Glioblastoma and radiotherapy: A multicenter AI study for Survival Predictions from MRI (GRASP study).

BACKGROUND: The aim was to predict survival of glioblastoma at 8 months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion. METHODS: Retrospective and prospective data were collected from 206 consecutive glioblastoma, isocitrate dehydrogenase -wildtype patients diagnosed between March 2014 and February 2022 across 11 UK centers. Models were trained on 158 retrospective patients from 3 centers. Holdout test sets were retrospective (n = 19; internal validation), and prospective (n = 29; external validation from 8 distinct centers). Neural network branches for T2-weighted and contrast-enhanced T1-weighted inputs were concatenated to predict survival. A nonimaging branch (demographics/MGMT/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; nonimaging features; and weighted dense blocks pretrained for abnormality detection. RESULTS: The imaging model outperformed the nonimaging model in all test sets (area under the receiver-operating characteristic curve, AUC P = .038) and performed similarly to a combined imaging/nonimaging model (P > .05). Imaging, nonimaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10 000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; P = .003). CONCLUSIONS: A deep learning model using MRI images after radiotherapy reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.

Clinical, Imaging and Neurogenetic Features of Patients with Gliomatosis Cerebri Referred to a Tertiary Neuro-Oncology Centre.

INTRODUCTION: Gliomatosis cerebri describes a rare growth pattern of diffusely infiltrating glioma. The treatment options are limited and clinical outcomes remain poor. To characterise this population of patients, we examined referrals to a specialist brain tumour centre. METHODS: We analysed demographic data, presenting symptoms, imaging, histology and genetics, and survival in individuals referred to a multidisciplinary team meeting over a 10-year period. RESULTS: In total, 29 patients fulfilled the inclusion criteria with a median age of 64 years. The most common presenting symptoms were neuropsychiatric (31%), seizure (24%) or headache (21%). Of 20 patients with molecular data, 15 had IDH wild-type glioblastoma, with an IDH1 mutation most common in the remainder (5/20). The median length of survival from MDT referral to death was 48 weeks (IQR 23 to 70 weeks). Contrast enhancement patterns varied between and within tumours. In eight patients who had DSC perfusion studies, five (63%) had a measurable region of increased tumour perfusion with rCBV values ranging from 2.8 to 5.7. A minority of patients underwent MR spectroscopy with 2/3 (66.6%) false-negative results. CONCLUSIONS: Gliomatosis imaging, histological and genetic findings are heterogeneous. Advanced imaging, including MR perfusion, could identify biopsy targets. Negative MR spectroscopy does not exclude the diagnosis of glioma.

Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium.

OBJECTIVE: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep-learning models. METHODS: MR imaging data were analysed from a random sample of five patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. RESULTS: All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. CONCLUSION: The imaging protocol and scheduling varies across the UK, making it challenging to develop machine-learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. ADVANCES IN KNOWLEDGE: Successful translation of deep-learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.

Imaging features associated with H3 K27-altered and H3 G34-mutant gliomas: a narrative systematic review.

BACKGROUND: Advances in molecular diagnostics accomplished the discovery of two malignant glioma entities harboring alterations in the H3 histone: diffuse midline glioma, H3 K27-altered and diffuse hemispheric glioma, H3 G34-mutant. Radiogenomics research, which aims to correlate tumor imaging features with genotypes, has not comprehensively examined histone-altered gliomas (HAG). The aim of this research was to synthesize the current published data on imaging features associated with HAG. METHODS: A systematic search was performed in March 2022 using PubMed and the Cochrane Library, identifying studies on the imaging features associated with H3 K27-altered and/or H3 G34-mutant gliomas. RESULTS: Forty-seven studies fulfilled the inclusion criteria, the majority on H3 K27-altered gliomas. Just under half (21/47) were case reports or short series, the remainder being diagnostic accuracy studies. Despite heterogeneous methodology, some themes emerged. In particular, enhancement of H3 K27M-altered gliomas is variable and can be less than expected given their highly malignant behavior. Low apparent diffusion coefficient values have been suggested as a biomarker of H3 K27-alteration, but high values do not exclude this genotype. Promising correlations between high relative cerebral blood volume values and H3 K27-alteration require further validation. Limited data on H3 G34-mutant gliomas suggest some morphologic overlap with 1p/19q-codeleted oligodendrogliomas. CONCLUSIONS: The existing data are limited, especially for H3 G34-mutant gliomas and artificial intelligence techniques. Current evidence indicates that imaging-based predictions of HAG are insufficient to replace histological assessment. In particular, H3 K27-altered gliomas should be considered when occurring in typical midline locations irrespective of enhancement characteristics.

Diverse imaging features of adolescent glioblastoma.

We highlight an unusual case of multifocal glioblastoma in an adolescent patient, manifesting as four discrete brain lesions, each distinct in appearance. Familiarity with the diverse imaging features of glioblastoma can reduce misdiagnosis and avoid treatment delays.

2021 WHO classification of tumours of the central nervous system: a review for the neuroradiologist.

The fifth edition of the World Health Organization Classification of Tumours of the Central Nervous System (WHO CNS5) published in 2021 builds on the 2016 edition and incorporates output from the Consortium to Inform Molecular and Practical Approaches to CNS Tumour Taxonomy (cIMPACT-NOW). WHO CNS5 introduces fundamental changes to brain tumour classification through the introduction of new tumour families and types, especially in the paediatric population, and a revision of diagnostic criteria for some of the existing neoplasms. Neuroradiologists are central to brain tumour diagnostics, and it is therefore essential that they become familiar with the key updates. This review aims to summarise the most relevant updates for the neuroradiologist and, where available, discuss the known radiophenotypes of various new tumour types to allow for increased accuracy of language and diagnosis. Of particular importance, WHO CNS5 places greater emphasis on organising tumours by molecular type to reflect biology, as well as to allow for better planning of treatment. The principal updates in adult tumours concern the molecular definition of glioblastoma, restructuring of diffuse gliomas, and the introduction of several new tumour types. The updates to the paediatric classification are protean, ranging from the introduction of new types to establishing separate tumour families for paediatric-type gliomas. This review summarises the most significant revisions and captures the rationale and radiological implications for the major updates.

Filtration-Histogram Based Magnetic Resonance Texture Analysis (MRTA) for the Distinction of Primary Central Nervous System Lymphoma and Glioblastoma.

Primary central nervous system lymphoma (PCNSL) has variable imaging appearances, which overlap with those of glioblastoma (GBM), thereby necessitating invasive tissue diagnosis. We aimed to investigate whether a rapid filtration histogram analysis of clinical MRI data supports the distinction of PCNSL from GBM. Ninety tumours (PCNSL n = 48, GBM n = 42) were analysed using pre-treatment MRI sequences (T1-weighted contrast-enhanced (T1CE), T2-weighted (T2), and apparent diffusion coefficient maps (ADC)). The segmentations were completed with proprietary texture analysis software (TexRAD version 3.3). Filtered (five filter sizes SSF = 2-6 mm) and unfiltered (SSF = 0) histogram parameters were compared using Mann-Whitney U non-parametric testing, with receiver operating characteristic (ROC) derived area under the curve (AUC) analysis for significant results. Across all (n = 90) tumours, the optimal algorithm performance was achieved using an unfiltered ADC mean and the mean of positive pixels (MPP), with a sensitivity of 83.8%, specificity of 8.9%, and AUC of 0.88. For subgroup analysis with >1/3 necrosis masses, ADC permitted the identification of PCNSL with a sensitivity of 96.9% and specificity of 100%. For T1CE-derived regions, the distinction was less accurate, with a sensitivity of 71.4%, specificity of 77.1%, and AUC of 0.779. A role may exist for cross-sectional texture analysis without complex machine learning models to differentiate PCNSL from GBM. ADC appears the most suitable sequence, especially for necrotic lesion distinction.

Imaging characteristics of H3 K27M histone-mutant diffuse midline glioma in teenagers and adults.

BACKGROUND: To assess anatomical and quantitative diffusion-weighted MR imaging features in a recently classified lethal neoplasm, H3 K27M histone-mutant diffuse midline glioma [World Health Organization (WHO) IV]. METHODS: Fifteen untreated gliomas in teenagers and adults (median age 19, range, 14-64) with confirmed H3 K27M histone-mutant genotype were analysed at a national referral centre. Morphological characteristics including tumour epicentre(s), T2/FLAIR and Gadolinium enhancement patterns, calcification, haemorrhage and cyst formation were recorded. Multiple apparent diffusion coefficient (ADCmin, ADCmean) regions of interest were sited in solid tumour and normal appearing white matter (ADCNAWM) using post-processing software (Olea Sphere v2.3, Olea Medical). ADC histogram data (2nd, 5th, 10th percentile, median, mean, kurtosis, skewness) were calculated from volumetric tumour segmentations and tested against the regions of interest (ROI) data (Wilcoxon signed rank test). RESULTS: The median interval from imaging to tissue diagnosis was 9 (range, 0-74) days. The structural MR imaging findings varied between individuals and within tumours, often featuring signal heterogeneity on all MR sequences. All gliomas demonstrated contact with the brain midline, and 67% exhibited rim-enhancing necrosis. The mean ROI ADCmin value was 0.84 (±0.15 standard deviation, SD) ×10-3 mm2/s. In the largest tumour cross-section (excluding necrosis), an average ADCmean value of 1.12 (±0.25)×10-3 mm2/s was observed. The mean ADCmin/NAWM ratio was 1.097 (±0.149), and the mean ADCmean/NAWM ratio measured 1.466 (±0.299). With the exception of the 2nd centile, no statistical difference was observed between the regional and histogram derived ADC results. CONCLUSIONS: H3 K27M-mutant gliomas demonstrate variable morphology and diffusivity, commonly featuring moderately low ADC values in solid tumour. Regional ADC measurements appeared representative of volumetric histogram data in this study.

Conventional MRI features of adult diffuse glioma molecular subtypes: a systematic review.

PURPOSE: Molecular parameters have become integral to glioma diagnosis. Much of radiogenomics research has focused on the use of advanced MRI techniques, but conventional MRI sequences remain the mainstay of clinical assessments. The aim of this research was to synthesize the current published data on the accuracy of standard clinical MRI for diffuse glioma genotyping, specifically targeting IDH and 1p19q status. METHODS: A systematic search was performed in September 2019 using PubMed and the Cochrane Library, identifying studies on the diagnostic value of T1 pre-/post-contrast, T2, FLAIR, T2*/SWI and/or 3-directional diffusion-weighted imaging sequences for the prediction of IDH and/or 1p19q status in WHO grade II-IV diffuse astrocytic and oligodendroglial tumours as defined in the WHO 2016 Classification of CNS Tumours. RESULTS: Forty-four studies including a total of 5286 patients fulfilled the inclusion criteria. Correlations between key glioma molecular markers, namely IDH and 1p19q, and distinctive MRI findings have been established, including tumour location, signal composition (including the T2-FLAIR mismatch sign) and apparent diffusion coefficient values. CONCLUSION: Consistent trends have emerged indicating that conventional MRI is valuable for glioma genotyping, particularly in presumed lower grade glioma. However, due to limited interobserver testing, the reproducibility of qualitatively assessed visual features remains an area of uncertainty.

World Health Organization Grade II/III Glioma Molecular Status: Prediction by MRI Morphologic Features and Apparent Diffusion Coefficient.

Background A readily implemented MRI biomarker for glioma genotyping is currently lacking. Purpose To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) status in patients with glioma. Materials and Methods In this retrospective study of patients studied from July 2008 to February 2019, untreated World Health Organization (WHO) grade II/III gliomas were analyzed by three neuroradiologists blinded to tissue results. Apparent diffusion coefficient (ADC) minimum (ADCmin) and mean (ADCmean) regions of interest were defined in tumor and normal appearing white matter (ADCNAWM). A visual rating of anatomic features (T1 weighted, T1 weighted with contrast enhancement, T2 weighted, and fluid-attenuated inversion recovery) was performed. Interobserver comparison (intraclass correlation coefficient and Cohen κ) was followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between ADC metrics and glioma genotypes, including Bonferroni correction for multiple testing. Descriptors with sufficient concordance (intraclass correlation coefficient, >0.8; κ > 0.6) underwent univariable analysis. Predictive variables (P < .05) were entered into a multivariable logistic regression and tested in an additional test sample of patients with glioma. Results The study included 290 patients (median age, 40 years; interquartile range, 33-52 years; 169 male patients) with 82 IDH wild-type, 107 IDH mutant/1p19q intact, and 101 IDH mutant/1p19q codeleted gliomas. Two predictive models incorporating ADCmean-to-ADCNAWM ratio, age, and morphologic characteristics, with model A mandating calcification result and model B recording cyst formation, classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.91, 0.97) and 0.96 (95% CI: 0.93, 0.98), respectively. In the test sample of 49 gliomas (nine IDH wild type, 21 IDH mutant/1p19q intact, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% CI: 71%, 92%) for model A and 42 of 49 gliomas (86%; 95% CI: 76%, 96%) for model B. Conclusion Two algorithms that incorporated apparent diffusion coefficient values, age, and tumor morphologic characteristics predicted isocitrate dehydrogenase status in World Health Organization grade II/III gliomas on the basis of standard clinical MRI sequences alone. © RSNA, 2020 Online supplemental material is available for this article.

Endogenous Chemical Exchange Saturation Transfer MRI for the Diagnosis and Therapy Response Assessment of Brain Tumors: A Systematic Review.

Purpose: To generate a narrative synthesis of published data on the use of endogenous chemical exchange saturation transfer (CEST) MRI in brain tumors. Materials and Methods: A systematic database search (PubMed, Ovid Embase, Cochrane Library) was used to collate eligible studies. Two researchers independently screened publications according to predefined exclusion and inclusion criteria, followed by comprehensive data extraction. All included studies were subjected to a bias risk assessment using the Quality Assessment of Diagnostic Accuracy Studies tool. Results: The electronic database search identified 430 studies, of which 36 fulfilled the inclusion criteria. The final selection of included studies was categorized into five groups as follows: grading gliomas, 19 studies (area under the receiver operating characteristic curve [AUC], 0.500-1.000); predicting molecular subtypes of gliomas, five studies (AUC, 0.610-0.920); distinction of different brain tumor types, seven studies (AUC, 0.707-0.905); therapy response assessment, three studies (AUC not given); and differentiating recurrence from treatment-related changes, five studies (AUC, 0.880-0.980). A high bias risk was observed in a substantial proportion of studies. Conclusion: Endogenous CEST MRI offers valuable, potentially unique information in brain tumors, but its diagnostic accuracy remains incompletely known. Further research is required to assess the method's role in support of molecular genetic diagnosis, to investigate its use in the posttreatment phase, and to compare techniques with a view to standardization.Keywords: Brain/Brain Stem, MR-Imaging, Neuro-OncologySupplemental material is available for this article.© RSNA, 2020.

Challenges in glucoCEST MR body imaging at 3 Tesla.

BACKGROUND: The aim of this study was to translate dynamic glucose enhancement (DGE) body magnetic resonance imaging (MRI) based on the glucose chemical exchange saturation transfer (glucoCEST) signal to a 3 T clinical field strength. METHODS: An infusion protocol for intravenous (i.v.) glucose was optimised using a hyperglycaemic clamp to maximise the chances of detecting exchange-sensitive MRI signal. Numerical simulations were performed to define the optimum parameters for glucoCEST measurements with consideration to physiological conditions. DGE images were acquired for patients with lymphomas and prostate cancer injected i.v. with 20% glucose. RESULTS: The optimised hyperglycaemic clamp infusion based on the DeFronzo method demonstrated higher efficiency and stability of glucose delivery as compared to manual determination of glucose infusion rates. DGE signal sensitivity was found to be dependent on T2, B1 saturation power and integration range. Our results show that motion correction and B0 field inhomogeneity correction are crucial to avoid mistaking signal changes for a glucose response while field drift is a substantial contributor. However, after B0 field drift correction, no significant glucoCEST signal enhancement was observed in tumour regions of all patients in vivo. CONCLUSIONS: Based on our simulated and experimental results, we conclude that glucose-related signal remains elusive at 3 T in body regions, where physiological movements and strong effects of B1 + and B0 render the originally small glucoCEST signal difficult to detect.

Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: A systematic review and meta-analysis.

BACKGROUND: T1-weighted dynamic contrast-enhanced (DCE) perfusion magnetic resonance imaging (MRI) has been broadly utilized in the evaluation of brain tumors. We aimed at assessing the diagnostic accuracy of DCE-MRI in discriminating between low-grade gliomas (LGGs) and high-grade gliomas (HGGs), between tumor recurrence and treatment-related changes, and between primary central nervous system lymphomas (PCNSLs) and HGGs. METHODS: We performed this study based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy Studies criteria. We systematically surveyed studies evaluating the diagnostic accuracy of DCE-MRI for the aforementioned entities. Meta-analysis was conducted with the use of a random effects model. RESULTS: Twenty-seven studies were included after screening of 2945 possible entries. We categorized the eligible studies into three groups: those utilizing DCE-MRI to differentiate between HGGs and LGGs (14 studies, 546 patients), between recurrence and treatment-related changes (9 studies, 298 patients) and between PCNSLs and HGGs (5 studies, 224 patients). The pooled sensitivity, specificity, and area under the curve for differentiating HGGs from LGGs were 0.93, 0.90, and 0.96, for differentiating tumor relapse from treatment-related changes were 0.88, 0.86, and 0.89, and for differentiating PCNSLs from HGGs were 0.78, 0.81, and 0.86, respectively. CONCLUSIONS: Dynamic contrast-enhanced-Magnetic resonance imaging is a promising noninvasive imaging method that has moderate or high accuracy in stratifying gliomas. DCE-MRI shows high diagnostic accuracy in discriminating between HGGs and their low-grade counterparts, and moderate diagnostic accuracy in discriminating recurrent lesions and treatment-related changes as well as PCNSLs and HGGs.

Filtration-histogram based magnetic resonance texture analysis (MRTA) for glioma IDH and 1p19q genotyping.

BACKGROUND: To determine if filtration-histogram based texture analysis (MRTA) of clinical MR imaging can non-invasively identify molecular subtypes of untreated gliomas. METHODS: Post Gadolinium T1-weighted (T1+Gad) images, T2-weighted (T2) images and apparent diffusion coefficient (ADC) maps of 97 gliomas (54 = WHO II, 20 = WHO III, 23 = WHO IV) between 2010 and 2016 were studied. Whole-tumor segmentations were performed on a proprietary texture analysis research platform (TexRAD, Cambridge, UK) using the software's freehand drawing tool. MRTA commences with a filtration step, followed by quantification of texture using histogram texture parameters. Results were correlated using non-parametric statistics with a logistic regression model generated. RESULTS: T1+Gad performed best for IDH typing of glioblastoma (sensitivity 91.9%, specificity 100%, AUC 0.945) and ADC for non-Gadolinium-enhancing gliomas (sensitivity 85.7%, specificity 78.4%, AUC 0.877). T2 was moderately precise (sensitivity 83.1%, specificity 78.9%, AUC 0.821). Excellent results for IDH typing were achieved from a combination of the three sequences (sensitivity 90.5%, specificity 94.5%, AUC = 0.98). For discriminating 1p19q genotypes, ADC produced the best results using unfiltered textures (sensitivity 80.6%, specificity 89.3%, AUC 0.811). CONCLUSION: Preoperative glioma genotyping with MRTA appears valuable with potential for clinical translation. The optimal choice of texture parameters is influenced by sequence choice, tumour morphology and segmentation method.

Modelling MR and clinical features in grade II/III astrocytomas to predict IDH mutation status.

BACKGROUND AND PURPOSE: There is increasing evidence that many IDH wildtype (IDHwt) astrocytomas have a poor prognosis and although MR features have been identified, there remains diagnostic uncertainty in the clinic. We have therefore conducted a comprehensive analysis of conventional MR features of IDHwt astrocytomas and performed a Bayesian logistic regression model to identify critical radiological and basic clinical features that can predict IDH mutation status. MATERIALS AND METHODS: 146 patients comprising 52 IDHwt astrocytomas (19 WHO Grade II diffuse astrocytomas (A II) and 33 WHO Grade III anaplastic astrocytomas (A III)), 68 IDHmut astrocytomas (53 A II and 15 A III) and 26 GBM were studied. Age, sex, presenting symptoms and Overall Survival were recorded. Two neuroradiologists assessed 23 VASARI imaging descriptors of MRI features and the relation between IDH mutation status and MR and basic clinical features was modelled by Bayesian logistic regression, and survival by Kaplan-Meier plots. RESULTS: The features of greatest predictive power for IDH mutation status were, age at presentation (OR = 0.94 +/-0.03), tumour location within the thalamus (OR = 0.15 +/-0.25), involvement of speech receptive areas (OR = 0.21 +/-0.26), deep white matter invasion of the brainstem (OR = 0.10 +/-0.32), and T1/FLAIR signal ratio (OR = 1.63 +/-0.64). A logistic regression model based on these five features demonstrated excellent out-of-sample predictive performance (AUC = 0.92 +/-0.07; balanced accuracy 0.81 +/-0.09). Stepwise addition of further VASARI variables did not improve performance. CONCLUSION: Five demographic and VASARI features enable excellent individual prediction ofIDH mutation status, opening the way to identifying patients with IDHwt astrocytomas for earlier tissue diagnosis and more aggressive management.

PET imaging in glioma: techniques and current evidence.

PET holds potential to provide additional information about tumour metabolic processes, which could aid brain tumour differential diagnosis, grading, molecular subtyping and/or the distinction of therapy effects from disease recurrence. This review discusses PET techniques currently in use for untreated and treated glioma characterization and aims to critically assess the evidence for different tracers ([F]Fluorodeoxyglucose, choline and amino acid tracers) in this context.

Pseudoprogression of brain tumors.

This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high-grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day-to-day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9-30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow-up. The combination of MRS and diffusion-weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer-aided diagnostic technology, and meticulous evaluation with clinical and pathological data. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Volumetric assessment of tumor size changes in pediatric low-grade gliomas: feasibility and comparison with linear measurements.

PURPOSE: We report a retrospective comparison between bi-dimensional RANO criteria and manual volumetric segmentation (MVS) in pediatric low-grade gliomas. METHODS: MRI FLAIR or T1 post contrast images were used for assessment of tumor response. Seventy patients were included in this single center study, for each patient two scans were assessed ("time 0" and "end of therapy") and response to therapy was evaluated for both methods. Inter-reader variability and average time for volumetric assessment were also calculated. RESULTS: Fourteen (20%) of the 70 patients had discordant results in terms of response assessment between the bi-dimensional measurements and MVS. All volumetric response assessments were in keeping with the subjective analysis of tumor (radiology report). Of the 14 patients, 6 had stable disease (SD) on MVS and progressive disease (PD) on 2D assessment, 5 patients had SD on MVS and partial response (PR) on 2D assessment, 2 patients had PD on MVS and SD on 2D assessment, and 1 patient had PR on MVS and SD on 2D analysis. The number of discordant results rises to 21(30%) if minor response is integrated in the response assessment. MVS was relatively fast and showed high inter-reader concordance. CONCLUSION: Our analysis shows that therapeutic response classification may change in a significant number of children by performing a volumetric tumor assessment. Furthermore, MVS is not particularly time consuming and has very good inter-reader concordance.

Imaging of skull base tumours.

The skull base is a highly complex and difficult to access anatomical region, which constitutes a relatively common site for neoplasms. Imaging plays a central role in establishing the differential diagnosis, to determine the anatomic tumour spread and for operative planning. All skull base imaging should be performed using thin-section multiplanar imaging, whereby CT and MRI can be considered complimentary. An interdisciplinary team approach is central to improve the outcome of these challenging tumours.