The diagnostic accuracy of multiparametric MRI to determine pediatric brain tumor grades and types

A Combination of Amide Proton Transfer, Tumor Blood Flow, and Apparent Diffusion Coefficient Histogram Analysis Is Useful for Differentiating Malignant from Benign Intracranial Tumors in Young Patients: A Preliminary Study

PURPOSE

To evaluate the amide proton transfer (APT), tumor blood flow (TBF), and apparent diffusion coefficient (ADC) combined diagnostic value for differentiating intracranial malignant tumors (MTs) from benign tumors (BTs) in young patients, as defined by the 2021 World Health Organization classification of central nervous system tumors.

METHODS

Fifteen patients with intracranial MTs and 10 patients with BTs aged 0-30 years underwent MRI with APT, pseudocontinuous arterial spin labeling (pCASL), and diffusion-weighted imaging. All tumors were evaluated through the use of histogram analysis and the Mann-Whitney U test to compare 10 parameters for each sequence between the groups. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

The APT maximum, mean, 10th, 25th, 50th, 75th, and 90th percentiles were significantly higher in MTs than in BTs; the TBF minimum (min) was significantly lower in MTs than in BTs; TBF kurtosis was significantly higher in MTs than in BTs; the ADC min, 10th, and 25th percentiles were significantly lower in MTs than in BTs (all p < 0.05). The APT 50th percentile (0.900), TBF min (0.813), and ADC min (0.900) had the highest area under the curve (AUC) values of the parameters in each sequence. The AUC for the combination of these three parameters was 0.933.

CONCLUSIONS

The combination of APT, TBF, and ADC evaluated through histogram analysis may be useful for differentiating intracranial MTs from BTs in young patients.

Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us?

Machine learning (ML) models have become capable of making critical decisions on our behalf. Nevertheless, due to complexity of these models, interpreting their decisions can be challenging, and humans cannot always control them. This paper provides explanations of decisions made by ML models in diagnosing four types of posterior fossa tumors: medulloblastoma, ependymoma, pilocytic astrocytoma, and brainstem glioma. The proposed methodology involves data analysis using kernel density estimations with Gaussian distributions to examine individual MRI features, conducting an analysis on the relationships between these features, and performing a comprehensive analysis of ML model behavior. This approach offers a simple yet informative and reliable means of identifying and validating distinguishable MRI features for the diagnosis of pediatric brain tumors. By presenting a comprehensive analysis of the responses of the four pediatric tumor types to each other and to ML models in a single source, this study aims to bridge the knowledge gap in the existing literature concerning the relationship between ML and medical outcomes. The results highlight that employing a simplistic approach in the absence of very large datasets leads to significantly more pronounced and explainable outcomes, as expected. Additionally, the study also demonstrates that the pre-analysis results consistently align with the outputs of the ML models and the clinical findings reported in the existing literature.

Deep Learning Classifies Low- and High-Grade Glioma Patients with High Accuracy, Sensitivity, and Specificity Based on Their Brain White Matter Networks Derived from Diffusion Tensor Imaging

Classifying low-grade glioma (LGG) patients from high-grade glioma (HGG) is one of the most challenging tasks in planning treatment strategies for brain tumor patients. Previous studies derived several handcrafted features based on the tumor's texture and volume from magnetic resonance images (MRI) to classify LGG and HGG patients. The accuracy of classification was moderate. We aimed to classify LGG from HGG with high accuracy using the brain white matter (WM) network connectivity matrix constructed using diffusion tensor tractography. We obtained diffusion tensor images (DTI) of 44 LGG and 48 HGG patients using routine clinical imaging. Fiber tractography and brain parcellation were performed for each patient to obtain the fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity weighted connectivity matrices. We used a deep convolutional neural network (DNN) for classification and the gradient class activation map (GRAD-CAM) technique to identify the neural connectivity features focused on by the DNN. DNN could classify both LGG and HGG with 98% accuracy. The sensitivity and specificity values were above 0.98. GRAD-CAM analysis revealed a distinct WM network pattern between LGG and HGG patients in the frontal, temporal, and parietal lobes. Our results demonstrate that glioma affects the WM network in LGG and HGG patients differently.

Dynamic susceptibility-contrast magnetic resonance imaging with contrast agent leakage correction aids in predicting grade in pediatric brain tumours: a multicenter study

BACKGROUND

Relative cerebral blood volume (rCBV) measured using dynamic susceptibility-contrast MRI can differentiate between low- and high-grade pediatric brain tumors. Multicenter studies are required for translation into clinical practice.

OBJECTIVE

We compared leakage-corrected dynamic susceptibility-contrast MRI perfusion parameters acquired at multiple centers in low- and high-grade pediatric brain tumors.

MATERIALS AND METHODS

Eighty-five pediatric patients underwent pre-treatment dynamic susceptibility-contrast MRI scans at four centers. MRI protocols were variable. We analyzed data using the Boxerman leakage-correction method producing pixel-by-pixel estimates of leakage-uncorrected (rCBVuncorr) and corrected (rCBVcorr) relative cerebral blood volume, and the leakage parameter, K2. Histological diagnoses were obtained. Tumors were classified by high-grade tumor. We compared whole-tumor median perfusion parameters between low- and high-grade tumors and across tumor types.

RESULTS

Forty tumors were classified as low grade, 45 as high grade. Mean whole-tumor median rCBVuncorr was higher in high-grade tumors than low-grade tumors (mean ± standard deviation [SD] = 2.37±2.61 vs. -0.14±5.55; P<0.01). Average median rCBV increased following leakage correction (2.54±1.63 vs. 1.68±1.36; P=0.010), remaining higher in high-grade tumors than low grade-tumors. Low-grade tumors, particularly pilocytic astrocytomas, showed T1-dominant leakage effects; high-grade tumors showed T2*-dominance (mean K2=0.017±0.049 vs. 0.002±0.017). Parameters varied with tumor type but not center. Median rCBVuncorr was higher (mean = 1.49 vs. 0.49; P=0.015) and K2 lower (mean = 0.005 vs. 0.016; P=0.013) in children who received a pre-bolus of contrast agent compared to those who did not. Leakage correction removed the difference.

CONCLUSION

Dynamic susceptibility-contrast MRI acquired at multiple centers helped distinguish between children's brain tumors. Relative cerebral blood volume was significantly higher in high-grade compared to low-grade tumors and differed among common tumor types. Vessel leakage correction is required to provide accurate rCBV, particularly in low-grade enhancing tumors.

Differentiation of paediatric posterior fossa tumours by the multiregional and multiparametric MRI radiomics approach: a study on the selection of optimal multiple sequences and multiregions

OBJECTIVE

To evaluate the diagnostic performance of a radiomics model based on multiregional and multiparametric MRI to classify paediatric posterior fossa tumours (PPFTs), explore the contribution of different MR sequences and tumour subregions in tumour classification, and examine whether contrast-enhanced T₁ weighted (T1C) images have irreplaceable added value.

METHODS

This retrospective study of 136 PPFTs extracted 11,958 multiregional (enhanced, non-enhanced, and total tumour) features from multiparametric MRI (T₁- and T₂ weighted, T1C, fluid-attenuated inversion recovery, and diffusion-weighted images). These features were subjected to fast correlation-based feature selection and classified by a support vector machine based on different tasks. Diagnostic performances of multiregional and multiparametric MRI features, different sequences, and different tumoral regions were evaluated using multiclass and one-vs-rest strategies.

RESULTS

The established model achieved an overall area under the curve (AUC) of 0.977 in the validation cohort. The performance of PPFTs significantly improved after replacing T1C with apparent diffusion coefficient maps added into the plain scan sequences (AUC from 0.812 to 0.917). When oedema features were added to contrast-enhancing tumour volume, the performance did not significantly improve.

CONCLUSION

The radiomics model built by multiregional and multiparametric MRI features allows for the excellent distinction of different PPFTs and provides valuable references for the rational adoption of MR sequences.

ADVANCES IN KNOWLEDGE

This study emphasized that T1C has limited added value in predicting PPFTs and should be cautiously adopted. Selecting optimal MR sequences may help guide clinicians to better allocate acquisition sequences and reduce medical costs.

The role of artificial intelligence in paediatric neuroradiology

Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping of neuroradiologic conditions is becoming increasingly prevalent, particularly with radiogenomic analyses correlating imaging characteristics with molecular biomarkers of disease. However, integration into routine clinical practice remains elusive. With modern multi-parametric MRI now providing additional data beyond anatomy, informing on histology, biology and physiology, such metric-rich information can present as information overload to the treating radiologist and, as such, information relevant to an individual case can become lost. Artificial intelligence techniques are capable of modelling the vast radiologic, biological and clinical datasets that accompany childhood neurologic disease, such that this information can become incorporated in upfront prognostic modelling systems, with artificial intelligence techniques providing a plausible approach to this solution. This review examines machine learning approaches than can be used to underpin such artificial intelligence applications, with exemplars for each machine learning approach from the world literature. Then, within the specific use case of paediatric neuro-oncology, we examine the potential future contribution for such artificial intelligence machine learning techniques to offer solutions for patient care in the form of decision support systems, potentially enabling personalised medicine within this domain of paediatric radiologic practice.

The impact of magnetic resonance imaging spectroscopy parameters on differentiating between paediatric medulloblastoma and ependymoma

Introduction

The distinction of medulloblastomas and ependymomas plays an important role in the care plans and prognosis of children. We aimed to investigate the role of magnetic resonance spectroscopy (MRS) in the differentiation between medulloblastomas and ependymomas in children.

Materials and methods

The institutional review board approved this prospective study. The brain magnetic resonance imaging protocol including axial multivoxel spectroscopy with a TE of 144 ms was assessed in 49 patients, who were divided into 2 groups: 40 patients with medulloblastomas and 9 patients with ependymomas. Receiver operating characteristic (ROC) curve analysis and the Youden index were utilized to determine the best cut-off, sensitivity, specificity, and area under the curve (AUC) values of the independent spectroscopy parameters.

Results

The choline level (Cho) and the choline/creatine (Cho/Cr) and choline/N-acetyl aspartate (Cho/NAA) ratios of medulloblastomas were significantly higher than those of ependymomas (p < 0.05). A Cho/NAA cut-off value of 1.24 to predict the diagnosis of medulloblastoma yielded the highest AUC and sensitivity of 80.3% and 97.5%, respectively, while a Cho cut-off value of 4.64 produced the highest specificity value of 88.9%.

Conclusions

Our findings suggest that Cho and Cho/NAA derived from MRS could serve as differential factors between paediatric medulloblastomas and ependymomas. Among those, a Cho/NAA cut-off value of 1.24 to predict the diagnosis of medulloblastoma generated the highest accuracy.

The Diagnostic Value of Apparent Diffusion Coefficient and Proton Magnetic Resonance Spectroscopy in the Grading of Pediatric Gliomas

The aims of this retrospective study were to assess the value of the quantitative analysis of apparent diffusion coefficient (ADC) and proton magnetic resonance spectroscopy (¹H-MRS) metabolites in differentiating grades of pediatric gliomas.

Posterior fossa extra-axial variations of medulloblastoma: a pictorial review as a primer for radiologists

Manifestations of an atypical variant of medulloblastoma of the posterior fossa in extra-axial locations have been reported, and key questions concerning its interpretation have been raised previously. This review illustrated the clinico-radiological and histopathological features of the posterior fossa extra-axial medulloblastoma and described possible management strategies. We thoroughly reviewed all atypical anatomical locations of medulloblastoma reported within the posterior fossa and extra-axial spaces. The main characteristics of diagnostic imaging and histopathological results, primarily the distinctive radiopathological characteristics, were summarized to distinguish between intra- and extra-axial medulloblastoma, or pathologies mimicking this tumor. Most cases of posterior fossa extra-axial medulloblastoma have been reported in the cerebellopontine angle, followed by the tentorial and lateral cerebellar locations. The dural tail sign, which is commonly observed in meningioma, is rarely seen in intra- or extra-axial medulloblastoma and might be associated with other benign or malignant lesions. In addition to magnetic resonance imaging, the proposed new imaging techniques, including advances in modern neuroimaging modalities, were discussed, as potentially efficient modalities for characterizing extra-axial medulloblastoma. Radionuclide imaging and magnetic resonance perfusion imaging are practical alternatives to limit the number of differential diagnoses. We believe that medulloblastoma cases are likely under-reported because of publication bias and frequent tumors in unusual locations. Addressing these issues would help establish a more accurate understanding of this entity.

Radiohistogenomics of pediatric low-grade neuroepithelial tumors

PURPOSE

In addition to histology, genetic alteration is now required to classify many central nervous system (CNS) tumors according to the most recent World Health Organization CNS tumor classification scheme. Although that is still not the case for classifying pediatric low-grade neuroepithelial tumors (PLGNTs), genetic and molecular features are increasingly being used for making treatment decisions. This approach has become a standard clinical practice in many specialized pediatric cancer centers and will likely be more widely practiced in the near future. This paradigm shift in the management of PLGNTs necessitates better understanding of how genetic alterations influence histology and imaging characteristics of individual PLGNT phenotypes.

METHODS

The complex association of genetic alterations with histology, clinical, and imaging of each phenotype of the extremely heterogeneous PLGNT family has been addressed in a holistic approach in this up-to-date review article. A new imaging stratification scheme has been proposed based on tumor morphology, location, histology, and genetics. Imaging characteristics of each PLGNT entity are also depicted in light of histology and genetics.

CONCLUSION

This article reviews the association of specific genetic alteration with location, histology, imaging, and prognosis of a specific tumor of the PLGNT family and how that information can be used for better imaging of these tumors.

Differentiation Between Ependymoma and Medulloblastoma in Children with Radiomics Approach

RATIONALE AND OBJECTIVES

Ependymoma (EP) and medulloblastoma (MB) of children are similar in age, location, manifestations and symptoms. Therefore, it is difficult to differentiate them through visual observation in clinical diagnosis. The aim of this study is to investigate the effectiveness of radiomics and machine-learning techniques on multimodal magnetic resonance imaging (MRI) in distinguish EP from MB.

MATERIALS AND METHODS

Three dimensional (3D) tumors were semi-automatic segmented by radiologists from postcontrast T1-weighted images and apparent diffusion coefficient maps in 51 patients (24 EPs, 27 MBs). Then, we extracted radiomics features and further reduced them by three feature selection methods. For each feature selection method, 4 classifiers were adopted which yield 12 different models. After extensive crossvalidation, pairwise test were carried out in receiver operating characteristic curves to explore performance of these models.

RESULTS

The radiomics model built with multivariable logistic regression as feature selection method and random forests as classifier had the best performance, area under the curve achieved 0.91 (95 % confidence interval 0.787-0.968). Five relevant features were highly correlated to discriminate EP and MB, which may used as imaging biomarkers to predict the kinds of tumors.

CONCLUSION

The combination of radiomics and machine-learning approach on 3D multimodal MRI could well distinguish EP and MB of childhood, which assistant doctors in clinical diagnosis. Since there is no uniform model to obtained best performance for every specific data set, it is necessary to try different combination methods.

Perfusion-weighted techniques in MRI grading of pediatric cerebral tumors: efficiency of dynamic susceptibility contrast and arterial spin labeling

PURPOSE

Dynamic susceptibility contrast (DSC) and arterial spin labeling (ASL) perfusion MRI are applied in pediatric brain tumor grading, but their value for clinical daily practice remains unclear. We explored the ability of ASL and DSC to distinguish low- and high-grade lesions, in an unselected cohort of pediatric cerebral tumors.

METHODS

We retrospectively compared standard perfusion outcomes including blood volume, blood flow, and time parameters from DSC and ASL at 1.5T or 3T MRI scanners of 46 treatment-naive patients by drawing ROI via consensus by two neuroradiologists on the solid portions of every tumor. The discriminant abilities of perfusion parameters were evaluated by receiver operating characteristic (ROC) over the entire cohort and depending on the tumor location and the magnetic field.

RESULTS

ASL and DSC parameters showed overall low to moderate performances to distinguish low- and high-grade tumors (area under the curve: between 0.548 and 0.697). Discriminant abilities were better for tumors located supratentorially (AUC between 0.777 and 0.810) than infratentorially, where none of the metrics reached significance. We observed a better differentiation between low- and high-grade cancers at 3T than at 1.5-T. For infratentorial tumors, time parameters from DSC performed better than the commonly used metrics (AUC ≥ 0.8).

CONCLUSION

DSC and ASL show moderate abilities to distinguish low- and high-grade brain tumors in an unselected cohort. Absolute value of K2, TMAX, tMIP, and normalized value of TMAX of the DSC appear as an alternative to conventional parameters for infratentorial tumors. Three Tesla evaluation should be favored over 1.5-Tesla.

The effect of semi-quantitative T1-perfusion parameters for the differentiation between pediatric medulloblastoma and ependymoma

Background

The differentiation between medulloblastomas and ependymomas plays an important role in treatment planning and prognosis for children. This study aims to investigate the role of T1-perfusion parameters during the differentiation between medulloblastomas and ependymomas in children. The institutional review board approved this prospective study. The brain magnetic resonance imaging (MRI) protocol, including axial T1-perfusion, was assessed in 26 patients, divided into a medulloblastoma group (group 1, n = 22) and an ependymoma group (group 2, n = 4). The quantified region of interest (ROI) values for tumors and the tumor to parenchyma ratios were collected and compared between the two groups. Receiver operating characteristic (ROC) curve analysis and the Youden index were utilized to identify the best cut-off, sensitivity, specificity, and area under the curve (AUC) values for the independent T1-perfusion parameters.

Results

The relative enhancement, maximum enhancement, maximum relative enhancement, time to peak, and AUC values for medulloblastomas were significantly higher than those for ependymomas (p < 0.05). Furthermore, the maximum enhancement and maximum relative enhancement for medulloblastoma to parenchyma ratios were also significantly higher than those for ependymomas. A cut-off maximum enhancement value of 100.25 was identified as sufficient to discriminate between medulloblastoma and ependymoma and resulted in a sensitivity of 90.9%, a specificity of 100%, and an AUC of 94.3%.

Conclusion

A cut-off maximum enhancement value of 100.25 derived from T1-perfusion was able to discriminate between medulloblastoma and ependymoma, with high sensitivity, specificity, and accuracy values.

Perfusion, Diffusion, Or Brain Tumor Barrier Integrity: Which Represents The Glioma Features Best?

Purpose

This study aims to incorporate informative histogram indicator analyses and advanced multimodal MRI parameters to differentiate low-grade gliomas (LGGs) from high-grade gliomas (HGGs) and to explore the features associated with patients’ survival.

Patients and methods

A total of 120 patients with pathologically confirmed LGGs or HGGs receiving conventional and advanced MRI such as three-dimensional arterial spin labeling (3D-ASL), intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) were included. The mean and histogram indicators from advanced MRI were calculated from the entire tumor. The efficacies of a single indicator or multiple parameters were tested in distinguishing HGGs from LGGs and predicting patients’ survival. Receiver operating characteristic (ROC) curve and multivariable stepwise logistic regression were used to evaluate the diagnostic efficacies. Leave-one-out cross-validation was further used to validate the accuracy of the parameter sets in glioma grading. Log-rank test using the Kaplan–Meier curve was utilized to predict patients’ survival.

Results

Overall, parameters from DCE-MRI performed better than those from 3D-ASL or IVIM-DWI in both glioma grading and survival prediction. The histogram metrics of V_e were demonstrated to have higher accuracies (the accuracies for Extended Tofts_V_e^mean and Extended Tofts_V_e^median were 68.33% and 71.67%, respectively, while those for the Incremental_V_e^mean and Incremental_V_e^75th were 68.33% and 72.50%, respectively) in grading LGGs from HGGs. The combination of Tofts_V_e histogram metrics was the one with the highest accuracy (81.67%) and area under ROC curve (AUC = 0.840). On the other hand, Patlak_K_trans^95th (AUC = 0.9265) and Extended Tofts_V_e^95th (AUC = 0.9154) performed better than their corresponding means (Patlak_K_trans^mean: AUC = 0.9118 and Extended Tofts_V_e^mean: AUC = 0.9044) in predicting patients’ overall survival (OS) at 18-month follow-up.

Conclusion

DCE-MRI-derived histogram features from the entire tumor were promising metrics for glioma grading and OS prediction. Combining single modal histogram features improved glioma grading.

Trial registration

NCT 02622620.

Posterior fossa tumors in children: Radiological tips & tricks in the age of genomic tumor classification and advance MR technology

Imaging plays a major role in the comprehensive assessment of posterior fossa tumor in children (PFTC). The objective is to propose a global method relying on the combined analysis of radiological, clinical and epidemiological criteria, (taking into account the child's age and the topography of the lesion) in order to improve our histological approach in imaging, helping the management and approach for surgeons in providing information to the patients' parents. Infratentorial tumors are the most frequent in children, representing mainly medulloblastoma, pilocytic astrocytoma and brainstem glioma. Pre-surgical identification of the tumor type and its aggressiveness could be improved by the combined analysis of key imaging features with epidemiologic data.

Brain DSC MR Perfusion in Children: A Clinical Feasibility Study Using Different Technical Standards of Contrast Administration

BACKGROUND AND PURPOSE

Dynamic susceptibility contrast MR perfusion imaging has limited results in children due to difficulties in reproducing technical standards derived from adults. This prospective, multicenter study aimed to determine DSC feasibility and quality in children using custom administration of a standard dose of gadolinium.

MATERIALS AND METHODS

Eighty-three consecutive children with brain tumors underwent DSC perfusion with a standard dose of gadobutrol administered by an automated power injector. The location and size of intravenous catheters and gadobutrol volume and flow rates were reported, and local and/or systemic adverse effects were recorded. DSC was qualitatively evaluated by CBV maps and signal intensity-time curves and quantitatively by the percentage of signal drop and full width at half-maximum, and the data were compared with the standards reported for adults. Quantitative data were grouped by flow rate, and differences among groups were assessed by analysis of covariance and tested for statistical significance with a t test.

RESULTS

No local or systemic adverse events were recorded independent of catheter location (63 arm, 14 hand, 6 foot), size (24-18 ga), and flow rates (1-5 mL/s). High-quality CBV maps and signal intensity-time curves were achieved in all patients, and quantitative evaluations were equal or superior to those reported for adults. No significant differences (P ≥ .05) were identified among the higher-flow-rate groups in the quantitative data.

CONCLUSIONS

A custom administration of a standard dose of gadobutrol allows safe and high-quality DSC MR perfusion imaging in children.

Imaging features of medulloblastoma: Conventional imaging, diffusion-weighted imaging, perfusion-weighted imaging, and spectroscopy: From general features to subtypes and characteristics

Medulloblastoma is a frequent high-grade neoplasm among pediatric brain tumours. Its classical imaging features are a midline tumour growing into the fourth ventricle, hyperdense on CT-scan, displaying a hypersignal when using diffusion-weighted imaging, with a variable contrast enhancement. Nevertheless, atypical imaging features have been widely reported, varying according to the age of the patient, and histopathological subtype. In this study, we review the classical and atypical imaging features of medulloblastomas, with emphasis on advanced MRI techniques, histopathological and molecular subtypes and characteristics, and follow-up modalities.

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

BACKGROUND

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

OBJECTIVES

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

SEARCH METHODS

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

SELECTION CRITERIA

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

DATA COLLECTION AND ANALYSIS

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

MAIN RESULTS

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

AUTHORS' CONCLUSIONS

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

Multiparametric Analysis of Permeability and ADC Histogram Metrics for Classification of Pediatric Brain Tumors by Tumor Grade

BACKGROUND AND PURPOSE

Accurate tumor grading is essential for treatment planning of pediatric brain tumors. We hypothesized that multiparametric analyses of a combination of permeability metrics and ADC histogram metrics would differentiate high- and low-grade tumors with high accuracy.

MATERIALS AND METHODS

DTI and dynamic contrast-enhanced MR imaging using T1-mapping with flip angles of 2°, 5°, 10°, and 15°, followed by a 0.1-mmol/kg body weight gadolinium-based bolus was performed on all patients in addition to standard MR imaging. Permeability data were processed and transfer constant from the blood plasma into the extracellular extravascular space, rate constant from the extracellular extravascular space back into blood plasma, extravascular extracellular volume fraction, and fractional blood plasma volume were calculated from 3D tumor volumes. Apparent diffusion coefficient histogram metrics were calculated for 3 separate tumor volumes derived from T2-FLAIR sequences, T1 contrast-enhanced sequences, and permeability maps, respectively.

RESULTS

Results from 41 patients (0.3-16.76 years of age; mean, 6.22 years) with newly diagnosed contrast-enhancing brain tumors (16 low-grade; 25 high-grade) were included in the institutional review board-approved retrospective analysis. Wilcoxon tests showed a higher transfer constant from blood plasma into extracellular extravascular space and rate constant from extracellular extravascular space back into blood plasma, and lower extracellular extravascular volume fraction (P < .001) in high-grade tumors. The mean ADCs of FLAIR and enhancing tumor volumes were significantly lower in high-grade tumors (P < .001). ROC analysis showed that a combination of extravascular volume fraction and mean ADC of FLAIR volume differentiated high- and low-grade tumors with high accuracy (area under receiver operating characteristic curve = 0.918).

CONCLUSIONS

ADC histogram metrics combined with permeability metrics differentiate low- and high-grade pediatric brain tumors with high accuracy.

Automated Processing of Dynamic Contrast-Enhanced MRI: Correlation of Advanced Pharmacokinetic Metrics with Tumor Grade in Pediatric Brain Tumors

BACKGROUND AND PURPOSE

Pharmacokinetic parameters from dynamic contrast-enhanced MR imaging have proved useful for differentiating brain tumor grades in adults. In this study, we retrospectively reviewed dynamic contrast-enhanced perfusion data from children with newly diagnosed brain tumors and analyzed the pharmacokinetic parameters correlating with tumor grade.

MATERIALS AND METHODS

Dynamic contrast-enhanced MR imaging data from 38 patients were analyzed by using commercially available software. Subjects were categorized into 2 groups based on pathologic analyses consisting of low-grade (World Health Organization I and II) and high-grade (World Health Organization III and IV) tumors. Pharmacokinetic parameters were compared between the 2 groups by using linear regression models. For parameters that were statistically distinct between the 2 groups, sensitivity and specificity were also estimated.

RESULTS

Eighteen tumors were classified as low-grade, and 20, as high-grade. Transfer constant from the blood plasma into the extracellular extravascular space (K^trans), rate constant from extracellular extravascular space back into blood plasma (K_ep), and extracellular extravascular volume fraction (V_e) were all significantly correlated with tumor grade; high-grade tumors showed higher K^trans, higher K_ep, and lower V_e. Although all 3 parameters had high specificity (range, 82%-100%), K_ep had the highest specificity for both grades. Optimal sensitivity was achieved for V_e, with a combined sensitivity of 76% (compared with 71% for K^trans and K_ep).

CONCLUSIONS

Pharmacokinetic parameters derived from dynamic contrast-enhanced MR imaging can effectively discriminate low- and high-grade pediatric brain tumors.