate dehydrogenase A.

CONCLUSIONS

Our study indicates that MRS-detectable GSH, lactate, and lactate production could serve as metabolic biomarkers of response to emerging TERT-targeted therapies for GBM with activating TERT promoter mutations. Importantly these biomarkers are readily translatable to the clinic, and thus could ultimately improve GBM patient management.

Advanced pediatric neuroimaging

Advanced magnetic resonance neuroimaging techniques play an important adjunct role to conventional MRI sequences for better depiction and characterization of a variety of brain disorders. In this article we briefly review the basic principles and clinical utility of a select number of these techniques, including clinical functional MRI for presurgical planning, clinical diffusion tensor imaging and related techniques, dynamic susceptibility contrast perfusion imaging using gadolinium injection, and arterial spin labeling perfusion imaging. The article focuses on general principles of clinical MRI acquisition protocols, relevant factors affecting image quality, and a general framework for obtaining images for each of these techniques. We also present relevant advances for acquiring these types of imaging sequences in a clinical setting.

Relapse patterns and radiation dose exposure in IDH wild-type glioblastoma at first radiographic recurrence following chemoradiation

PURPOSE

To quantify the radiation dose distribution and lesion morphometry (shape) at baseline, prior to chemoradiation, and at the time of radiographic recurrence in patients with glioblastoma (GBM).

METHODS

The IMRT dose distribution, location of the center of mass, sphericity, and solidity of the contrast enhancing tumor at baseline and the time of tumor recurrence was quantified in 48 IDH wild-type GBM who underwent postoperative IMRT (2 Gy daily for total of 60 Gy) with concomitant and adjuvant temozolomide.

RESULTS

Average radiation dose within enhancing tumor at baseline and recurrence was ≥ 60 Gy. Centroid location of the enhancing tumor shifted an average of 11.3 mm at the time of recurrence with respect to pre-IMRT location. A positive correlation was observed between change in centroid location and PFS in MGMT methylated patients (P = 0.0007) and Cox multivariate regression confirmed centroid distance from baseline was associated with PFS when accounting for clinical factors (P = 0.0189). Lesion solidity was higher at recurrence compared to baseline (P = 0.0118). Tumors that progressed > 12 weeks after IMRT were significantly more spherical (P = 0.0094).

CONCLUSION

Most GBMs recur local within therapeutic IMRT doses; however, tumors with longer PFS occurred further from the original tumor location and were more solid and/or nodular.

Alternative RNA splicing modulates ribosomal composition and determines the spatial phenotype of glioblastoma cells

Glioblastoma (GBM) is characterized by exceptionally high intratumoral heterogeneity. However, the molecular mechanisms underlying the origin of different GBM cell populations remain unclear. Here, we found that the compositions of ribosomes of GBM cells in the tumour core and edge differ due to alternative RNA splicing. The acidic pH in the core switches before messenger RNA splicing of the ribosomal gene RPL22L1 towards the RPL22L1b isoform. This allows cells to survive acidosis, increases stemness and correlates with worse patient outcome. Mechanistically, RPL22L1b promotes RNA splicing by interacting with lncMALAT1 in the nucleus and inducing its degradation. Contrarily, in the tumour edge region, RPL22L1a interacts with ribosomes in the cytoplasm and upregulates the translation of multiple messenger RNAs including TP53. We found that the RPL22L1 isoform switch is regulated by SRSF4 and identified a compound that inhibits this process and decreases tumour growth. These findings demonstrate how distinct GBM cell populations arise during tumour growth. Targeting this mechanism may decrease GBM heterogeneity and facilitate therapy.

Radiographic Response Assessment Strategies for Early-Phase Brain Trials in Complex Tumor Types and Drug Combinations: from Digital "Flipbooks" to Control Systems Theory

There is an urgent need for drug development in brain tumors. While current radiographic response assessment provides instructions for identifying large treatment effects in simple high- and low-grade gliomas, there remains a void of strategies to evaluate complex or difficult to measure tumors or tumors of mixed grade with enhancing and non-enhancing components. Furthermore, most patients exhibit some period of alteration in tumor growth after starting a new therapy, but simple response categorization (e.g., stable disease, progressive disease) fails to provide any meaningful insight into the depth or degree of potential "subclinical" therapeutic response. We propose a creative solution to these issues based on a tiered strategy meant to increase confidence in identifying therapeutic effects even in the most challenging tumor types, while also providing a framework for complex evaluation of combination and sequential treatment schemes. Specifically, we demonstrate the utility of digital "flipbooks" to quickly identify subtle changes in complex tumors. We show how a modified Levin criteria can be used to quantify the degree of visual changes, while establishing estimates of the association between tumor volume and visual inspection. Lastly, we introduce the concept of quantifying therapeutic response using control systems theory. We propose measuring changes in volume (proportional), the area under the volume vs. time curve (integral) and changes in growth rates (derivative) to utilize a "PID" controller model of single or combination therapeutic activity.

Updates in IDH-Wildtype Glioblastoma

Glioblastoma is the most aggressive primary brain tumor with a poor prognosis. The 2021 WHO CNS5 classification has further stressed the importance of molecular signatures in diagnosis although therapeutic breakthroughs are still lacking. In this review article, updates on the current and novel therapies in IDH-wildtype GBM will be discussed.

Diagnostic and Therapeutic Strategy in Anaplastic (Malignant) Meningioma, CNS WHO Grade 3

Simple Summary

Only 1% of all meningioma diagnosis is classified as malignant (anaplastic) meningioma. Due to their rarity, clinical management of these tumors presents several gaps. In this review, we investigate current knowledge of anaplastic meningioma focusing on their pathological and radiological diagnosis, molecular assessment, and loco-regional and systemic management. Despite the current marginal role of systemic therapy, it is possible that the increasing knowledge of molecular altered pathways of the disease will lead to the development of novel effective systemic treatments.

Abstract

Background: Meningiomas are the most common primary central nervous system malignancies accounting for 36% of all intracranial tumors. However, only 1% of meningioma is classified as malignant (anaplastic) meningioma. Due to their rarity, clinical management of these tumors presents several gaps. Methods: We carried out a narrative review aimed to investigate current knowledge of anaplastic meningioma focusing on their pathological and radiological diagnosis, molecular assessment, and loco-regional and systemic management. Results: The most frequent genetic alteration occurring in meningioma is the inactivation in the neurofibromatosis 2 genes (merlin). The accumulation of copy number losses, including 1p, 6p/q, 10q, 14q, and 18p/q, and less frequently 2p/q, 3p, 4p/q, 7p, 8p/q, and 9p, compatible with instability, is restricted to NF2 mutated meningioma. Surgery and different RT approaches represent the milestone of grade 3 meningioma management, while there is a marginal role of systemic therapy. Conclusions: Anaplastic meningiomas are rare tumors, and diagnosis should be suspected and confirmed by trained radiologists and pathologists. Despite the current marginal role of systemic therapy, it is possible that the increasing knowledge of molecular altered pathways of the disease will lead to the development of novel effective systemic treatments.

Assessment of higher-order singular value decomposition denoising methods on dynamic hyperpolarized [1-13C]pyruvate MRI data from patients with glioma

BACKGROUND

Real-time metabolic conversion of intravenously-injected hyperpolarized [1-13C]pyruvate to [1-13C]lactate and [13C]bicarbonate in the brain can be measured using dynamic hyperpolarized carbon-13 (HP-13C) MRI. However, voxel-wise evaluation of metabolism in patients with glioma is challenged by the limited signal-to-noise ratio (SNR) of downstream 13C metabolites, especially within lesions. The purpose of this study was to evaluate the ability of higher-order singular value decomposition (HOSVD) denoising methods to enhance dynamic HP [1-13C]pyruvate MRI data acquired from patients with glioma.

METHODS

Dynamic HP-13C MRI were acquired from 14 patients with glioma. The effects of two HOSVD denoising techniques, tensor rank truncation-image enhancement (TRI) and global-local HOSVD (GL-HOSVD), on the SNR and kinetic modeling were analyzed in [1-13C]lactate data with simulated noise that matched the levels of [13C]bicarbonate signals. Both methods were then evaluated in patient data based on their ability to improve [1-13C]pyruvate, [1-13C]lactate and [13C]bicarbonate SNR. The effects of denoising on voxel-wise kinetic modeling of kPL and kPB was also evaluated. The number of voxels with reliable kinetic modeling of pyruvate-to-lactate (kPL) and pyruvate-to-bicarbonate (kPB) conversion rates within regions of interest (ROIs) before and after denoising was then compared.

RESULTS

Both denoising methods improved metabolite SNR and regional signal coverage. In patient data, the average increase in peak dynamic metabolite SNR was 2-fold using TRI and 4-5 folds using GL-HOSVD denoising compared to acquired data. Denoising reduced kPL modeling errors from a native average of 23% to 16% (TRI) and 15% (GL-HOSVD); and kPB error from 42% to 34% (TRI) and 37% (GL-HOSVD) (values were averaged voxelwise over all datasets). In contrast-enhancing lesions, the average number of voxels demonstrating within-tolerance kPL modeling error relative to the total voxels increased from 48% in the original data to 84% (TRI) and 90% (GL-HOSVD), while the number of voxels showing within-tolerance kPB modeling error increased from 0% to 15% (TRI) and 8% (GL-HOSVD).

CONCLUSION

Post-processing denoising methods significantly improved the SNR of dynamic HP-13C imaging data, resulting in a greater number of voxels satisfying minimum SNR criteria and maximum kinetic modeling errors in tumor lesions. This enhancement can aid in the voxel-wise analysis of HP-13C data and thereby improve monitoring of metabolic changes in patients with glioma following treatment.

Differentiating solitary brain metastases from high-grade gliomas with MR: comparing qualitative versus quantitative diagnostic strategies

PURPOSE

To investigate the diagnostic efficacy of MRI diagnostic algorithms with an ascending automatization, in distinguishing between high-grade glioma (HGG) and solitary brain metastases (SBM).

METHODS

36 patients with histologically proven HGG (n = 18) or SBM (n = 18), matched by size and location were enrolled from a database containing 655 patients. Four different diagnostic algorithms were performed serially to mimic the clinical setting where a radiologist would typically seek out further findings to reach a decision: pure qualitative, analytic qualitative (based on standardized evaluation of tumor features), semi-quantitative (based on perfusion and diffusion cutoffs included in the literature) and a quantitative data-driven algorithm of the perfusion and diffusion parameters. The diagnostic yields of the four algorithms were tested with ROC analysis and Kendall coefficient of concordance.

RESULTS

Qualitative algorithm yielded sensitivity of 72.2%, specificity of 78.8%, and AUC of 0.75. Analytic qualitative algorithm distinguished HGG from SBM with a sensitivity of 100%, specificity of 77.7%, and an AUC of 0.889. The semi-quantitative algorithm yielded sensitivity of 94.4%, specificity of 83.3%, and AUC = 0.889. The data-driven algorithm yielded sensitivity = 94.4%, specificity = 100%, and AUC = 0.948. The concordance analysis between the four algorithms and the histologic findings showed moderate concordance for the first algorithm, (k = 0.501, P < 0.01), good concordance for the second (k = 0.798, P < 0.01), and third (k = 0.783, P < 0.01), and excellent concordance for fourth (k = 0.901, p < 0.0001).

CONCLUSION

When differentiating HGG from SBM, an analytical qualitative algorithm outperformed qualitative algorithm, and obtained similar results compared to the semi-quantitative approach. However, the use of data-driven quantitative algorithm yielded an excellent differentiation.

Salvage resection plus cesium-131 brachytherapy durably controls post-SRS recurrent brain metastases

BACKGROUND

Salvage of recurrent previously irradiated brain metastases (rBrM) is a significant challenge. Resection without adjuvant re-irradiation is associated with a high local failure rate, while reirradiation only partially reduces failure but is associated with greater radiation necrosis risk. Salvage resection plus Cs131 brachytherapy may offer dosimetric and biologic advantages including improved local control versus observation, with reduced normal brain dose versus re-irradiation, however data are limited.

METHODS

A prospective registry of consecutive patients with post-stereotactic radiosurgery (SRS) rBrM undergoing resection plus implantation of collagen-matrix embedded Cs131 seeds (GammaTile, GT Medical Technologies) prescribed to 60 Gy at 5 mm from the cavity was analyzed.

RESULTS

Twenty patients underwent 24 operations with Cs131 implantation in 25 tumor cavities. Median maximum preoperative diameter was 3.0 cm (range 1.1-6.3). Gross- or near-total resection was achieved in 80% of lesions. A median of 16 Cs131 seeds (range 6-30), with a median air-kerma strength of 3.5 U/seed were implanted. There was one postoperative wound dehiscence. With median follow-up of 1.6 years for survivors, two tumors recurred (one in-field, one marginal) resulting in 8.4% 1-year progression incidence (95%CI = 0.0-19.9). Radiographic seed settling was identified in 7/25 cavities (28%) 1.9-11.7 months post-implantation, with 1 case of distant migration (4%), without clinical sequelae. There were 8 cases of radiation necrosis, of which 4 were symptomatic.

CONCLUSIONS

With > 1.5 years of follow-up, intraoperative brachytherapy with commercially available Cs131 implants was associated with favorable local control and toxicity profiles. Weak correlation between preoperative tumor geometry and implanted tiles highlights a need to optimize planning criteria.

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.

AutoComBat: a generic method for harmonizing MRI-based radiomic features

The use of multicentric data is becoming essential for developing generalizable radiomic signatures. In particular, Magnetic Resonance Imaging (MRI) data used in brain oncology are often heterogeneous in terms of scanners and acquisitions, which significantly impact quantitative radiomic features. Various methods have been proposed to decrease dependency, including methods acting directly on MR images, i.e., based on the application of several preprocessing steps before feature extraction or the ComBat method, which harmonizes radiomic features themselves. The ComBat method used for radiomics may be misleading and presents some limitations, such as the need to know the labels associated with the "batch effect". In addition, a statistically representative sample is required and the applicability of a signature whose batch label is not present in the train set is not possible. This work aimed to compare a priori and a posteriori radiomic harmonization methods and propose a code adaptation to be machine learning compatible. Furthermore, we have developed AutoComBat, which aims to automatically determine the batch labels, using either MRI metadata or quality metrics as inputs of the proposed constrained clustering. A heterogeneous dataset consisting of high and low-grade gliomas coming from eight different centers was considered. The different methods were compared based on their ability to decrease relative standard deviation of radiomic features extracted from white matter and on their performance on a classification task using different machine learning models. ComBat and AutoComBat using image-derived quality metrics as inputs for batch assignment and preprocessing methods presented promising results on white matter harmonization, but with no clear consensus for all MR images. Preprocessing showed the best results on the T1w-gd images for the grading task. For T2w-flair, AutoComBat, using either metadata plus quality metrics or metadata alone as inputs, performs better than the conventional ComBat, highlighting its potential for data harmonization. Our results are MRI weighting, feature class and task dependent and require further investigations on other datasets.

The Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in the Differentiation of Pseudoprogression and Recurrence of Intracranial Gliomas

Objective

The objective of this study was to determine the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing postoperative changes in intracranial gliomas.

Method

A total of fifty-one patients who had new enhanced lesions after surgical resection followed by standard radiotherapy and chemotherapy were collected retrospectively from October 2014 to June 2021. The patients were divided into a pseudoprogression group (15 cases) and a recurrence group (36 cases) according to the pathological results of the second operation or a follow-up of more than six months. The follow-up data of all patients were complete, and DCE-MRI was performed. The images were processed to obtain the quantitative parameters K^trans, Ve, and Kep and the semiquantitative parameter iAUC, which were analysed with relevant statistical software.

Results

First, the difference in K^trans and iAUC values between the two groups was statistically significant (P < 0.05), and the difference in Ve and Kep values was not statistically significant (P > 0.05). Second, by comparing the area under the curve, threshold, sensitivity and specificity of K^trans, and iAUC, it was found that the iAUC threshold value was slightly higher than that of K^trans, and the specificity of K^trans was equal to that of iAUC, while the area under the curve and sensitivity of K^trans were higher than those of iAUC. Third, K^trans and iAUC had high accuracy in diagnosing recurrence and pseudoprogression, and K^trans had higher accuracy than iAUC.

Conclusion

In this study, DCE-MRI has a certain diagnostic value in the early differentiation of recurrence and pseudoprogression, offering a new method for the diagnosis and assessment of gliomas after surgery.

Practical Aspects of novel MRI Techniques in Neuroradiology: Part 2 - Acceleration Methods and Implications for Individual Regions

BACKGROUND

 Recently introduced MRI techniques facilitate accelerated examinations or increased resolution with the same duration. Further techniques offer homogeneous image quality in regions with anatomical transitions. The question arises whether and how these techniques can be adopted for routine diagnostic imaging.

METHODS

 Narrative review with an educational focus based on current literature research and practical experiences of different professions involved (physicians, MRI technologists/radiographers, physics/biomedical engineering). Different hardware manufacturers are considered.

RESULTS AND CONCLUSIONS

 Compressed sensing and simultaneous multi-slice imaging are novel acceleration techniques with different yet complimentary applications. They do not suffer from classical signal-to-noise-ratio penalties. Combining 3 D and acceleration techniques facilitates new broader examination protocols, particularly for clinical brain imaging. In further regions of the nervous systems mainly specific applications appear to benefit from recent technological improvements.

KEY POINTS

  · New acceleration techniques allow for faster or higher resolution examinations.. · New brain imaging approaches have evolved, including more universal examination protocols.. · Other regions of the nervous system are dominated by targeted applications of recently introduced MRI techniques..

CITATION FORMAT

· Sundermann B, Billebaut B, Bauer J et al. Practical Aspects of novel MRI Techniques in Neuroradiology: Part 2 - Acceleration Methods and Implications for Individual Regions. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1800-8789.