Neuroimaging in neuro-oncology

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.

Challenges in radiological evaluation of brain metastases, beyond progression

MRI is the cornerstone in the evaluation of brain metastases. The clinical challenges lie in discriminating metastases from mimickers such as infections or primary tumors and in evaluating the response to treatment. The latter sometimes leads to growth, which must be framed as pseudo-progression or radionecrosis, both inflammatory phenomena attributable to treatment, or be considered as recurrence. To meet these needs, imaging techniques are the subject of constant research. However, an exponential growth after radiotherapy must be interpreted with caution, even in the presence of results suspicious of tumor progression by advanced techniques, because it may be due to inflammatory changes. The aim of this paper is to familiarize the reader with inflammatory phenomena of brain metastases treated with radiotherapy and to describe two related radiological signs: "the inflammatory cloud" and "incomplete ring enhancement", in order to adopt a conservative management with close follow-up.

Advanced Hyperpolarized 13C Metabolic Imaging Protocol for Patients with Gliomas: A Comprehensive Multimodal MRI Approach

This study aimed to implement a multimodal 1H/HP-13C imaging protocol to augment the serial monitoring of patients with glioma, while simultaneously pursuing methods for improving the robustness of HP-13C metabolic data. A total of 100 1H/HP [1-13C]-pyruvate MR examinations (104 HP-13C datasets) were acquired from 42 patients according to the comprehensive multimodal glioma imaging protocol. Serial data coverage, accuracy of frequency reference, and acquisition delay were evaluated using a mixed-effects model to account for multiple exams per patient. Serial atlas-based HP-13C MRI demonstrated consistency in volumetric coverage measured by inter-exam dice coefficients (0.977 ± 0.008, mean ± SD; four patients/11 exams). The atlas-derived prescription provided significantly improved data quality compared to manually prescribed acquisitions (n = 26/78; p = 0.04). The water-based method for referencing [1-13C]-pyruvate center frequency significantly reduced off-resonance excitation relative to the coil-embedded [13C]-urea phantom (4.1 ± 3.7 Hz vs. 9.9 ± 10.7 Hz; p = 0.0007). Significantly improved capture of tracer inflow was achieved with the 2-s versus 5-s HP-13C MRI acquisition delay (p = 0.007). This study demonstrated the implementation of a comprehensive multimodal 1H/HP-13C MR protocol emphasizing the monitoring of steady-state/dynamic metabolism in patients with glioma.

Addressing blood-brain-tumor-barrier heterogeneity in pediatric brain tumors with innovative preclinical models

Brain tumors represent the leading cause of disease-related mortality and morbidity in children, with effective treatments urgently required. One factor limiting the effectiveness of systemic therapy is the blood-brain-barrier (BBB), which limits the brain penetration of many anticancer drugs. BBB integrity is often compromised in tumors, referred to as the blood-brain-tumor-barrier (BBTB), and the impact of a compromised BBTB on the therapeutic sensitivity of brain tumors has been clearly shown for a few selected agents. However, the heterogeneity of barrier alteration observed within a single tumor and across distinct pediatric tumor types represents an additional challenge. Herein, we discuss what is known regarding the heterogeneity of tumor-associated vasculature in pediatric brain tumors. We discuss innovative and complementary preclinical model systems that will facilitate real-time functional analyses of BBTB for all pediatric brain tumor types. We believe a broader use of these preclinical models will enable us to develop a greater understanding of the processes underlying tumor-associated vasculature formation and ultimately more efficacious treatment options.

Smart Visualization of Medical Images as a Tool in the Function of Education in Neuroradiology

The smart visualization of medical images (SVMI) model is based on multi-detector computed tomography (MDCT) data sets and can provide a clearer view of changes in the brain, such as tumors (expansive changes), bleeding, and ischemia on native imaging (i.e., a non-contrast MDCT scan). The new SVMI method provides a more precise representation of the brain image by hiding pixels that are not carrying information and rescaling and coloring the range of pixels essential for detecting and visualizing the disease. In addition, SVMI can be used to avoid the additional exposure of patients to ionizing radiation, which can lead to the occurrence of allergic reactions due to the contrast media administration. Results of the SVMI model were compared with the final diagnosis of the disease after additional diagnostics and confirmation by neuroradiologists, who are highly trained physicians with many years of experience. The application of the realized and presented SVMI model can optimize the engagement of material, medical, and human resources and has the potential for general application in medical training, education, and clinical research.

Differentiation of Intracerebral Tumor Entities with Quantitative Contrast Attenuation and Iodine Mapping in Dual-Layer Computed Tomography

Purpose: To investigate if quantitative contrast enhancement and iodine mapping of common brain tumor (BT) entities may correctly differentiate between tumor etiologies in standardized stereotactic CT protocols. Material and Methods: A retrospective monocentric study of 139 consecutive standardized dual-layer dual-energy CT (dlDECT) scans conducted prior to the stereotactic needle biopsy of untreated primary brain tumor lesions. Attenuation of contrast-enhancing BT was derived from polyenergetic images as well as spectral iodine density maps (IDM) and their contrast-to-noise-ratios (CNR) were determined using ROI measures in contrast-enhancing BT and healthy contralateral white matter. The measures were correlated to histopathology regarding tumor entity, isocitrate dehydrogenase (IDH) and MGMT mutation status. Results: The cohort included 52 female and 76 male patients, mean age of 59.4 (±17.1) years. Brain lymphomas showed the highest attenuation (IDM CNR 3.28 ± 1,23), significantly higher than glioblastoma (2.37 ± 1.55, p < 0.005) and metastases (1.95 ± 1.14, p < 0.02), while the differences between glioblastomas and metastases were not significant. These strongly enhancing lesions differed from oligodendroglioma and astrocytoma (Grade II and III) that showed IDM CNR in the range of 1.22−1.27 (±0.45−0.82). Conventional attenuation measurements in DLCT data performed equally or slightly superior to iodine density measurements. Conclusion: Quantitative attenuation and iodine density measurements of contrast-enhancing brain tumors are feasible imaging biomarkers for the discrimination of cerebral tumor lesions but not specifically for single tumor entities. CNR based on simple HU measurements performed equally or slightly superior to iodine quantification.

FLAIRectomy: Resecting beyond the Contrast Margin for Glioblastoma

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

18F-FSPG PET/CT Imaging of System xC- Transporter Activity in Patients with Primary and Metastatic Brain Tumors

Background The PET tracer (4S)-4-(3-[¹⁸F]fluoropropyl)-l-glutamate (¹⁸F-FSPG) targets the system x_C^- cotransporter, which is overexpressed in various tumors. Purpose To assess the role of ¹⁸F-FSPG PET/CT in intracranial malignancies. Materials and Methods Twenty-six patients (mean age, 54 years ± 12; 17 men; 48 total lesions) with primary brain tumors (n = 17) or brain metastases (n = 9) were enrolled in this prospective, single-center study (ClinicalTrials.gov identifier: NCT02370563) between November 2014 and March 2016. A 30-minute dynamic brain ¹⁸F-FSPG PET/CT scan and a static whole-body (WB) ¹⁸F-FSPG PET/CT scan at 60-75 minutes were acquired. Moreover, all participants underwent MRI, and four participants underwent fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET imaging. PET parameters and their relative changes were obtained for all lesions. Kinetic modeling was used to estimate the ¹⁸F-FSPG tumor rate constants using the dynamic and dynamic plus WB PET data. Imaging parameters were correlated to lesion outcomes, as determined with follow-up MRI and/or pathologic examination. The Mann-Whitney U test or Student t test was used for group mean comparisons. Receiver operating characteristic curve analysis was used for performance comparison of different decision measures. Results ¹⁸F-FSPG PET/CT helped identify all 48 brain lesions. The mean tumor-to-background ratio (TBR) on the whole-brain PET images at the WB time point was 26.6 ± 24.9 (range: 2.6-150.3). When ¹⁸F-FDG PET was performed, ¹⁸F-FSPG permitted visualization of non-¹⁸F-FDG-avid lesions or allowed better lesion differentiation from surrounding tissues. In participants with primary brain tumors, the predictive accuracy of the relative changes in influx rate constant K_i and maximum standardized uptake value to discriminate between poor and good lesion outcomes were 89% and 81%, respectively. There were significant differences in the ¹⁸F-FSPG uptake curves of lesions with good versus poor outcomes in the primary brain tumor group (P < .05) but not in the brain metastases group. Conclusion PET/CT imaging with (4S)-4-(3-[¹⁸F]fluoropropyl)-l-glutamate (¹⁸F-FSPG) helped detect primary brain tumors and brain metastases with a high tumor-to-background ratio. Relative changes in ¹⁸F-FSPG uptake with multi-time-point PET appear to be helpful in predicting lesion outcomes. Clinical trial registration no. NCT02370563 © RSNA, 2022 Online supplemental material is available for this article.

Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology

Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.

Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer

Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment.

A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type

The explosion of medical imaging data along with the advent of big data analytics has launched an exciting era for clinical research. One factor affecting the ability to aggregate large medical image collections for research is the lack of infrastructure for automated data annotation. Among all imaging modalities, annotation of magnetic resonance (MR) images is particularly challenging due to the non-standard labeling of MR image types. In this work, we aimed to train a deep neural network to annotate MR image sequence type for scans of brain tumor patients. We focused on the four most common MR sequence types within neuroimaging: T1-weighted (T1W), T1-weighted post-gadolinium contrast (T1Gd), T2-weighted (T2W), and T2-weighted fluid-attenuated inversion recovery (FLAIR). Our repository contains images acquired using a variety of pulse sequences, sequence parameters, field strengths, and scanner manufacturers. Image selection was agnostic to patient demographics, diagnosis, and the presence of tumor in the imaging field of view. We used a total of 14,400 two-dimensional images, each visualizing a different part of the brain. Data was split into train, validation, and test sets (9600, 2400, and 2400 images, respectively) and sets consisted of equal-sized groups of image types. Overall, the model reached an accuracy of 99% on the test set. Our results showed excellent performance of deep learning techniques in predicting sequence types for brain tumor MR images. We conclude deep learning models can serve as tools to support clinical research and facilitate efficient database management.

Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities

Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient's clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.

One-pot synthesis of carboxymethyl-dextran coated iron oxide nanoparticles (CION) for preclinical fMRI and MRA applications

Superparamagnetic iron-oxide nanoparticles are robust contrast agents for magnetic resonance imaging (MRI) used for sensitive structural and functional mapping of the cerebral blood volume (CBV) when administered intravenously. To date, many CBV-MRI studies are conducted with Feraheme, manufactured for the clinical treatment of iron-deficiency. Unfortunately, Feraheme is currently not available outside the United States due to commercial and regulatory constraints, making CBV-MRI methods either inaccessible or very costly to achieve. To address this barrier, we developed a simple, one-pot recipe to synthesize Carboxymethyl-dextran coated Iron Oxide Nanoparticles, namely, “CION”, suitable for preclinical CBV-MRI applications. Here we disseminate a step-by-step instruction of our one-pot synthesis protocol, which allows CION to be produced in laboratories with minimal cost. We also characterized different CION-conjugations by manipulating polymer to metal stoichiometric ratio in terms of their size, surface chemistry, and chemical composition, and shifts in MR relaxivity and pharmacokinetics. We performed several proof-of-concept experiments in vivo, demonstrating the utility of CION for functional and structural MRI applications, including hypercapnic CO₂ challenge, visual stimulation, targeted optogenetic stimulation, and microangiography. We also present evidence that CION can serve as a cross-modality research platform by showing concurrent in vivo optical and MRI measurement of CBV using fluorescent-labeled CION. The simplicity and cost-effectiveness of our one-pot synthesis method should allow researchers to reproduce CION and tailor the relaxivity and pharmacokinetics according to their imaging needs. It is our hope that this work makes CBV-MRI more openly available and affordable for a variety of research applications.

Patient-derived orthotopic xenograft models of medulloblastoma lack a functional blood-brain barrier

BACKGROUND

Novel targeted therapies for children diagnosed with medulloblastoma (MB), the most common malignant pediatric brain tumor, are urgently required. A major hurdle in the development of effective therapies is the impaired delivery of systemic therapies to tumor cells due to a specialized endothelial blood-brain barrier (BBB). Accordingly, the integrity of the BBB is an essential consideration in any preclinical model used for assessing novel therapeutics. This study sought to assess the functional integrity of the BBB in several preclinical mouse models of MB.

METHODS

Dynamic contrast enhancement magnetic resonance imaging (MRI) was used to evaluate blood-brain-tumor barrier (BBTB) permeability in a murine genetically engineered mouse model (GEMM) of Sonic Hedgehog (SHH) MB, patient-derived orthotopic xenograft models of MB (SHH and Gp3), and orthotopic transplantation of GEMM tumor cells, enabling a comparison of the direct effects of transplantation on the integrity of the BBTB. Immunofluorescence analysis was performed to compare the structural and subcellular features of tumor-associated vasculature in all models.

RESULTS

Contrast enhancement was observed in all transplantation models of MB. No contrast enhancement was observed in the GEMM despite significant tumor burden. Cellular analysis of BBTB integrity revealed aberrancies in all transplantation models, correlating to the varying levels of BBTB permeability observed by MRI in these models.

CONCLUSIONS

These results highlight functional differences in the integrity of the BBTB and tumor vessel phenotype between commonly utilized preclinical models of MB, with important implications for the preclinical evaluation of novel therapeutic agents for MB.

The Management of Brain Metastases-Systematic Review of Neurosurgical Aspects

The multidisciplinary management of patients with brain metastases (BM) consists of surgical resection, different radiation treatment modalities, cytotoxic chemotherapy, and targeted molecular treatment. This review presents the current state of neurosurgical technology applied to achieve maximal resection with minimal morbidity as a treatment paradigm in patients with BM. In addition, we discuss the contribution of neurosurgical resection on functional outcome, advanced systemic treatment strategies, and enhanced understanding of the tumor biology.

Invasive growth associated with Cold-Inducible RNA-Binding Protein expression drives recurrence of surgically resected brain metastases

BACKGROUND

Sixty percent of surgically resected brain metastases (BrM) recur within 1 year. These recurrences have long been thought to result from the dispersion of cancer cells during surgery. We tested the alternative hypothesis that invasion of cancer cells into the adjacent brain plays a significant role in local recurrence and shortened overall survival.

METHODS

We determined the invasion pattern of 164 surgically resected BrM and correlated with local recurrence and overall survival. We performed single-cell RNA sequencing (scRNAseq) of >15,000 cells from BrM and adjacent brain tissue. Validation of targets was performed with a novel cohort of BrM patient-derived xenografts (PDX) and patient tissues.

RESULTS

We demonstrate that invasion of metastatic cancer cells into the adjacent brain is associated with local recurrence and shortened overall survival. scRNAseq of paired tumor and adjacent brain samples confirmed the existence of invasive cancer cells in the tumor-adjacent brain. Analysis of these cells identified Cold-Inducible RNA-Binding Protein (CIRBP) overexpression in invasive cancer cells compared to cancer cells located within the metastases. Applying PDX models that recapitulate the invasion pattern observed in patients, we show that CIRBP is overexpressed in highly invasive BrM and is required for efficient invasive growth in the brain.

CONCLUSIONS

These data demonstrate peritumoral invasion as a driver of treatment failure in BrM that is functionally mediated by CIRBP. These findings improve our understanding of the biology underlying post-operative treatment failure and lay the groundwork for rational clinical trial development based upon invasion pattern in surgically resected brain metastases.

The Use of Treatment Response Assessment Maps in Discriminating Between Radiation Effect and Persistent Tumoral Lesion in Metastatic Brain Tumors Treated with Gamma Knife Radiosurgery

BACKGROUND

Traditional imaging modalities are not useful in the follow-up of irradiated metastatic brain tumors, because radiation can change imaging characteristics. We aimed to assess the ability of treatment response assessment maps (TRAMs) calculated from delayed-contrast magnetic resonance imaging (MRI) in differentiation between radiation effect and persistent tumoral tissue.

METHODS

TRAMs were calculated by subtracting three-dimensional T1 MRIs acquired 5 minutes after contrast injection from the images acquired 60-105 minutes later. Red areas were regarded as radiation effect and blue areas as persistent tumoral lesion. Thirty-seven patients with 130 metastatic brain tumors who were treated with Gamma Knife radiosurgery and who underwent TRAMs perfusion-weighted MRI were enrolled in this retrospective study.

RESULTS

The median age was 58 years and the most common primary diagnosis was lung cancer (n = 21). The median follow-up period of patients was 12 months. The overall local control rate was 100% at 1 year and 98.9% at 2 years. The median progression-free survival was 12 months. The mean overall survival was 27.3 months. The radiologic and clinical follow-up showed a clinicoradiologic diagnosis of a persistent tumoral lesion in 3 tumors (2.3%) and radiation effect in 127 tumors (97.7%). There was a fair agreement between clinicoradiologic diagnosis and TRAMs analysis (κ = 0.380). The sensitivity and positive predictive value of TRAMs in diagnosing radiation effect were 96.06% and 99.2%, respectively. TRAMs showed comparable results to perfusion-weighted MRI, with a diagnostic odds ratio of 27.4 versus 20.7, respectively.

CONCLUSIONS

The presented results show the ability of TRAMs in differentiating radiation effect and persistent tumoral lesions.

Perioperative imaging in patients treated with resection of brain metastases: a survey by the European Association of Neuro-Oncology (EANO) Youngsters committee

BACKGROUND

Neurosurgical resection represents an important treatment option in the modern, multimodal therapy approach of brain metastases (BM). Guidelines for perioperative imaging exist for primary brain tumors to guide postsurgical treatment. Optimal perioperative imaging of BM patients is so far a matter of debate as no structured guidelines exist.

METHODS

A comprehensive questionnaire about perioperative imaging was designed by the European Association of Neuro-Oncology (EANO) Youngsters Committee. The survey was distributed to physicians via the EANO network to perform a descriptive overview on the current habits and their variability on perioperative imaging. Chi square test was used for dichotomous variables.

RESULTS

One hundred twenty physicians worldwide responded to the survey. MRI was the preferred preoperative imaging method (93.3%). Overall 106/120 (88.3%) physicians performed postsurgical imaging routinely including MRI alone (62/120 [51.7%]), postoperative CT (29/120 [24.2%]) and MRI + CT (15/120 [12.5%]). No correlation of postsurgical MRI utilization in academic vs. non-academic hospitals (58/89 [65.2%] vs. 19/31 [61.3%], p = 0.698) was found. Early postoperative MRI within ≤72 h after resection is obtained by 60.8% of the participants. The most frequent reason for postsurgical imaging was to evaluate the extent of tumor resection (73/120 [60.8%]). In case of residual tumor, 32/120 (26.7%) participants indicated to adjust radiotherapy, 34/120 (28.3%) to consider re-surgery to achieve complete resection and 8/120 (6.7%) to evaluate both.

CONCLUSIONS

MRI was the preferred imaging method in the preoperative setting. In the postoperative course, imaging modalities and timing showed high variability. International guidelines for perioperative imaging with special focus on postoperative MRI to assess residual tumor are warranted to optimize standardized management and adjuvant treatment decisions for BM patients.

Reference values of physiological 18F-FET uptake: Implications for brain tumor discrimination

Purpose

The aim of this study was to derive reference values of 18F-fluoro-ethyl-L-tyrosine positron emission tomography (18F-FET-PET) uptake in normal brain and head structures to allow for differentiation from tumor tissue.

Materials and methods

We examined the datasets of 70 patients (median age 53 years, range 15–79), whose dynamic 18F-FET-PET was acquired between January 2016 and October 2017. Maximum standardized uptake value (SUVmax), target-to-background standardized uptake value ratio (TBR), and time activity curve (TAC) of the 18F-FET-PET were assessed in tumor tissue and in eight normal anatomic structures and compared using the t-test and Mann-Whitney U-test. Correlation analyses were performed using Pearson or Spearman coefficients, and comparisons between several variables with Pearson’s chi-squared tests and Kruskal-Wallis tests as well as the Benjamini-Hochberg correction.

Results

All analyzed structures showed an 18F-FET uptake higher than background (threshold: TBR > 1.5). The venous sinuses and cranial muscles exhibited a TBR of 2.03±0.46 (confidence interval (CI) 1.92–2.14), higher than the uptake of caudate nucleus, pineal gland, putamen, and thalamus (TBR 1.42±0.17, CI 1.38–1.47). SUVmax, TBR, and TAC showed no difference in the analyzed structures between subjects with high-grade gliomas and subjects with low-grade gliomas, except the SUV_max of the pineal gland (t-tests of the pineal gland: SUVmax: p = 0.022; TBR: p = 0.411). No significant differences were found for gender and age.

Conclusion

Normal brain tissue demonstrates increased 18F-FET uptake compared to background tissue. Two distinct clusters have been identified, comprising venous structures and gray matter with a reference uptake of up to SUV_max of 2.99 and 2.33, respectively.

Differentiation among Glioblastomas, Primary Cerebral Lymphomas, and Solitary Brain Metastases Using Diffusion-Weighted Imaging and Diffusion Tensor Imaging: A PRISMA-Compliant Meta-analysis

Previous studies showed a high diagnostic value of diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) in differentiation among glioblastomas, primary cerebral lymphomas (PCLs), and solitary brain metastases, whereas other studies reported a low or no diagnostic value of DWI and DTI in differentiation among the three types of brain malignant tumors. In order to enhance the strength of evidence, meta-analysis was conducted to summarize results of studies evaluating the diagnostic values of DWI or DTI in differentiation among the three types of brain malignant tumors. Articles evaluating the diagnostic values of DWI or DTI in differentiation among the three types of tumors and published before December 2019 were searched in databases (PubMed, Medline, Web of Science, EMBASE, and Google Scholar). A summary of sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), and diagnostic odds ratio (DOR) were calculated from the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) of each study using STATA 12.0 software and Meta-Disc Version 1.4. In addition, the summary receive-operating characteristic (SROC) curve was constructed. Ultimately, we included 19 diagnostic studies (including 735 glioblastomas patients, 31 PCLs patients, and 792 patients with solitary brain metastases). Regarding differentiation between glioblastomas and solitary brain metastases using DWI or DTI, the calculated pooled parameters were as follows: sensitivity, 0.84 [95% confidence interval (CI): 0.78-0.89]; specificity, 0.88 (95% CI: 0.83-0.92); PLR, 7.2 (95% CI: 4.6-11.3); NLR, 0.18 (95% CI: 0.12-0.27); and DOR, 41 (95% CI: 18-93). The analysis showed a significant heterogeneity (sensitivity, I² = 91.31%, p < 0.01; specificity, I² = 89.24%, p < 0.01). In conclusion, DWI and DTI showed a moderate diagnostic value for differentiating glioblastomas from solitary brain metastasis. Additionally, large-scale prospective studies are essential to explore differentiation between PCLs and solitary brain metastases using DWI or DTI.

Anatomical relationships between medullary veins and three types of deep-seated malignant brain tumors as detected by susceptibility-weighted imaging

BACKGROUND

Deep-seated brain tumors can be difficult to differentiate. Three tumor types (primary central nervous system lymphoma [PCNSL], high-grade glioma, and metastatic brain tumors), identified by susceptibility-weighted imaging, have different relationships with small medullary veins, and these relationships can be used to enhance diagnostic accuracy.

METHODS

Records of patients with pathology confirmed malignant brain tumors who received susceptibility-weighted imaging between 2009 and 2015 were reviewed. A total of 29 patients with deep-seated malignant brain tumors in the territory of small medullary veins were enrolled in this study. The sensitivity, specificity, and diagnostic accuracy of medullary vein blockage (MVB), defined as a small medullary vein terminating at the margin of the tumor, for indicating malignant brain tumors were analyzed.

RESULTS

Of 11 patients with PCNSLs, 5 with high-grade gliomas, and 13 with metastases, only the latter presented MVBs. The sensitivity, specificity, and accuracy of using MVBs for diagnosing metastatic tumors were 76.9%, 100%, and 89.7%, respectively.

CONCLUSION

An MVB is an accurate sign for differentiating metastatic brain tumors from two other common malignancies and thus provides a useful tool for preoperative planning.

Investigating dynamic susceptibility contrast-enhanced perfusion-weighted magnetic resonance imaging in posterior fossa tumors: differences and similarities with supratentorial tumors

PURPOSE

To assess the accuracy of dynamic susceptibility contrast-enhanced perfusion-weighted magnetic resonance imaging in glioma grading and brain tumor characterization of infratentorial tumors, and to investigate differences from supratentorial tumors.

METHODS

This retrospective study, approved by the institutional review board, included 246 patients with brain tumors (184 supratentorial, 62 infratentorial), grouped by tumor type: high-grade gliomas (HGG), low-grade gliomas (LGG), metastases (Met), and primary central nervous system lymphoma (PCNSL). Relative cerebral blood volume (rCBV) and mean percentage of signal recovery (PSR) were calculated. For statistical analyses, lesions were grouped by location and histology. Differences were tested with Mann-Whitney U tests. From ROC curves, we calculated accuracy, sensitivity, specificity, PPV, and NPV, for rCBV and PSR.

RESULTS

For infratentorial tumors, rCBV was highly accurate in differentiating HGG from LGG (AUC = 0.938). Mean PSR showed high accuracy in differentiating PCNSL and HGG from Met (AUC = 0.978 and AUC = 0.881, respectively). Infratentorial and supratentorial tumors had similarly high rCBV in HGG, high mean PSR in PCNSL, and low mean PSR in Met. The main differences were the optimum threshold rCBV values (3.04 for supratentorial, 1.77 for infratentorial tumors) and the mean PSR, which was significantly higher in LGG than in HGG in supratentorial (p = 0.035), but not infratentorial gliomas. Using infratentorial rCBV threshold values for supratentorial tumors decreased the sensitivity and specificity.

CONCLUSION

rCBV and mean PSR were useful in grading and differentiating infratentorial tumors. Proper cutoff values were important in the accuracy of perfusion-weighted imaging in posterior fossa tumors.

18F-Fluciclovine PET/CT in Suspected Residual or Recurrent High-Grade Glioma

PURPOSE

To retrospectively investigate the uptake of F-fluciclovine on PET/CT in patients with suspected recurrent high-grade glioma (HGG).

METHODS

Twenty-one patients were included. The standard of truth was histopathologic interpretation if available. When histopathology was not available or rebiopsy did not show signs of malignancy, clinical follow-up including MRI and clinical outcome was considered the standard of truth.

RESULTS

All 21 patients met the reference standard of either histopathologic proof of HGG recurrence (n = 10) or disease progression clinically and with tumor growth corresponding to the primary tumor sites on follow-up MRI (n = 11). Median time from PET/CT to death was 5 months (range, 1-20 months). Median time from primary diagnosis to death was 14.5 months (range, 6 to >400). Average SUVmax of the lesions was 8.3 ± 5.3 (SD) and 0.34 ± 0.13 for normal brain tissue. Median lesion-to-background ratio was 21.6 (range, 3.1-84.4). In 4 patients, F-fluciclovine PET/CT detected small satellite tumors that had not been reported on MR.

CONCLUSIONS

The uptake of F-fluciclovine in clinically and/or histopathologically confirmed recurrent HGG is high compared with the uptake reported for other amino acid PET tracers. Because of the high tumor uptake and thus high tracer contrast, small satellite tumors with a diameter below usual reported PET spatial resolution and not reported on MRI were detected in 4 patients. As no patients with confirmed treatment-related changes were included, we cannot as of yet ascertain the ability of F-fluciclovine PET to discriminate between recurrent HGG and treatment-related changes, for example, pseudoprogression and radionecrosis.

Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging

Background

Differentiating glioblastoma, brain metastasis, and central nervous system lymphoma (CNSL) on conventional magnetic resonance imaging (MRI) can present a diagnostic dilemma due to the potential for overlapping imaging features. We investigate whether machine learning evaluation of multimodal MRI can reliably differentiate these entities.

Methods

Preoperative brain MRI including diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE), and dynamic susceptibility contrast (DSC) perfusion in patients with glioblastoma, lymphoma, or metastasis were retrospectively reviewed. Perfusion maps (rCBV, rCBF), permeability maps (K-trans, Kep, Vp, Ve), ADC, T1C+ and T2/FLAIR images were coregistered and two separate volumes of interest (VOIs) were obtained from the enhancing tumor and non-enhancing T2 hyperintense (NET2) regions. The tumor volumes obtained from these VOIs were utilized for supervised training of support vector classifier (SVC) and multilayer perceptron (MLP) models. Validation of the trained models was performed on unlabeled cases using the leave-one-subject-out method. Head-to-head and multiclass models were created. Accuracies of the multiclass models were compared against two human interpreters reviewing conventional and diffusion-weighted MR images.

Results

Twenty-six patients enrolled with histopathologically-proven glioblastoma (n=9), metastasis (n=9), and CNS lymphoma (n=8) were included. The trained multiclass ML models discriminated the three pathologic classes with a maximum accuracy of 69.2% accuracy (18 out of 26; kappa 0.540, P=0.01) using an MLP trained with the VpNET2 tumor volumes. Human readers achieved 65.4% (17 out of 26) and 80.8% (21 out of 26) accuracies, respectively. Using the MLP VpNET2 model as a computer-aided diagnosis (CADx) for cases in which the human reviewers disagreed with each other on the diagnosis resulted in correct diagnoses in 5 (19.2%) additional cases.

Conclusions

Our trained multiclass MLP using VpNET2 can differentiate glioblastoma, brain metastasis, and CNS lymphoma with modest diagnostic accuracy and provides approximately 19% increase in diagnostic yield when added to routine human interpretation.

Differentiation between glioblastomas and brain metastases and regarding their primary site of malignancy using dynamic susceptibility contrast MRI at 3T

BACKGROUND

Differentiation between glioblastoma and brain metastasis may be challenging in conventional contrast-enhanced MRI.

PURPOSE

To investigate if perfusion-weighted MRI is able to differentiate glioblastoma from metastasis and, as a second aim was to see if it was possible in the latter group, to predict the primary site of neoplasm.

MATERIAL AND METHODS

Hundred and fourteen patients with newly discovered tumor lesion (76 metastases and 38 glioblastomas) underwent conventional contrast-enhanced MRI including dynamic susceptibility contrast perfusion sequence. The calculated relative cerebral blood volumes were analyzed in the solid tumor area, peritumoral area, area adjacent to peritumoral area, and normal appearing white matter in contralateral semioval center. The Student t-test was used to detect statistically significant differences in relative cerebral blood volume between glioblastomas and metastases in the aforementioned areas. Furthermore, the metastasis group was divided in four sub groups (lung-, breast-, melanoma-, and gastrointestinal origin) and using one-way ANOVA test. P-values < 0.05 were considered significant.

RESULTS

Relative cerebral blood volume (rCBV) in the peritumoral edema was significantly higher in glioblastomas than in metastases (mean 3.2 ± 1.4 and mean 0.9 ± 0.7), respectively, (P < 0.0001). No significant differences in the solid tumor area or the area adjacent to edema were found, (P = 0.28 and 0.21 respectively). There were no significant differences among metastases in the four groups.

CONCLUSION

It is possible to differentiate glioblastomas from metastases by measuring the CBV in the peritumoral edema. It is not possible to differentiate between brain metastases from different primaries (lung-, breast-, melanoma or gastrointestinal) using CBV-measurements in the solid tumor area, peritumoral edema or area adjacent to edema.

The role of 13N-ammonia in the differential diagnosis of gliomas and brain inflammatory lesions

OBJECTIVE

To investigate the utility of ¹³N-ammonia PET/CT imaging in the differential diagnosis of gliomas and brain inflammations.

METHODS

¹³N-ammonia PET/CT imaging data of 77 patients with gliomas and 34 patients with brain inflammations were retrospectively analyzed. No patients received any treatment before ¹³N-ammonia imaging. All the patients were diagnosed by stereotactic biopsy or clinical follow-up. Visual and semi-quantitative analysis was performed to analyze the results of ¹³N-ammonia imaging. Finally, the uptake ratios of each lesion were calculated and its differences among different groups were tested with one-way ANOVA.

RESULTS

29.4% inflammations, 51.6% low-grade gliomas and 91.3% high-grade gliomas were positive by visual analysis in ¹³N-ammonia imaging. The sensitivity, specificity and accuracy for the diagnosis of gliomas were 75.3%, 55.8% and 67.8%, respectively. As for semi-quantitative analysis, the T/G ratios of inflammatory lesions, low-grade gliomas and high-grade gliomas were 0.88 ± 0.24, 1.04 ± 0.43 and 1.43 ± 0.49, respectively. One-way ANOVA revealed that the T/G ratios of high-grade gliomas were significantly higher than those of low-grade gliomas and inflammations (P < 0.05), but there was no statistical difference between low-grade gliomas and inflammations (P = 0.118). Among the inflammatory lesions, T/G ratios were not statistically different between infectious and demyelinating lesions (P > 0.05). ROC curve analysis showed that the optimal cut-off value of T/G ratio in distinguishing gliomas from inflammations was 1.21 with the AUC 0.78. The sensitivity, specificity, accuracy, PPV and NPV were 52.9%, 94.4%, 65.3%, 95.7% and 45.9%, respectively. ROC curve analysis showed that the optimal cut-off value of T/G ratio in distinguishing high-grade gliomas from low-grade gliomas was 1.06 with the AUC 0.78. The sensitivity, specificity, accuracy, PPV and NPV were 81.5%, 67.7%, 76.5%, 81.5% and 67.7%, respectively. ROC curve analysis showed that the optimal cut-off value of T/G ratio in distinguishing high-grade gliomas from low-grade gliomas and inflammations was 1.19 with the AUC 0.84. The sensitivity, specificity, accuracy, PPV and NPV were 70.4%, 85.1%, 78.5%, 79.2% and 78.1%, respectively.

CONCLUSIONS

¹³N-ammonia imaging is effective in distinguishing high-grade gliomas from low-grade gliomas and inflammations, but its role in the differential diagnosis of low-grade gliomas and brain inflammatory lesions is limited, and the accuracy needs to be improved.

Diagnosis and Management of Radiation Necrosis in Patients With Brain Metastases

The use of radiotherapy, either in the form of stereotactic radiosurgery (SRS) or whole-brain radiotherapy (WBRT), remains the cornerstone for the treatment of brain metastases (BM). As the survival of patients with BM is being prolonged, due to improved systemic therapy (i.e., for better extra-cranial control) and increased use of SRS (i.e., for improved intra-cranial control), patients are clinically manifesting late effects of radiotherapy. One of these late effects is radiation necrosis (RN). Unfortunately, symptomatic RN is notoriously hard to diagnose and manage. The features of RN overlap considerably with tumor recurrence, and misdiagnosing RN as tumor recurrence may lead to deleterious treatment which may cause detrimental effects to the patient. In this review, we will explore the pathophysiology of RN, risk factors for its development, and the strategies to evaluate and manage RN.

Advances in Neuro-Oncology Imaging Techniques

Background

Central nervous system (CNS) tumors are a rare but devastating malignancy, often robbing patients of the basic quality of life. Despite advances in our understanding of the CNS tumor disease processes, the prognosis for patients with CNS tumors remains poor. Better characterization and diagnostic and monitoring approaches are necessary to assist in diagnosis and treatment of CNS tumors. One important tool in the neuro-oncology armamentarium is the use of advanced imaging techniques.

Methods

We searched PubMed using the keywords neuro-oncology imaging, pseudoprogression, molecular imaging, and biomarkers. We limited our search to full-text English articles and identified other relevant articles from the reference lists of previously identified articles.

Results

Advances in imaging techniques have allowed investigators to explore various imaging modalities, from tumor characterization to differentiating pseudoprogression from tumor progression. Better imaging can result in better diagnostic approaches, greater and safer resection techniques, and improved monitoring of tumor progression.

Conclusion

This review highlights advances in neuro-oncology imaging techniques and their clinical utility in the treatment and management of primary brain tumors.

Cluster versus ROI analysis to assess combined antiangiogenic therapy and radiotherapy in the F98 rat-glioma model

For glioblastoma (GBM), current therapeutic approaches focus on the combination of several therapies, each of them individually approved for GBM or other tumor types. Many efforts are made to decipher the best sequence of treatments that would ultimately promote the most efficient tumor response. There is therefore a strong interest in developing new clinical in vivo imaging procedures that can rapidly detect treatment efficacy and allow individual modulation of the treatment. In this preclinical study, we propose to evaluate tumor tissue changes under combined therapies, tumor vascular normalization under antiangiogenic treatment followed by radiotherapy, using a voxel-based clustering approach. This approach was applied to a rat model of glioma (F98). Six MRI parameters were mapped: apparent diffusion coefficient, vessel wall permeability, cerebral blood volume fraction, cerebral blood flow, tissue oxygen saturation and vessel size index. We compared the classical region of interest (ROI)-based analysis with a cluster-based analysis. Five clusters, defined by their MRI features, were sufficient to characterize tumor progression and tumor changes during treatments. These results suggest that the cluster-based analysis was as efficient as the ROI-based analysis to assess tumor physiological changes during treatment, but also gave additional information regarding the voxels impacted by treatments and their localization within the tumor. Overall, cluster-based analysis appears to be a powerful tool for subtle monitoring of tumor changes during combined therapies.

CG, Dutra C, Ferreira RM, Linhares MN, Denyak V. Evaluation of high-grade astrocytoma recurrence patterns after radiotherapy in the era of temozolomide: A single institution experience

AIM

Evaluating the recurrence patterns of high-grade astrocytomas in patients who were treated with radiotherapy (RT) plus temozolomide (TMZ).

BACKGROUND

The current literature suggests that reducing the margins added to the CTV does not significantly change the risk of recurrence and overall survival; thus, we decided to analyze our data and to examine the possibility of changing the adopted margins.

MATERIALS AND METHODS

From February 2008 till September 2013, 55 patients were treated for high-grade astrocytomas, 20 patients who had been confirmed to have recurrence were selected for the present study. Post-operative MRI was superimposed on the planning CT images in order to correlate the anatomical structures with the treatment targets. Recurrences were defined according to the Response Assessment Criteria for Glioblastoma. The mean margins of the PTVinitial and PTVboost were 1.2 cm and 1.4 cm, respectively. The analysis of the percentage of the recurrence volume (Volrec) within the 100% isodose surface was based on the following criteria: (I) Central: >95% of the Volrec; (II) In-field: 81-95% of the Volrec; (III) Marginal: 20-80% of the Volrec; and (IV) Outside: <20% of the Volrec.

RESULTS

Of the 20 patients, 13 presented with central recurrences, 3 with in-field recurrences, 2 with marginal recurrences and 2 with outside recurrences. Therefore, the lower Volrec within 100% of the prescribed dose was considered in the classification.

CONCLUSIONS

Of the selected patients, 80% had ≥81-95% of the Volrec within 100% of the prescribed dose and predominantly had central or in-field recurrences. These results are comparable with those from the literature.

Neuroradiologic Pearls for Neuro-oncology

PURPOSE OF REVIEW

This article reviews key principles in the identification of tumors of the central nervous system (CNS) using standard and advanced imaging modalities. This article highlights the pitfalls and pearls of the imaging evaluation of patients with cancer at time of diagnosis and during cancer therapy and discusses the challenges of the imaging evaluation of treatment-related toxicities.

RECENT FINDINGS

Treatment of CNS tumors with surgery, chemotherapy, or radiation alters the imaging appearance of the tumor and can be associated with a variety of treatment-related toxicities. The clinician must be familiar with how to assess response to treatment and how to differentiate tumor progression from treatment-related effects.

SUMMARY

Management and follow-up of neuro-oncology patients is optimized by a comprehensive radiologic approach to CNS tumors and recognition of the challenges in the assessment of response to treatments.

Serial MRI Scan of Posterior Fossa Tumours Predict Patients at Risk of Developing Neurocognitive Impairment

Background:

Brain tumours are the most common solid tumours in children. More than 50% of these tumours develop in the posterior cranial fossa. Long term survivors of posterior fossa tumours (PFT) suffer from neurocognitive and memory issues. We hypothesized that serial MRI scanning of brain would show differences in hippocampal and ACC volume change in PFT patients treated with and without chemo-radiotherapy.

Material and Methods:

Twelve patients (8 females and 4 males) underwent 76 serial MR imaging examinations before and during treatment for posterior fossa tumours. Seven patients (4 medulloblastoma, 2 as ependymoma and 1 high grade glioma) were treated with maximum surgical resection followed by adjuvant radiotherapy and chemotherapy (Group 1). The other five patients were diagnosed as pilocytic astrocytoma who were treated only with surgery (Group 2). Hippocampal volumes were obtained manually on high-resolution 3Tesla T1-weighted images and normalised to intracranial volume, while ACC thickness and volume were obtained automatically using FreeSurfer software.

Results:

After the treatment period, the change in normalised hippocampal volume from baseline was significantly lower in group 1 patients compared to group 2 (mean change -0.0001470 ± 8.981e-005; Mean ± SEM vs 0.0002765 ± 9.151e-005; Mean ± SEM, respectively, P=0.004). Displayed graphically, the negative hippocampal growth trajectory in group 1 gradually returned to a positive growth pattern. There were no statistically significant changes in ACC volume and thickness. Both groups had similar rates of pre-operative hydrocephalus.

Conclusion:

Compared to PFT patients treated with surgery alone, PFT patients treated with chemo-radiotherapy showed lower hippocampal volumes and altered hippocampal growth trajectory. Serial quantitative MRI measures of brain may provide a neuroanatomical substrate for assessing functional impact on normal brain function following treatment of posterior fossa tumours.

Application of intravoxel incoherent motion (IVIM) magnetic resonance imaging in the evaluation of primitive brain tumours

Intravoxel incoherent motion is a potential non-invasive diagnostic tool in brain tumours, without any clear guidelines for its evaluation yet. In our study, we compare intravoxel incoherent motion with dynamic susceptibility contrast magnetic resonance imaging in the quantification of tumour tissue blood perfusion in 28 patients affected by brain tumours, highlighting the issues encountered during the acquisition set-up and post-processing steps. Intravoxel incoherent motion is a new imaging tool and an alternative technique to dynamic susceptibility contrast-magnetic resonance imaging which is of considerable interest at present. This is partly because it does not require the use of a contrast agent and relies on the intrinsic properties of motion in the capillaries of the spins. Compared to dynamic susceptibility contrast-magnetic resonance imaging, the intravoxel incoherent motion technique is also characterised by better resolution because the gadolinium-based contrast agent bolus used in the standard technique results in a variation by more than 50% of the signal coming from the brain. Finally, intravoxel incoherent motion is more sensitive to the incoherent motion that originates from small capillary vessels, while the dynamic susceptibility contrast signal is also contaminated by the input from larger arteries and veins, which may result in an overestimation of the blood volume. Although there are limitations due to the heterogeneity of the sample considered in our study, intravoxel incoherent motion has been shown to be an accurate noninvasive radiological biomarker, useful to distinguish between low and high grade glial tumours.

Headache Caused by Brain Metastases of Castration-resistant Prostate Cancer during Cabazitaxel Therapy

We describe the case of a 55-year-old man who underwent four cycles of cabazitaxel therapy for castration-resistant prostate cancer (CRPC). After the fourth cycle of cabazitaxel, the patient experienced severe headaches. Brain gadolinium (Gd) contrast-enhanced magnetic resonance imaging (MRI) revealed multiple brain metastases. A few days later, the patient suffered impaired consciousness that progressed rapidly. The patient was treated for the symptoms of increased intracranial pressure and underwent whole-brain radiation. One month later, the patient's consciousness level and headache had improved. Although brain metastases of prostate cancer are rare, the possibility of brain metastases should be considered for prostate cancer patients, especially when a CRPC patient complains of headache. Additionally, even if major conditions such as cerebral hemorrhage are excluded by the use of non-contrast-enhanced computed tomography, brain Gd contrast-enhanced MRI should be performed in consideration of the possibility of brain metastases of prostate cancer.

Amino acid PET and MR perfusion imaging in brain tumours

Purpose

Despite the excellent capacity of the conventional MRI to image brain tumours, problems remain in answering a number of critical diagnostic questions. To overcome these diagnostic shortcomings, PET using radiolabeled amino acids and perfusion-weighted imaging (PWI) are currently under clinical evaluation. The role of amino acid PET and PWI in different diagnostic challenges in brain tumours is controversial.

Methods

Based on the literature and experience of our centres in correlative imaging with PWI and PET using O-(2-[¹⁸F]fluoroethyl)-l-tyrosine or 3,4-dihydroxy-6-[¹⁸F]-fluoro-l-phenylalanine, the current role and shortcomings of amino acid PET and PWI in different diagnostic challenges in brain tumours are reviewed. Literature searches were performed on PubMed, and additional literature was retrieved from the reference lists of identified articles. In particular, all studies in which amino acid PET was directly compared with PWI were included.

Results

PWI is more readily available, but requires substantial expertise and is more sensitive to artifacts than amino acid PET. At initial diagnosis, PWI and amino acid PET can help to define a site for biopsy but amino acid PET appears to be more powerful to define the tumor extent. Both methods are helpful to differentiate progression or recurrence from unspecific posttherapeutic changes. Assessment of therapeutic efficacy can be achieved especially with amino acid PET, while the data with PWI are sparse.

Conclusion

Both PWI and amino acid PET add valuable diagnostic information to the conventional MRI in the assessment of patients with brain tumours, but further studies are necessary to explore the complementary nature of these two methods.

The Potential of Metabolic Imaging

Metabolic imaging is a field of molecular imaging that focuses and targets changes in metabolic pathways for the evaluation of different clinical conditions. Targeting and quantifying metabolic changes noninvasively is a powerful approach to facilitate diagnosis and evaluate therapeutic response. This review addresses only techniques targeting metabolic pathways. Other molecular imaging strategies, such as affinity or receptor imaging or microenvironment-dependent methods are beyond the scope of this review. Here we describe the current state of the art in clinically translatable metabolic imaging modalities. Specifically, we focus on PET and MR spectroscopy, including conventional (1)H- and (13)C-MR spectroscopy at thermal equilibrium and hyperpolarized MRI. In this article, we first provide an overview of metabolic pathways that are altered in many pathologic conditions and the corresponding probes and techniques used to study those alterations. We then describe the application of metabolic imaging to several common diseases, including cancer, neurodegeneration, cardiac ischemia, and infection or inflammation.

ANG1005 for breast cancer brain metastases: correlation between 18F-FLT-PET after first cycle and MRI in response assessment

PURPOSE

Improved therapies and imaging modalities are needed for the treatment of breast cancer brain metastases (BCBM). ANG1005 is a drug conjugate consisting of paclitaxel covalently linked to Angiopep-2, designed to cross the blood-brain barrier. We conducted a biomarker substudy to evaluate ¹⁸F-FLT-PET for response assessment.

METHODS

Ten patients with measurable BCBM received ANG1005 at a dose of 550 mg/m² IV every 21 days. Before and after cycle 1, patients underwent PET imaging with ¹⁸F-FLT, a thymidine analog, retention of which reflects cellular proliferation, for comparison with gadolinium-contrast magnetic resonance imaging (Gd-MRI) in brain metastases detection and response assessment. A 20 % change in uptake after one cycle of ANG1005 was deemed significant.

RESULTS

Thirty-two target and twenty non-target metastatic brain lesions were analyzed. The median tumor reduction by MRI after cycle 1 was -17.5 % (n = 10 patients, lower, upper quartiles: -25.5, -4.8 %) in target lesion size compared with baseline. Fifteen of twenty-nine target lesions (52 %) and 12/20 nontarget lesions (60 %) showed a ≥20 % decrease post-therapy in FLT-PET SUV change (odds ratio 0.71, 95 % CI: 0.19, 2.61). The median percentage change in SUV_max was -20.9 % (n = 29 lesions; lower, upper quartiles: -42.4, 2.0 %), and the median percentage change in SUV₈₀ was also -20.9 % (n = 29; lower, upper quartiles: -49.0, 0.0 %). Two patients had confirmed partial responses by PET and MRI lasting 6 and 18 cycles, respectively. Seven patients had stable disease, receiving a median of six cycles.

CONCLUSIONS

ANG1005 warrants further study in BCBM. Results demonstrated a moderately strong association between MRI and ¹⁸F-FLT-PET imaging.

Metastasis in Adult Brain Tumors

Metastatic cancer to the central nervous system is primarily deposited by hematogenous spread in various anatomically distinct regions: calvarial, pachymeningeal, leptomeningeal, and brain parenchyma. A patient's overall clinical status and the information needed to make treatment decisions are the primary considerations in initial imaging modality selection. Contrast-enhanced MR imaging is the preferred imaging modality. Morphologic MR imaging is limited to delineating anatomic deraignment of tissues. Dynamic susceptibility contrast-enhanced perfusion and diffusion-weighted physiology-based MR imaging sequences have been developed that complement morphologic MR imaging by providing additional diagnostic information.

Local image variance of 7 Tesla SWI is a new technique for preoperative characterization of diffusely infiltrating gliomas: correlation with tumour grade and IDH1 mutational status

OBJECTIVES

To investigate the value of local image variance (LIV) as a new technique for quantification of hypointense microvascular susceptibility-weighted imaging (SWI) structures at 7 Tesla for preoperative glioma characterization.

METHODS

Adult patients with neuroradiologically suspected diffusely infiltrating gliomas were prospectively recruited and 7 Tesla SWI was performed in addition to standard imaging. After tumour segmentation, quantification of intratumoural SWI hypointensities was conducted by the SWI-LIV technique. Following surgery, the histopathological tumour grade and isocitrate dehydrogenase 1 (IDH1)-R132H mutational status was determined and SWI-LIV values were compared between low-grade gliomas (LGG) and high-grade gliomas (HGG), IDH1-R132H negative and positive tumours, as well as gliomas with significant and non-significant contrast-enhancement (CE) on MRI.

RESULTS

In 30 patients, 9 LGG and 21 HGG were diagnosed. The calculation of SWI-LIV values was feasible in all tumours. Significantly higher mean SWI-LIV values were found in HGG compared to LGG (92.7 versus 30.8; p < 0.0001), IDH1-R132H negative compared to IDH1-R132H positive gliomas (109.9 versus 38.3; p < 0.0001) and tumours with significant CE compared to non-significant CE (120.1 versus 39.0; p < 0.0001).

CONCLUSIONS

Our data indicate that 7 Tesla SWI-LIV might improve preoperative characterization of diffusely infiltrating gliomas and thus optimize patient management by quantification of hypointense microvascular structures.

KEY POINTS

• 7 Tesla local image variance helps to quantify hypointense susceptibility-weighted imaging structures. • SWI-LIV is significantly increased in high-grade and IDH1-R132H negative gliomas. • SWI-LIV is a promising technique for improved preoperative glioma characterization. • Preoperative management of diffusely infiltrating gliomas will be optimized.

Advanced neuroimaging in the clinic: critical appraisal of the evidence base

The shortage of high-quality systematic reviews in the field of radiology limits evidence-based integration of imaging methods into clinical practice and may perpetuate misconceptions regarding the efficacy and appropriateness of imaging techniques for specific applications. Diffusion tensor imaging for patients with mild traumatic brain injury (DTI-mTBI) and dynamic susceptibility contrast MRI for patients with glioma (DSC-glioma) are applications of quantitative neuroimaging, which similarly detect manifestations of disease where conventional neuroimaging techniques cannot. We performed a critical appraisal of reviews, based on the current evidence-based medicine methodology, addressing the ability of DTI-mTBI and DSC-glioma to (a) detect brain abnormalities and/or (b) predict clinical outcomes. 23 reviews of DTI-mTBI and 26 reviews of DSC-glioma met criteria for inclusion. All reviews addressed detection of brain abnormalities, whereas 12 DTI-mTBI reviews and 22 DSC-glioma reviews addressed prediction of a clinical outcome. All reviews were assessed using a critical appraisal worksheet consisting of 19 yes/no questions. Reviews were graded according to the total number of positive responses and the 2011 Oxford Centre for evidence-based medicine levels of evidence criteria. Reviews addressing DTI-mTBI detection had moderate quality, while those addressing DSC-glioma were of low quality. Reviews addressing prediction of outcomes for both applications were of low quality. Five DTI-mTBI reviews, but only one review of DSC-glioma met criteria for classification as a meta-analysis/systematic/quantitative review.

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

Purpose

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

Experimental Design

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

Results

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

Conclusions

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

Trial Registration

ClinicalTrials.gov NCT01186601

The predictive capacity of apparent diffusion coefficient (ADC) in response assessment of brain metastases following radiation

To investigate the predictive capacity of the apparent diffusion coefficient (ADC) as a biomarker of radiation response in brain metastases. Seventy brain metastases from 42 patients treated with either stereotactic radiosurgery or whole brain radiotherapy were imaged at baseline, 1 week, and 1 month post-treatment using diffusion-weighted MRI. Mean and median relative ADC for metastases was calculated by normalizing ADC measurements to baseline ADC. At 1 year post-treatment, or last available follow-up MRI, volume criteria determined final tumour response status. Uni- and multivariate analysis was used to account for factors associated with tumour response at 1 week and 1 month. A generalized estimating equations model took into consideration multiple tumours per subject. Optimal thresholds that distinguished responders from non-responders, as well as sensitivity and specificity were determined by receiver operator characteristic analysis and Youden's index. Lower relative ADC values distinguished responders from non-responders at 1 week and 1 month (P < 0.05). Optimal cut-off values for response were 1.060 at 1 week with a sensitivity and specificity of 75.0 and 56.3 %, respectively. At 1 month, the cut-off was 0.971 with a sensitivity and specificity of 70.0 and 68.8 %, respectively. A multivariate general estimating equations analysis identified no prior radiation [odds ratio (OR) 0.211 and 0.137, P = 0.033 and 0.0177], and a lower median relative ADC at 1 week and 1 month (OR 0.619 and 0.694, P = 0.0036 and 0.005), as predictors of tumour response. Lower relative ADC values at 1 week and 1 month following radiation distinguished responders from non-responders and may be a promising biomarker of early radiation response.

Diffusion tensor imaging and functional MRI

The advances in diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI) over the last 20 years have vastly contributed to improving the understanding of the brain structure and function in patients with many diseases of the central nervous system (CNS). DWI is commonly used, for instance, in the diagnostic workup of stroke, CNS neoplasia, and rapidly progressive dementia cases. The new DTI methods provide more specific information about the most destructive aspects of tumors, neurodegenerative dementia, and multiple sclerosis pathology and give a more complete picture of the complex pathologic mechanisms of these conditions. More recently, fMRI has provided insight to the mechanisms of brain adaptation and plasticity to damage related to many neurologic conditions and has further extended our ability to understand the functional significance of pathologic changes in these diseases. Although at present fMRI does not have a role in the diagnosis, routine assessment, and monitoring of neurologic diseases, significant efforts are under way in order to achieve harmonization of both acquisition and postprocessing procedures, which are likely to contribute to a significant change of the clinical scenario.

Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression

BACKGROUND AND PURPOSE

MR imaging in neuro-oncology is challenging due to inherent ambiguities in proton signal behavior. Sodium-MR imaging may substantially contribute to the characterization of tumors because it reflects the functional status of the sodium-potassium pump and sodium channels.

MATERIALS AND METHODS

Sodium-MR imaging data of patients with treatment-naïve glioma WHO grades I-IV (n = 34; mean age, 51.29 ± 17.77 years) were acquired by using a 7T MR system. For acquisition of sodium-MR images, we applied density-adapted 3D radial projection reconstruction pulse sequences. Proton-MR imaging data were acquired by using a 3T whole-body system.

RESULTS

We demonstrated that the initial sodium signal of a treatment-naïve brain tumor is a significant predictor of isocitrate dehydrogenase (IDH) mutation status (P < .001). Moreover, independent of this correlation, the Cox proportional hazards model confirmed the sodium signal of treatment-naïve brain tumors as a predictor of progression (P = .003). Compared with the molecular signature of IDH mutation status, information criteria of model comparison revealed that the sodium signal is even superior to IDH in progression prediction. In addition, sodium-MR imaging provides a new approach to noninvasive tumor classification. The sodium signal of contrast-enhancing tumor portions facilitates differentiation among most glioma types (P < .001).

CONCLUSIONS

The information of sodium-MR imaging may help to classify neoplasias at an early stage, to reduce invasive tissue characterization such as stereotactic biopsy specimens, and overall to promote improved and individualized patient management in neuro-oncology by novel imaging signatures of brain tumors.