Value and limitations of diffusion-weighted imaging in grading and diagnosis of pediatric posterior fossa tumors

Advanced imaging techniques and non-invasive biomarkers in pediatric brain tumors: state of the art

In the pediatric age group, brain neoplasms are the second most common tumor category after leukemia, with an annual incidence of 6.13 per 100,000. Conventional MRI sequences, complemented by CT whenever necessary, are fundamental for the initial diagnosis and surgical planning as well as for post-operative evaluations, assessment of response to treatment, and surveillance; however, they have limitations, especially concerning histopathologic or biomolecular phenotyping and grading. In recent years, several advanced MRI sequences, including diffusion-weighted imaging, diffusion tensor imaging, arterial spin labelling (ASL) perfusion, and MR spectroscopy, have emerged as a powerful aid to diagnosis as well as prognostication; furthermore, other techniques such as diffusion kurtosis, amide proton transfer imaging, and MR elastography are being translated from the research environment to clinical practice. Molecular imaging, especially PET with amino-acid tracers, complement MRI in several aspects, including biopsy targeting and outcome prediction. Finally, radiomics with radiogenomics are opening entirely new perspectives for a quantitative approach aiming at identifying biomarkers that can be used for personalized, precision management strategies.

Pineal gland ADC values in children aged 0 to 4 years: normative data and usefulness in the differential diagnosis with trilateral retinoblastoma

PURPOSE

Normative ADC values of the pineal gland in young children are currently lacking, however, these are potentially useful in the differential diagnosis of pineal involvement in trilateral retinoblastoma, which is challenging when the size of the tumor is less than 10-15 mm. The main objective of this study was to establish ADC reference values of the normal pineal gland in a large cohort of children between 0 and 4 years.

METHODS

This retrospective study was conducted in a tertiary pediatric hospital. We collected 64 patients with normal MRI examination (between 2017 and 2024) and clinical indication unrelated to the pineal gland, and divided them into 5 age groups (0 to 4 years). Gland size and mean ADC values were calculated, using the ellipsoid formula and ROI/histogram analysis, respectively. The established values were tested in three cases of trilateral retinoblastoma (10 to 20 months).

RESULTS

Mean ADC values were always above 1000 × 10- 6 mm2/s, while in patients with trilateral retinoblastoma they were around 800 × 10- 6 mm2/s. Pineal ADC values were identical in both genders. The volume of the pineal gland showed a tendency to increase with age.

CONCLUSIONS

We present ADC reference data for the pineal gland in children under 4 years of age. The distribution of mean ADC values of trilateral retinoblastoma was significantly different from the normative values, hence, the use DWI/ADC may help to identify small trilateral retinoblastoma in children with ocular pathology.

Machine Learning Analysis in Diffusion Kurtosis Imaging for Discriminating Pediatric Posterior Fossa Tumors: A Repeatability and Accuracy Pilot Study

Background and purpose: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches. Our purpose was to compare repeatability between ROI and VOI DKI-derived diffusion measurements and assess DKI accuracy in discriminating among pediatric PF tumors. Materials and methods: We retrospectively analyzed 34 children (M, F, mean age 7.48 years) with PF tumors who underwent preoperative examination on a 3 Tesla magnet, including DKI. For each patient, two neuroradiologists independently segmented the whole solid tumor, the ROI of the area of maximum tumor diameter, and a small 5 mm ROI. The automated analysis pipeline included inter-observer variability, statistical, and machine learning (ML) analyses. We evaluated inter-observer variability with coefficient of variation (COV) and Bland-Altman plots. We estimated DKI metrics accuracy in discriminating among tumor histology with MANOVA analysis. In order to account for class imbalances, we applied SMOTE to balance the dataset. Finally, we performed a Random Forest (RF) machine learning classification analysis based on all DKI metrics from the SMOTE dataset by partitioning 70/30 the training and testing cohort. Results: Tumor histology included medulloblastoma (15), pilocytic astrocytoma (14), and ependymoma (5). VOI-based measurements presented lower variability than ROI-based measurements across all DKI metrics and were used for the analysis. DKI-derived metrics could accurately discriminate between tumor subtypes (Pillai's trace: p < 0.001). SMOTE generated 11 synthetic observations (10 EP and 1 PA), resulting in a balanced dataset with 45 instances (34 original and 11 synthetic). ML analysis yielded an accuracy of 0.928, which correctly predicted all but one lesion in the testing set. Conclusions: VOI-based measurements presented improved repeatability compared to ROI-based measurements across all diffusion metrics. An ML classification algorithm resulted accurate in discriminating PF tumors on a SMOTE-generated dataset. ML techniques based on DKI-derived metrics are useful for the discrimination of pediatric PF tumors.

Preoperative 11 C-Methionine PET-MRI in Pediatric Infratentorial Tumors

PURPOSE

MRI is the main imaging modality for pediatric brain tumors, but amino acid PET can provide additional information. Simultaneous PET-MRI acquisition allows to fully assess the tumor and lower the radiation exposure. Although symptomatic posterior fossa tumors are typically resected, the patient management is evolving and will benefit from an improved preoperative tumor characterization. We aimed to explore, in children with newly diagnosed posterior fossa tumor, the complementarity of the information provided by amino acid PET and MRI parameters and the correlation to histopathological results.

PATIENTS AND METHODS

Children with a newly diagnosed posterior fossa tumor prospectively underwent a preoperative 11 C-methionine (MET) PET-MRI. Images were assessed visually and semiquantitatively. Using correlation, minimum apparent diffusion coefficient (ADC min ) and contrast enhancement were compared with MET SUV max . The diameter of the enhancing lesions was compared with metabolic tumoral volume. Lesions were classified according to the 2021 World Health Organization (WHO) classification.

RESULTS

Ten children were included 4 pilocytic astrocytomas, 2 medulloblastomas, 1 ganglioglioma, 1 central nervous system embryonal tumor, and 1 schwannoma. All lesions showed visually increased MET uptake. A negative moderate correlation was found between ADC min and SUV max values ( r = -0.39). Mean SUV max was 3.8 (range, 3.3-4.2) in WHO grade 4 versus 2.5 (range, 1.7-3.0) in WHO grade 1 lesions. A positive moderate correlation was found between metabolic tumoral volume and diameter values ( r = 0.34). There was no correlation between SUV max and contrast enhancement intensity ( r = -0.15).

CONCLUSIONS

Preoperative 11 C-MET PET and MRI could provide complementary information to characterize pediatric infratentorial tumors.

Supratentorial and Infratentorial Ependymoma

Ependymomas are the third most common intracranial tumor in children, presenting in both the supratentorial and infratentorial compartments. They may present in infants, young children, and adolescents with symptoms depending on size, location, and the age of the patient. The ideal imaging for evaluation and treatment is MRI. This is crucial for preoperative evaluation and planning, as well as postoperative assessment and evaluating the efficacy of treatment. Essentially without exception, aggressive surgery aimed at complete resection is the initial and most important factor in the long-term outcome of all these children. Histopathologic diagnosis for intracranial pediatric ependymoma has been narrowed to grade II and grade III, no longer characterized as classic and anaplastic. Subsequent conformal photon or proton beam irradiation is an established post-surgical therapy, with solid evidence that it benefits survival and offers lower toxicity to the normal brain of the young child. Although chemotherapeutic treatment has not been generally impactful, immunotherapeutic interventions may be on the horizon. Updated molecular subgrouping of ependymoma is changing the post-resection approach of these tumors with regard to both treatment and outcome. Excluding spinal ependymoma and subependymoma, there are four subtypes that are defined by genetic characteristics, two found in the supratentorial compartment, ST-EPN-YAP1 and ST-EPN-ZFTA, and two in the posterior fossa, PF-EPN-A and PF-EPN-B. Younger children harboring ZFTA fusion-positive supratentorial and type A posterior fossa tumors, regardless of histology, tend toward the poorest outcomes. On the contrary, older children with supratentorial YAP1 fusion-positive ependymomas and type B posterior fossa tumors may survive with surgery alone. The paradigm shift regarding the behavior of the various childhood ependymoma subtypes will hopefully lead to targeted, individualized therapies and improved outcomes.

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.

Posterior fossa tumors in children: An update and new concepts

Background:

Posterior fossa tumors account for approximately half of the central nervous system tumors in children. Major technological advances, mainly in the fields of molecular biology and neuroimaging, have modified their classification, leading to a more detailed description of these entities. Into the classic taxonomy, used for many years, new concepts have been incorporated at times eliminating or modifying former ones.

Methods:

A literature search was conducted in PubMed using the medical subject headings involving the five most common pediatric posterior fossa tumors: diffuse midline glioma, medulloblastoma, ependymoma, atypical teratoid/rhabdoid tumor, and pilocytic astrocytoma. Only English published articles in the past 11 years that provided technological, neuroimaging, and molecular biology insight into posterior fossa tumors in children were considered.

Results:

Substantial changes have been introduced in the nomenclature of pediatric posterior fossa tumors. Diffuse midline gliomas are named based on alterations in histone H3. Molecular rearrangements of medulloblastomas are more important in defining the prognosis than histological variants; therefore, these tumors are currently named based on their molecular subgroups. Posterior fossa ependymomas and atypical teratoid rhabdoid tumor classification have incorporated new groups based on different genetic profiles. Pilocytic astrocytoma has been placed in a new category that distinguishes circumscribed from diffuse entities.

Conclusion:

Advances in molecular biology and neuroimaging have substantially changed the way pediatric neoplasms are studied. The classical taxonomy has been modified leading to more accurate classifications that are based on the genetic alterations.

Apparent Diffusion Coefficient in the Differentiation of Common Pediatric Brain Tumors in the Posterior Fossa: Different Region-of-Interest Selection Methods for Time Efficiency, Measurement Reproducibility, and Diagnostic Utility

OBJECTIVES

This study aimed to explore the diagnostic ability of apparent diffusion coefficient (ADC) values obtained from different region of interest (ROI) measurements in tumor parenchyma for differentiating posterior fossa tumors (PFTs) and the correlations between ADC values and Ki-67.

METHODS

Seventy-three pediatric patients with PFTs who underwent conventional diffusion-weighted imaging were recruited in this study. Five different ROIs were manually drawn by 2 radiologists (ROI-polygon, ROI-3 sections, ROI-3-5 ovals, ROI-more ovals, and ROI-whole). The interreader/intrareader repeatability, time required, diagnostic ability, and Ki-67 correlation analysis of the ADC values based on these ROI strategies were calculated.

RESULTS

Both interreader and intrareader reliabilities were excellent for ADC values among the different ROI strategies (intraclass correlation coefficient, 0.899-0.992). There were statistically significant differences in time consumption among the 5 ROI selection methods ( P < 0.001). The time required for the ROI-3-5 ovals was the shortest (32.23 ± 5.14 seconds), whereas the time required for the ROI-whole was the longest (204.52 ± 92.34 seconds). The diagnostic efficiency of the ADC values showed no significant differences among the different ROI measurements ( P > 0.05). The ADC value was negatively correlated with Ki-67 ( r = -0.745 to -0.798, all P < 0.0001).

CONCLUSIONS

The ROI-3-5 ovals method has the best interobserver repeatability, the shortest amount of time spent, and the best diagnostic ability. Thus, it is considered an effective measurement to produce ADC values in the evaluation of pediatric PFTs.

Correlation Between Apparent Diffusion Coefficient and the Ki-67 Proliferation Index in Grading Pediatric Glioma

OBJECTIVE

This study aimed to investigate the correlation between apparent diffusion coefficient (ADC) and the Ki-67 proliferation index with the pathologic grades of pediatric glioma and to compare their diagnostic performance in differentiating grades of pediatric glioma.

PATIENTS AND METHODS

Magnetic resonance imaging examinations and histopathologies of 121 surgically treated pediatric gliomas (87 low-grade gliomas [LGGs; grades 1 and 2] and 34 high-grade gliomas [HGGs; grades 3 and 4]) were retrospectively reviewed. The mean tumor ADC (ADCmean), minimum tumor ADC (ADCmin), tumor/normal brain ADC ratio (ADC ratio), and value of the Ki-67 proliferation index of LGGs and HGGs were compared. Correlation coefficients were calculated for ADC parameters and Ki-67 values. The receiver operating characteristic curve was used to determine the diagnostic value of ADCmean, ADCmin, ADC ratio, and Ki-67 proliferation index for differentiating LGGs and HGGs.

RESULTS

The ADC values were significantly negatively correlated with glioma grade, and the Ki-67 proliferation index had a significant positive correlation with glioma grade. A significant negative correlation was observed between ADCmean and Ki-67 proliferation index, between ADCmin and Ki-67 proliferation index, and between ADC ratio and Ki-67 proliferation index. The receiver operating characteristic analysis demonstrated moderate to good accuracy for ADCmean in discriminating LGGs from HGGs (area under the curve [AUC], 0.875; sensitivity, 79.3%; specificity, 82.4%; accuracy, 80.2%; positive predictive value [PPV], 92.0%; and negative predictive value [NPV], 60.9% [cutoff value, 1.187] [×10-3 mm2/s]). Minimum tumor ADC showed very good to excellent accuracy with AUC of 0.946, sensitivity of 86.2%, specificity of 94.1%, accuracy of 88.4%, PPV of 97.4%, and NPV of 72.7% (cutoff value, 0.970) (×10-3 mm2/s). The ADC ratio showed moderate to good accuracy with AUC of 0.854, sensitivity of 72.4%, specificity of 88.2%, accuracy of 76.9%, PPV of 94.0%, and NPV of 55.6% (cutoff value, 1.426). For the parameter of the Ki-67 proliferation index, in discriminating LGGs from HGGs, very good to excellent diagnostic accuracy was observed (AUC, 0.962; sensitivity, 94.1%; specificity, 89.7%; accuracy, 90.9%; PPV, 97.5%; and NPV, 78.0% [cutoff value, 7]).

CONCLUSIONS

Apparent diffusion coefficient parameters and the Ki-67 proliferation index were significantly correlated with histological grade in pediatric gliomas. Apparent diffusion coefficient was closely correlated with the proliferative potential of pediatric gliomas. In addition, ADCmin showed superior performance compared with ADCmean and ADC ratio in differentiating pediatric glioma grade, with a close diagnostic efficacy to the Ki-67 proliferation index.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

Application of Apparent Diffusion Coefficient Histogram Metrics for Differentiation of Pediatric Posterior Fossa Tumors : A Large Retrospective Study and Brief Review of Literature

PURPOSE

This study aimed to evaluate the application of apparent diffusion coefficient (ADC) histogram analysis to differentiate posterior fossa tumors (PFTs) in children.

METHODS

A total of 175 pediatric patients with PFT, including 75 pilocytic astrocytomas (PA), 59 medulloblastomas, 16 ependymomas, and 13 atypical teratoid rhabdoid tumors (ATRT), were analyzed. Tumors were visually assessed using DWI trace and conventional MRI images and manually segmented and post-processed using parametric software (pMRI). Furthermore, tumor ADC values were normalized to the thalamus and cerebellar cortex. The following histogram metrics were obtained: entropy, minimum, 10th, and 90th percentiles, maximum, mean, median, skewness, and kurtosis to distinguish the different types of tumors. Kruskal Wallis and Mann-Whitney U tests were used to evaluate the differences. Finally, receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off values for differentiating the various PFTs.

RESULTS

Most ADC histogram metrics showed significant differences between PFTs (p < 0.001) except for entropy, skewness, and kurtosis. There were significant pairwise differences in ADC metrics for PA versus medulloblastoma, PA versus ependymoma, PA versus ATRT, medulloblastoma versus ependymoma, and ependymoma versus ATRT (all p < 0.05). Our results showed no significant differences between medulloblastoma and ATRT. Normalized ADC data showed similar results to the absolute ADC value analysis. ROC curve analysis for normalized ADC_median values to thalamus showed 94.9% sensitivity (95% CI: 85-100%) and 93.3% specificity (95% CI: 87-100%) for differentiating medulloblastoma from ependymoma.

CONCLUSION

ADC histogram metrics can be applied to differentiate most types of posterior fossa tumors in children.

The Diagnostic Efficiency of Quantitative Diffusion Weighted Imaging in Differentiating Medulloblastoma from Posterior Fossa Tumors: A Systematic Review and Meta-Analysis

Medulloblastoma (MB) is considered the most common and highly malignant posterior fossa tumor (PFT) in children. The accurate preoperative diagnosis of MB is beneficial in choosing the appropriate surgical methods and treatment strategies. Diffusion-weighted imaging (DWI) has improved the accuracy of differential diagnosis of posterior fossa tumors. Nonetheless, further studies are needed to confirm its value for clinical application. This study aimed to evaluate the performance of DWI in differentiating MB from other PFT. A literature search was conducted using databases PubMed, Embase, and Web of Science for studies reporting the diagnostic performance of DWI for PFT from January 2000 to January 2022. A bivariate random-effects model was employed to evaluate the pooled sensitivities and specificities. A univariable meta-regression analysis was used to assess relevant factors for heterogeneity, and subgroup analyses were performed. A total of 15 studies with 823 patients were eligible for data extraction. Overall pooled sensitivity and specificity of DWI were 0.94 (95% confident interval [CI]: 0.89-0.97) and 0.94 (95% CI: 0.90-0.96) respectively. The area under the curve (AUC) of DWI was 0.98 (95% CI: 0.96-0.99). Heterogeneity was found in the sensitivity (I² = 62.59%) and the specificity (I² = 35.94%). Magnetic field intensity, region of interest definition and DWI diagnostic parameters are the factors that affect the diagnostic performance of DWI. DWI has excellent diagnostic accuracy for differentiating MB from other PFT. Hence, it is necessary to set DWI as a routine examination sequence for posterior fossa tumors.

Advanced Neuroimaging Approaches to Pediatric Brain Tumors

Simple Summary

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

Abstract

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

MRI-based Whole-Tumor Radiomics to Classify the Types of Pediatric Posterior Fossa Brain Tumor

BACKGROUND

Differential diagnosis between medulloblastoma (MB), ependymoma (EP) and astrocytoma (PA) is important due to differing medical treatment strategies and predicted survival. The aim of this study was to investigate non-invasive MRI-based radiomic analysis of whole tumors to classify the histologic tumor types of pediatric posterior fossa brain tumor and improve the accuracy of discrimination, using a random forest classifier.

METHODS

MRI images of 99 patients, with 59 MBs, 13 EPs and 27 PAs histologically confirmed by surgery and pathology before treatment, were included in this retrospective study. Registration was performed between the three sequences, and high- throughput features were extracted from manually segmented tumors on MR images of each case. The forest-based feature selection method was adopted to select the top ten significant features. Finally, the results were compared and analyzed according to the classification.

RESULTS

The top ten contributions according to the classifier of wavelet features all came from the ADC sequence. The random forest classifier achieved 100% accuracy on the training data and validated the best accuracy (0.938): sensitivity = 1.000, 0.948 and 0.808, specificity = 0.952, 0.926 and 1.000 for EP, MB and PA, respectively.

CONCLUSION

A random forest classifier based on the ADC sequence of the whole tumor provides more quantitative information than TIWI and T2WI in differentiating pediatric posterior fossa brain tumors. In particular, the histogram percentile value showed great superiority, which added diagnostic value in pediatric neuro-oncology.

Ependymoma: Evaluation and Management Updates

PURPOSE OF REVIEW

To review state of art and relevant advances in the molecular genetics and management of ependymomas of children and adults.

RECENT FINDINGS

Ependymomas may occur either in the brain or in the spinal cord. Compared with intracranial ependymomas, spinal ependymomas are less frequent and exhibit a better prognosis. The new WHO classification of CNS tumors of 2021 has subdivided ependymomas into different histomolecular subgroups with different outcome. The majority of studies have shown a major impact of extent of resection; thus, a complete resection must be performed, whenever possible, at first surgery or at reoperation. Conformal radiotherapy is recommended for grade 3 or incompletely resected grade II tumors. Proton therapy is increasingly employed especially in children to reduce the risk of neurocognitive and endocrine sequelae. Craniospinal irradiation is reserved for metastatic disease. Chemotherapy is not useful as primary treatment and is commonly employed as salvage treatment for patients failing surgery and radiotherapy. Standard treatments are still the mainstay of treatment: the discovery of new druggable pathways will hopefully increase the therapeutic armamentarium in the near future.

Pearls and Pitfalls of Imaging in Pediatric Brain Tumors

The central nervous system (CNS) tumors constitute the most common type of solid tumors in the pediatric population. The cerebral and cerebellar parenchyma are the most common site of pediatric CNS neoplasms. Imaging plays an important role in detection, characterization, staging and prognostication of brain tumors. The focus of the current article is pediatric brain tumor imaging with emphasis on pearls and pitfalls of conventional and advanced imaging in various pediatric brain tumor subtypes. The article also elucidates changes in brain tumor terms and entities as applicable to pediatric patients, updated as per World Health Organization (WHO) 2016 classification of primary CNS tumors. This classification introduced the genetic and/or molecular information of primary CNS neoplasms as part of comprehensive tumor pathology report in the routine clinical workflow. The concepts from 2016 classification have been further refined based on current research, by the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) group and published in the form of updates. The updates serve as guidelines in the time interval between WHO updates and expect to be broadly adopted in the subsequent WHO classification. The current review covers most pediatric brain tumors except pituitary tumors, meningeal origin tumors, nerve sheath tumors and CNS lymphoma/leukemia.

Meta-Analysis of Apparent Diffusion Coefficient in Pediatric Medulloblastoma, Ependymoma, and Pilocytic Astrocytoma

BACKGROUND

Medulloblastoma, ependymoma, and pilocytic astrocytoma are common pediatric posterior fossa tumors. These tumors show overlapping characteristics on conventional MRI scans, making diagnosis difficult.

PURPOSE

To investigate whether apparent diffusion coefficient (ADC) values differ between tumor types and to identify optimum cut-off values to accurately classify the tumors using different performance metrics.

STUDY TYPE

Systematic review and meta-analysis.

SUBJECTS

Seven studies reporting ADC in pediatric posterior fossa tumors (115 medulloblastoma, 68 ependymoma, and 86 pilocytic astrocytoma) were included following PubMed and ScienceDirect searches.

SEQUENCE AND FIELD STRENGTH

Diffusion weighted imaging (DWI) was performed on 1.5 and 3 T across multiple institution and vendors.

ASSESSMENT

The combined mean and standard deviation of ADC were calculated for each tumor type using a random-effects model, and the effect size was calculated using Hedge's g.

STATISTICAL TESTS

Sensitivity/specificity, weighted classification accuracy, balanced classification accuracy. A P value < 0.05 was considered statistically significant, and a Hedge's g value of >1.2 was considered to represent a large difference.

RESULTS

The mean (± standard deviation) ADCs of medulloblastoma, ependymoma, and pilocytic astrocytoma were 0.76 ± 0.16, 1.10 ± 0.10, and 1.49 ± 0.16 mm² /sec × 10^-3 . To maximize sensitivity and specificity using the mean ADC, the cut-off was found to be 0.96 mm² /sec × 10^-3 for medulloblastoma and ependymoma and 1.26 mm² /sec × 10^-3 for ependymoma and pilocytic astrocytoma. The meta-analysis showed significantly different ADC distributions for the three posterior fossa tumors. The cut-off values changed markedly (up to 7%) based on the performance metric used and the prevalence of the tumor types.

DATA CONCLUSION

There were significant differences in ADC between tumor types. However, it should be noted that only summary statistics from each study were analyzed and there were differences in how regions of interest were defined between studies.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 3.

Supratentorial Extraventricular Ependymomas: Imaging Features and the Added Value of Apparent Diffusion Coefficient

OBJECTIVE

To improve the understanding and the diagnosis of intracranial ependymal tumors.

METHODS

The clinical, radiological and prognostic features of 48 supratentorial extraventricular ependymomas and 74 intraventricular ependymomas were summarized and compared.

RESULTS

Supratentorial extraventricular ependymomas, most often located in the frontal lobe (33.3%) and classified as grade III (75.0%), had relatively large eccentric cysts (3.07 ± 2.03 cm), significant enhancement (84.8%), low apparent diffusion coefficient (ADC) values, and associated with higher mortality (41.3%). The majority of intraventricular lesions occurred in the fourth ventricle (86.5%) and classified as grade II (78.4%), had relatively small and multiple cystic changes (1.04 ± 0.87 cm), slight or moderate enhancement (76.9%), high ADC values and associated with lower mortality (20.7%). There were few significant differences between grade II and grade III tumors in these 2 groups, respectively. Young age, high grade and low ADC values are worse prognostic indicators for patients with supratentorial extraventricular ependymomas, but not for those with intraventricular ependymomas.

CONCLUSIONS

Conventional radiological features, combined with clinical manifestations and quantitative information provided by diffusion-weighted imaging, may not only enhance the diagnosis and assist in determining prognosis but also provide a better pathophysiological understanding of intracranial ependymal tumors.

Role of MRI in Differentiating Various Posterior Cranial Fossa Space-Occupying Lesions Using Sensitivity and Specificity: A Prospective Study

BACKGROUND

Any abnormal space-occupying posterior fossa lesion may directly involve the vital structures like the brain stem, cranial nerves, cerebellum, vertebrobasilar artery, and venous sinuses, which makes the surgical approach and total excision very difficult. Hence for these reasons, precise evaluation of posterior fossa lesion with MRI is a must to visualize the vital structures, which helps in planning and safe surgery.  Objective: This study aimed to evaluate the added value of diffusion-weighted imaging and magnetic resonance spectroscopy in the localization, extension, characterization, differentiation of various posterior fossa space-occupying lesions, and correlating with the histopathological result.

MATERIALS AND METHODS

This prospective study comprised of 40 patients who were suspected with posterior fossa space-occupying lesions on basis of clinical features or on CT scan. All patients were evaluated using conventional as well as newer MRI techniques using Siemens 1.5 Tesla MRI scanner (Siemens Medical System, Erlangen, Germany). Diffusion-weighted imaging (DWI) was done in all patients and magnetic resonance spectroscopy (MRS) was done in 27 patients. Based on the MRI findings, various posterior fossa lesions were classified as neoplastic or non-neoplastic. The neoplastic lesions were further classified as benign and malignant. The MRI findings were correlated with histopathological findings or follow-up.

STATISTICAL ANALYSIS

Independent sample t-test was used to compare the mean apparent diffusion coefficient (ADC) values of various posterior fossa space-occupying lesions. Receiver operating characteristic (ROC) curve analysis was done to determine the optimal cut-off mean ADC values and choline/creatinine (Cho/cr) ratios for various benign and malignant posterior fossa tumors.

RESULTS

Of 40 patients with posterior fossa lesions, 23 were males and 17 were females with a mean age of 34.67±1.93[SD] years. Metastases were the most common posterior fossa lesions in our study sample and found in seven patients (17.5%) followed by vestibular schwannomas and brainstem gliomas in five patients (12.5%) each, demyelinating lesion in four patients (10%), tubercular abscess in three patients (7.5%), hemangioblastoma, tuberculoma, arachnoid cyst, epidermoid cyst, pilocytic astrocytoma, low-grade glioma in two patients (5%) each, meningioma, medulloblastoma, pyogenic abscess and high-grade glioma in one patient (2.5%) each. The mean ADC value of benign tumors was higher than that of malignant tumors and this difference was found to be significant (p = 0.019). The cut-off ADC value 1.022 x 10^-3mm²/s had a sensitivity of 78.6% and specificity of 66.7%. MRS played important role in differentiating neoplastic from non-neoplastic lesions and benign from malignant tumors. The cut-off Cho/cr ratio of 1.25 had a sensitivity of 66.7%, specificity of 85.7% to differentiate benign from malignant tumors.

CONCLUSION

Conventional MRI sequences able to diagnose most of the benign-appearing lesions of posterior fossa, however, adding advanced MRI sequences like diffusion-weighted imaging and MR spectroscopy helps us to differentiate and diagnose various posterior fossa lesions even closer to the actual histopathological diagnosis.

An Insight into Pathophysiological Features and Therapeutic Advances on Ependymoma

Simple Summary

Although biological information and the molecular classification of ependymoma have been studied, the treatment systems for ependymoma are still insufficient. In addition, because the disease occurs infrequently, it is difficult to obtain sufficient data to conduct large-scale or randomized clinical trials. Therefore, this study is intended to emphasize the importance of understanding its pathological characteristics and prognosis as well as developing treatments for ependymoma through multilateral studies.

Abstract

Glial cells comprise the non-sensory parts of the central nervous system as well as the peripheral nervous system. Glial cells, also known as neuroglia, constitute a significant portion of the mammalian nervous system and can be viewed simply as a matrix of neural cells. Despite being the “Nervenkitt” or “glue of the nerves”, they aptly serve multiple roles, including neuron repair, myelin sheath formation, and cerebrospinal fluid circulation. Ependymal cells are one of four kinds of glial cells that exert distinct functions. Tumorigenesis of a glial cell is termed a glioma, and in the case of an ependymal cell, it is called an ependymoma. Among the various gliomas, an ependymoma in children is one of the more challenging brain tumors to cure. Children are afflicted more severely by ependymal tumors than adults. It has appeared from several surveys that ependymoma comprises approximately six to ten percent of all tumors in children. Presently, the surgical removal of the tumor is considered a standard treatment for ependymomas. It has been conspicuously evident that a combination of irradiation therapy and surgery is much more efficacious in treating ependymomas. The main purpose of this review is to present the importance of both a deep understanding and ongoing research into histopathological features and prognoses of ependymomas to ensure that effective diagnostic methods and treatments can be developed.

Valores del coeficiente de difusión aparente en el diagnóstico diferencial de los tumores de fosa posterior en población pediátrica de Colombia

Introducción. Los tumores de la fosa posterior son frecuentes en la población pediátrica y requieren de una adecuada caracterización mediante resonancia magnética (RM) estructural y técnicas avanzadas de RM para lograr un enfoque terapéutico apropiado. Objetivo. Determinar la utilidad de los valores del coeficiente de difusión aparente (ADC cuantitativo) en el diagnóstico diferencial de los tumores de la fosa posterior en población pediátrica de un hospital de referencia en Bogotá D.C., Colombia. Materiales y métodos. Estudio de validez de prueba diagnóstica realizado en 28 pacientes pediátricos diagnosticados con tumor de fosa posterior entre 2017-2019 en la Fundación Hospital de la Misericordia, hospital de IV nivel de complejidad. Se midieron los valores de ADC, los cuales fueron comparados con  el diagnóstico histopatológico como estándar de oro, obteniendo datos de sensibilidad, especificidad, valores predictivos positivos y negativos.. Las diferencias entre medianas fueron determinadas mediante la prueba de Kruskall-Wallis. El valor p entre el valor ADC y el estándar de oro se calculó con la prueba de X2 de Pearson, con un nivel de significancia de p<0.05. Resultados. La edad media fue 83.9 meses (DE=57 meses) y 64.28% fueron niños. El tumor más frecuente fue el meduloblastoma (39.29%). Para el diagnóstico de meduloblastoma se estableció un valor ADC cuantitativo de 0.6210x10-³mm²/s, obteniéndose una sensibilidad y especificidad de 81.82% y 76.47%, respectivamente, y para el diagnóstico de astrocitoma pilocítico, un ADC cuantitativo de 1.03x10-³mm²/s, con una sensibilidad de 66.67% y una especificidad de 89.40%-. Conclusiones. El valor ADC cuantitativo es útil para clasificar y diferenciar los tumores de la fosa posterior en población pediátrica colombiana, mostrando una relación inversamente proporcional con el grado histológico tumoral.

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.

Neuroimaging of pediatric infratentorial tumors and the value of diffusion-weighted imaging (DWI) in determining tumor grade

BACKGROUND

Diffusion-weighted imaging (DWI) provides information about the cellular density of tumors. This feature is useful in grading and identifying different tumor types.

PURPOSE

To assess the value of diffusion restriction and apparent diffusion coefficient (ADC) values in differentiating pediatric infratentorial tumors.

MATERIAL AND METHODS

This was a retrospective review of the magnetic resonance imaging (MRI) of 82 children (age range 1-16 years) with infratentorial tumors. Histopathological grading after surgical excision/biopsy was categorized as low grade (WHO grades I and II) (n = 31; 29 pilocytic astrocytomas, 2 ependymomas) and high grade (WHO grade III and IV) (n = 51; 40 medulloblastomas, 8 anaplastic ependymomas, 1 anaplastic astrocytoma, 2 atypical rhabdoid teratoid tumors [ATRT]). MRI features and ADC values were compared among tumor types and grades using a two-tailed t test, Mann-Whitney U test for continuous data and Chi-square test for categorical variables.

RESULTS

Diffusion restriction and low ADC value was a feature of high-grade tumors (P<0.001). The mean ADC values of the low-grade and high-grade tumors were 1.567 × 10^-3mm²/s and 0.661 × 10^-3mm²/s, respectively. Using 0.9 × 10^-3mm²/s as the cut-off value, the sensitivity, specificity, positive and negative predictive values for differentiating the grades was 87%, 100%, 100%, and 81.8%, respectively. Significant differences were found between the mean ADC values of the individual tumor types (P<0.05), except between medulloblastoma and ATRT.

CONCLUSION

ADC values and visual assessment of diffusion restriction are useful in tumor grading. The individual tumor types can be identified by an algorithmic approach, using DWI in conjunction with other described MRI features.

Diffusion MRI in Evaluation of Pediatric Posterior Fossa Tumors

Background:

To evaluate the role of diffusion MRI in differentiating pediatric posterior fossa tumors and determine the cut-off values of ADC ratio to distinguish medulloblastoma from other common tumors.

Methods:

We retrospectively reviewed MRI of 90 patients (7.5-year median age) with pathologically proven posterior fossa tumors (24 medulloblastoma, 7 ependymoma, 4 anaplastic ependymoma, 13 pilocytic astrocytoma, 30 diffuse intrinsic pontine glioma (DIPG), 4 ATRT, 3 diffuse astrocytoma, 2 high grade astrocytoma, 2 glioblastoma, and 1 low grade glioma). The conventional MRI characteristics were evaluated. Two readers reviewed DWI visual scale and measured ADC values by consensus. ADC measurement was performed at the solid component of tumors. ADC ratio between the tumors to cerebellar white matter were calculated.

Results:

The ADC ratio of medulloblastoma was significantly lower than ependymoma, pilocytic astrocytoma and DIPG. The ADC cut-off ratio of ≤ 1.115 allowed discrimination medulloblastoma from other posterior fossa tumors with sensitivity, specificity, PPV and NPV of 95.8%, 81%, 67.6% and 97.9%, respectively. ADC ratio cut-off level to differentiate medulloblastoma from ependymoma was ≤ 0.995 with area under the curve (AUC)= 0.8693. ADC ratio cut-off level for differentiate medulloblastoma from pilocytic astrocytoma at ≤ 1.17 with AUC = 0.9936. ADC cut-off level for differentiate medulloblastoma from DIPG at ≤ 1.195 with AUC = 0.9681. The ADC ratio was correlated with WHO grading by the lower ADC ratio associated with the higher grade. Furthermore, High DWI visual scale was associated with high grade tumor.

Conclusion:

Diffusion MRI has a significant role in diagnosis of pediatric posterior fossa tumors. ADC ratio can be used to distinguish medulloblastoma from other posterior fossa tumor with good level of diagnostic performance.

Measurement of Apparent Diffusion Coefficient (ADC) Values of Ependymoma and Medulloblastoma Tumors: a Patient-based Study

Background:

Some brain tumors such as ependymoma and Medulloblastoma have similar MR images which may result to undifferentiated them from each other.

Objective:

This study aimed to compare the apparent diffusion coefficient (ADC) of two different cerebellar pediatric tumors, including ependymoma and medulloblastoma which have shown similar clinical images in conventional magnetic resonance imaging (MRI) methods.

Material and Methods:

In this analytical study, thirty six pediatric patients who were suspected to have the mentioned tumors according to their CT image findings were included in this study. The patients were subjected to conventional MRI protocols followed by diffusion weighted imaging (DWI) and ADC values of the tumors were calculated automatically using MRI scanner software.

Results:

The mean (± SD) ADC value for ependymoma (1.2± 0.06 ×10^-3 mm²/s) was significantly higher than medulloblastoma (0.87 ± 0.02 ×10^-3 mm²/s) (p = 0.041). Moreover, the maximum ADC value of ependymoma was considerably different in comparison with medulloblastoma (1.4 ×10^-3 mm²/s and 0.96×10^-3 mm²/s, respectively; p = 0.035). Furthermore, the minimum ADC value of ependymoma was higher compared to medulloblastoma (1.0 ×10^-3 mm²/s and 0.61×10^-3 mm²/s, respectively), but there was not significant (p = 0.067).

Conclusion:

Evaluation of ADC values for ependymoma and medulloblastoma is a reliable method to differentiate these two malignancies. This is due to different ADC values reflected during the evaluation.

The Role of Apparent Diffusion Coefficient in the Differentiation between Cerebellar Medulloblastoma and Brainstem Glioma

For certain clinical circumstances, the differentiation between cerebellar medulloblastoma and brainstem glioma is essential. We aimed to evaluate the role played by the apparent diffusion coefficient (ADC) values in the differentiation between cerebellar medulloblastomas and brainstem gliomas in children. The institutional review board approved this prospective study. Brain magnetic resonance imaging (MRI), including diffusion-weighted imaging (DWI) and ADC, was assessed in 32 patients (median age: 7.0 years), divided into two groups, a medulloblastoma group (group 1, n = 22) and a brainstem glioma group (group 2, n = 10). The Mann-Whitney U test was utilized to compare tumor ADCmax, ADCmin, ADCmean, and ADCsd values, and their ratios with the parenchyma values between the two groups. Receiver operating characteristic (ROC) curve analysis and the Youden index were used to calculate the cut-off value, along with the area under the curve (AUC), sensitivity, and specificity. The median ADCmax, ADCmin, and ADCmean values were significantly higher in group 2 than in group 1 (p < 0.05). The median ratios of ADCmin and ADCmean to the parenchyma were significantly higher in group 2 than in group 1 (p < 0.05). The ROC analysis showed that the AUC for the ADCmean ratio was the highest among these parameters, at 98.2%. The ADCmean tumor to parenchyma ratio was a significant and effective parameter for the differentiation between pediatric medulloblastomas and brainstem gliomas.

The Effects of Applying Apparent Diffusion Coefficient Parameters on the Differentiation between Fourth Ventricular Ependymoma and Diffuse Intrinsic Pontine Glioma

Distinguishing the fourth ventricular ependymoma from diffuse intrinsic pontine glioma (DIPG) is essential to improve the treatment strategy between these two tumor types. We attempted to evaluate the effects of applying apparent diffusion coefficient (ADC) values to the distinction between pediatric fourth ventricular ependymomas and DIPGs. Brain magnetic resonance imaging, including diffusion-weighted imaging and ADC, was assessed in 26 patients, who were divided into two groups: group 1 included 8 patients with fourth ventricular ependymoma and group 2 included 18 patients with DIPG. The Mann–Whitney U test was utilized to compare tumoral maximum (ADCmax), minimum ADC (ADCmin), mean ADC (ADCmean), and standard deviation (ADCsd) values, and the ratios between the tumor and parenchyma values for each of these parameters (rADCmax, rADCmin, rADCmean, and rADCsd, respectively) between the two groups. Cutoff values were calculated based on receiver operating characteristic curve analysis and the Youden index, and the area under the curve (AUC), sensitivity, and specificity were determined. The median ADCmax, ADCmin, ADCmean, rADCmax, rADCmin, and rADCmean values were significantly lower in group 1 than in group 2 (p < 0.05). For the differential diagnosis of ependymomas and DIPGs, a cutoff ADCmean value of 1.02 × 10−3 mm2/s was determined, which produced a sensitivity of 100%, a specificity of 88.9%, and an AUC of 95.8%. ADC parameters should be considered when performing a differential diagnosis between fourth ventricular ependymomas and DIPGs. Based on our findings, a cutoff ADCmean value of 1.02 × 10−3 mm2/s was the most significant and effective parameter for this purpose.

Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging

BACKGROUND AND PURPOSE

Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging.

MATERIALS AND METHODS

This retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma (n = 111), ependymoma (n = 70), and pilocytic astrocytoma (n = 107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation.

RESULTS

For 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method χ2 score and the Generalized Linear Model classifier achieved a test micro-averaged area under the curve of 0.92 with an accuracy of 0.74. Tree-Based Pipeline Optimization Tool models achieved significantly higher accuracy than average qualitative expert MR imaging review (0.83 versus 0.54, P < .001). For binary classification, Tree-Based Pipeline Optimization Tool models achieved an area under the curve of 0.94 with an accuracy of 0.85 for medulloblastoma versus nonmedulloblastoma, an area under the curve of 0.84 with an accuracy of 0.80 for ependymoma versus nonependymoma, and an area under the curve of 0.94 with an accuracy of 0.88 for pilocytic astrocytoma versus non-pilocytic astrocytoma.

CONCLUSIONS

Automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.

Combination of diffusion-weighted imaging and arterial spin labeling at 3.0 T for the clinical staging of nasopharyngeal carcinoma

PURPOSE

To probe the utility of diffusion-weighted imaging (DWI) and 3D arterial spin labeling (ASL) in assessing the clinical stage of nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

This prospective study included sixty-five newly diagnosed NPC patients who underwent DWI and 3D ASL scans on a 3.0-T magnetic resonance imaging (MRI) system. The apparent diffusion coefficient (ADC) and the tumor blood flow (TBF) of NPC were measured. Tumors were classified as low or high T, N and American Joint Committee on Cancer (AJCC) stages. Student's t-test was used to evaluate the differences between tumors with low and high clinical stages. Pearson correlation analyses were performed to determine the correlation between MRI parameters and clinical stages. Receiver operating characteristic (ROC) curves were then used to evaluate diagnostic capability.

RESULTS

High T stage (T3/4) NPC showed significantly lower ADC_min (P = 0.000) and higher TBF_max (P = 0.003) and TBF_mean (P = 0.008) values than low T stage (T1/2) NPC. High N stage (N2/3) NPC showed significantly lower ADC_min values (P = 0.023) than low N stage (N0/1) NPC. High AJCC stage (III/IV) NPC showed significantly lower ADC_min (P = 0.000) and higher TBF_max (P = 0.005) and TBF_mean (P = 0.011) values than low AJCC stage (I/II) NPC. ADC_min values showed moderate negative correlations with T stage (r = -0.512, P = 0.000), N stage (r = -0.281, P = 0.023), and AJCC stage (r = -0.494, P = 0.000). TBF_max values showed moderate positive correlations with T stage (r = 0.369, P = 0.003) and AJCC stage (r = 0.346, P = 0.005). Compared with ADC_min and TBF_max alone, the combination of ADC_min and TBF_max improved the accuracy from 72.3% and 75.4% to 78.5%, respectively, for T staging, as well as from 72.3% and 69.2% to 83.1% for AJCC staging.

CONCLUSIONS

ADC_min and TBF_max values in patients with NPC could help evaluate clinical stages. ADC_min and TBF_max values combined could clearly improve the accuracy in the assessment of AJCC stage.

Diagnostic accuracy of apparent diffusion coefficient ratio in distinguishing common pediatric CNS posterior fossa tumors

Background

Pilocytic astrocytoma, medulloblastoma, and ependymoma are the most common pediatric CNS tumors seen at posterior cranial fossa and final diagnosis obtained by histopathology after surgical excision. Routine MRI study gives an idea about site and extension of the tumors but provide a little information about type and grade of tumors. ADC ratio had high sensitivity and specificity in differentiation between these tumors as regard type and grade according to tumor cellularity.

Patients and methods

Prospective study conducted on thirty pediatric patients (11 males and 19 females) with CNS posterior fossa masses, their ages ranged from 2 to 17 years (mean age of 8.7 years), conventional MRI, DWI, ADC value, and ADC ratio were done for all patients.

Results

ADC values were significantly different between pilocytic astrocytomas (1.43 ± 0.28 × 10−3) and medulloblastomas (0.71 ± 0. 21 × 10−3) with a P value < 0.001, also there was a significant difference when comparing medulloblastomas (0.71 ± 0.21 × 10−3) with ependymomas (1.04 × 10−3 ± 0.21) with a P value < 0.001. ADC ratio at a cutoff > 1.7 showed significant good power of discrimination of astrocytoma (AUC = 0.85) from ependymoma with 87.5% sensitivity and 93.3% specificity. Similarly, at cutoff ≤ 1.6-> 1.2 was a significant good predictor of ependymoma (AUC = 0.85) with 87.8% sensitivity and 99.5% specificity. While, ADC ratio ≤ 1.2 was significant excellent discriminator of medulloblastoma (AUC = 0.99) with 100% sensitivity and 90% specificity.

Conclusion

ADC ratio is a simple way used in distinguishing juvenile pilocytic astrocytoma, ependymoma, and medulloblastoma, which are the most frequent pediatric posterior fossa tumors. Cutoff ADC ratio of more than 1.7 characteristic of JPA with 87.5% sensitivity and 93.3% specificity, ADC ratio less than 1.1 characteristic of medulloblastoma with 100% sensitivity and 90% specificity. ADC ratios more than 1.1 and less than 1.7 characteristic of ependymoma with 87.8% sensitivity and 99.5% specificity. We recommended ADC ratio as a routine study in evaluation of pediatric CNS posterior fossa tumors.

Advantage of high b value diffusion-weighted imaging for differentiation of common pediatric brain tumors in posterior fossa

PURPOSE

The pediatric posterior fossa (PF) brain tumors with higher frequencies are embryonal tumors (ET), ependymal tumors (EPN) and pilocytic astrocytomas (PA), however, it is often difficult to make a differential diagnosis among them with conventional MRI. The ADC calculated from DWI could be beneficial for diagnostic work up.

METHOD

We acquired DWI at b = 1000 and 4000(s/mm²). The relationship between ADC and the three types of brain tumors was evaluated with Mann-Whitney U test. We also performed simple linear regression analysis to evaluate the relationship between ADC and cellularity, and implemented receiver operating characteristic curve (ROC curve) to test the diagnostic performance among tumors.

RESULTS

The highest ADC (b1000/b4000 × 10^-3 mm²/s) was observed in PA (1.02-1.91/0.73-1.28), followed by PF-EPN (0.83-1.28/0.60-0.79) and the lowest was ET (0.41-0.75/0.29-0.47). There was significant difference among the groups in both ADC value (b-1000/b-4000: ET vs. PF-EPN p < 0.0001/0.0001, ET vs. PA p < 0.0001/0.0001, PF-EPN vs. PA p < 0.0001/0.0001). ROC analysis revealed that ADC in both b-values showed complete separation between ET and PF-EPN. And it also revealed that ADC at b-4000 could differentiate PF-EPN and PA (96.0%) better than ADC at b-1000 (90.1%). The stronger negative correlation was observed between the ADC and cellularity at b-4000 than at b-1000 (R²  = 0.7415 vs.0.7070) CONCLUSIONS: ADC of ET was significantly lower than the other two groups, and ADC of PA was significantly higher than the other two groups in both b-1000 and b-4000. Our results showed that ADC at b-4000 was more useful than ADC at b-1000 especially for differentiation between PF-EPN and PA.

Grading Gliomas Capability: Comparison between Visual Assessment and Apparent Diffusion Coefficient (ADC) Value Measurement on Diffusion-Weighted Imaging (DWI)

Background:

To compare diagnostic accuracy between DWI visual scale assessment and ADC value measurement of solid portion of the tumor in grading gliomas.

Methods:

This retrospective study included 38 patients who had pathologically proven gliomas between January 2013 and August 2018 with 18 low grade and 20 high grade tumors. All patients underwent MRI and biopsy. Two readers reviewed DWI visual scale independently. Disagreement was resolved by consensus. One reviewer measured ADC value of entire solid part of the tumor in single axial slice with greatest dimension of tumor which was chosen by consensus. Two data sets of visual scale and ADC value were analyzed and comparison of diagnostic accuracy in glioma grading was done by using area under the curve (AUC) of receiver operating characteristic curve (ROC).

Results:

Visual scale and ADC value could be used to distinguish between low and high grade gliomas with a statistically significant difference. (P-value 0.002 and <0.001). Almost all high grade gliomas had visual scale 5. The sensitivity, specificity, PPV NPV and accuracy were 50%, 100%, 100% , 64.3%,73.68% respectively. The cutoff level for the ADC value was determined to be 1119.48 x10^-6 mm²/s in differentiation between low and high grade gliomas with the sensitivity, specificity, PPV, NPV, accuracy of 90%, 88.89% , 90%, 88.9% and 89.47% respectively. There was no statistically significant difference(P-value = 0.163).

Conclusion:

Both Visual scale and ADC value were capable of differentiating between low and high grade gliomas. Although visual scale may not replace ADC measurement, larger scale prospective study is needed for validate this initial result.

Three-Dimensional Pseudo-Continuous Arterial Spin Labeling Parameters Distinguish Pediatric Medulloblastoma and Pilocytic Astrocytoma

Introduction: Arterial Spin Labeling (ASL), a perfusion assessment without using gadolinium-based contrast agents, is outstandingly advantageous for pediatric patients. The differentiation of medulloblastomas from pilocytic astrocytomas in children plays a significant role in determining treatment strategies and prognosis. This study aimed to assess the use of ASL parameters during the differentiation between pediatric medulloblastoma and pilocytic astrocytoma. Methods: The institutional review board of Children's Hospital 2 approved this prospective study. The brain magnetic resonance imaging (MRI) protocol, including axial three-dimensional (3D) pseudo-continuous ASL, was evaluated in 33 patients, who were divided into a medulloblastoma group (n = 25) and a pilocytic astrocytoma group (n = 8). The quantified region of interest (ROI) values for the 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 significant ASL parameters. Results: The cerebral blood flow (CBF) and the ratio between the CBF of the tumor relative to that of the parenchyma (rCBF) values for medulloblastomas were significantly higher than those for pilocytic astrocytomas (p < 0.05). A cut-off value of 0.51 for rCBF was able to discriminate between medulloblastoma and pilocytic astrocytoma, generating a sensitivity of 88%, a specificity of 75%, and an AUC of 83.5%. Conclusion: The rCBF measurement, obtained during MRI with 3D pseudo-continuous ASL, plays a supplemental role in the differentiation of medulloblastoma from pilocytic astrocytoma.

Comparison of 2D BLADE Turbo Gradient- and Spin-Echo and 2D Spin-Echo Echo-Planar Diffusion-Weighted Brain MRI at 3 T: Preliminary Experience in Children

RATIONALE AND OBJECTIVES

We describe our preliminary experience using a 2D turbo gradient- and spin-echo (TGSE) diffusion-weighted (DW) pulse sequence with non-Cartesian BLADE trajectory at 3 T in pediatric patients. We compared the TGSE BLADE to conventional DW spin-echo echo-planar imaging (SE-EPI) in pediatric brain imaging, assessing the presence of artifacts from signal pile-ups, geometric distortion, motion, susceptibility from air-tissue interface, shunts and orthodontia, and diagnostic image quality.

MATERIALS AND METHODS

Data were acquired in 53 patients (10.4 ± 7.9 years). All DW imaging data were acquired precontrast, with SE-EPI first. A four-point scale for rating was used-1 (best) and 4 (worst). A neuroradiologist scored the two sequences and further noted whether the TGSE BLADE approach or SE-EPI was preferred in each case. Apparent diffusion coefficients were compared quantitatively between the two sequences in a subset of 16 patients, in 41 separate regions of interests including caudate nucleus, putamen, globus pallidus, thalamus, and pathological areas.

RESULTS

In 43.4% of the cases, TGSE BLADE was preferred; in 49.1% of the cases, both sequences were preferred equally. Average scores for SE-EPI were 2.2 ± 0.8 versus TGSE's 1.2 ± 0.4 in assessing diagnostic quality (p < 0.05). Motion artifacts were minimal on both sequences in 92.5% of the cases. In the TGSE BLADE scores, no case received a "4" for significant artifacts with marginally acceptable image quality. Apparent diffusion coefficients values between the two sequences were statistically similar, with a linear regression slope of 0.92 (r² = 0.97).

CONCLUSION

TGSE BLADE DW imaging exhibited less geometric distortion in the brain and reduced signal pile-ups in areas of high susceptibility than conventional SE-EPI.

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.

Childhood Medulloblastoma Revisited

Medulloblastoma is the most common malignant solid tumor in childhood and the most common embryonal neuroepithelial tumor of the central nervous system. Several morphological variants are recognized: classic medulloblastoma, large cell/anaplastic medulloblastoma, desmoplastic/nodular medulloblastoma, and medulloblastoma with extensive nodularity. Recent advances in transcriptome and methylome profiling of these tumors led to a molecular classification that includes 4 major genetically defined groups. Accordingly, the 2016 revision of the World Health Organization's Classification of Tumors of the Central Nervous System recognizes the following medulloblastoma entities: Wingless (WNT)-activated, Sonic hedgehog (SHH)-activated, Group 3, and Group 4. This transcriptionally driven classification constitutes the basis of new risk stratification schemes applied to current therapeutic clinical trials. Because additional layers of molecular tumor heterogeneities are being progressively unveiled, several clinically relevant subgroups within the 4 major groups have already been identified. The purpose of this article is to review the recent basic science and clinical advances in the understanding of "medulloblastomas," and their diagnostic imaging correlates and the implications of those on current neuroimaging practice.

Arterial spin labelling and diffusion-weighted imaging in paediatric brain tumours

BACKGROUND

Diffusion- and perfusion-weighted MRI are valuable tools for measuring the cellular and vascular properties of brain tumours. This has been well studied in adult patients, however, the biological features of childhood brain tumours are unique, and paediatric-focused studies are less common. We aimed to assess the diagnostic utility of apparent diffusion coefficient (ADC) values derived from diffusion-weighted imaging (DWI) and cerebral blood flow (CBF) values derived from arterial spin labelling (ASL) in paediatric brain tumours.

METHODS

We performed a meta-analysis of published studies reporting ADC and ASL-derived CBF values in paediatric brain tumours. Data were combined using a random effects model in order to define typical parameter ranges for different histological tumour subtypes and WHO grades. New data were also acquired in a 'validation cohort' at our institution, in which ADC and CBF values in treatment naïve paediatric brain tumour patients were measured, in order to test the validity of the findings from the literature in an un-seen cohort. ADC and CBF quantification was performed by two radiologists via manual placement of tumour regions of interest (ROIs), in addition to an automated approach to tumour ROI placement.

RESULTS

A total of 14 studies met the inclusion criteria for the meta-analysis, constituting data acquired in 542 paediatric patients. Parameters of interest were based on measurements from ROIs placed within the tumour, including mean and minimum ADC values (ADC_ROI-mean, ADC_ROI-min) and the maximum CBF value normalised to grey matter (nCBF_ROI-max). After combination of the literature data, a number of histological tumour subtype groups showed significant differences in ADC values, which were confirmed, where possible, in our validation cohort of 32 patients. In both the meta-analysis and our cohort, diffuse midline glioma was found to be an outlier among high-grade tumour subtypes, with ADC and CBF values more similar to the low-grade tumours. After grouping patients by WHO grade, significant differences in grade groups were found in ADC_ROI-mean, ADC_ROI-min, and nCBF_ROI-max, in both the meta-analysis and our validation cohort. After excluding diffuse midline glioma, optimum thresholds (derived from ROC analysis) for separating low/high-grade tumours were 0.95 × 10^-3 mm²/s (ADC_ROI-mean), 0.82 × 10^-3 mm²/s (ADC_ROI-min) and 1.45 (nCBF_ROI-max). These thresholds were able to identify low/high-grade tumours with 96%, 83%, and 83% accuracy respectively in our validation cohort, and agreed well with the results from the meta-analysis. Diagnostic power was improved by combining ADC and CBF measurements from the same tumour, after which 100% of tumours in our cohort were correctly classified as either low- or high-grade (excluding diffuse midline glioma).

CONCLUSION

ADC and CBF values are useful for differentiating certain histological subtypes, and separating low- and high-grade paediatric brain tumours. The threshold values presented here are in agreement with previously published studies, as well as a new patient cohort. If ADC and CBF values acquired in the same tumour are combined, the diagnostic accuracy is optimised.

Can apparent diffusion coefficient values help distinguish between different types of pediatric brain tumors?

Rationale and objectives

Classifying brain tumors is challenging, but recently developed imaging techniques offer the opportunity for neuroradiologists and neurosurgeons to diagnose, differentiate, and manage different types of brain tumors. Such advances will be reflected in improvements in patients’ life expectancy and quality of life. Among the newest techniques, the apparent diffusion coefficient (ADC), which tracks the rate of microscopic water diffusion within tissues, has become a focus of investigation. Recently, ADC has been used as a preoperative diffusion-weighted magnetic resonance imaging (MRI) parameter that facilitates tumor diagnosis and grading. Here, we aimed to determine the ADC cutoff values for pediatric brain tumors (PBTs) categorized according to the World Health Organization (WHO) classification of brain tumors.

Materials and methods

We retrospectively reviewed 80 cases, and assessed them based on their MRI-derived ADC. These results were compared with those of WHO classification-based histopathology.

Results

Whole-lesion ADC values ranged 0.225–1.240 × 10⁻³ mm²/s for ependymal tumors, 0.107–1.571 × 10⁻³ mm²/s for embryonal tumors, 0.1065–2.37801 × 10⁻³ mm²/s for diffuse astrocytic and oligodendroglial tumors, 0.5220–0.7840 × 10⁻³ mm²/s for other astrocytic tumors, and 0.1530–0.8160 × 10⁻³ mm²/s for meningiomas. These findings revealed the usefulness of ADC in the differential diagnosis of PBT, as it was able to discriminate between five types of PBTs.

Conclusion

The application of an ADC diagnostic criterion would reduce the need for spectroscopic analysis. However, further research is needed to implement ADC in the differential diagnosis of PBT.

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.

EANO guidelines for the diagnosis and treatment of ependymal tumors

Ependymal tumors are rare CNS tumors and may occur at any age, but their proportion among primary brain tumors is highest in children and young adults. Thus, the level of evidence of diagnostic and therapeutic interventions is higher in the pediatric compared with the adult patient population.The diagnosis and disease staging is performed by craniospinal MRI. Tumor classification is achieved by histological and molecular diagnostic assessment of tissue specimens according to the World Health Organization (WHO) classification 2016. Surgery is the crucial initial treatment in both children and adults. In pediatric patients with intracranial ependymomas of WHO grades II or III, surgery is followed by local radiotherapy regardless of residual tumor volume. In adults, radiotherapy is employed in patients with anaplastic ependymoma WHO grade III, and in case of incomplete resection of WHO grade II ependymoma. Chemotherapy alone is reserved for young children <12 months and for adults with recurrent disease when further surgery and irradiation are no longer feasible. A gross total resection is the mainstay of treatment in spinal ependymomas, and radiotherapy is reserved for incompletely resected tumors. Nine subgroups of ependymal tumors across different anatomical compartments (supratentorial, posterior fossa, spinal) and patient ages have been identified with distinct genetic and epigenetic alterations, and with distinct outcomes. These findings may lead to more precise diagnostic and prognostic assessments, molecular subgroup-adapted therapies, and eventually new recommendations pending validation in prospective studies.

Use of Preoperative Apparent Diffusion Coefficients to Predict Brain Tumor Grade

Introduction

The apparent diffusion coefficient (ADC) sequence is based on the diffusion properties of water molecules within tissues and correlates with tissue cellularity. ADC may have a role in predicting tumor grade for gliomas, and may in turn assist in identifying tumor biopsy sites. The purpose of this investigation was to assess the competence of preoperative ADC values in predicting tumor grades.

Methods

This was a retrospective investigation. We calculated the ADC values in the areas of greatest restriction in solid tumor components, and we recorded the pattern of contrast enhancement. Pathology reports masked to the imaging results were reviewed independently. We calculated the differences in the mean values of different tumor grades and high-grade and low-grade gliomas. A receiver operator curve (ROC) analysis assessed the predictive potential of ADC values for low-grade gliomas.

Results

Forty-eight cases of glioma were included in our study. We noted a statistically significant difference in the lowest mean ADC values for the tumor regions of Grade IV lesions (333.83 ± 295.47) compared with Grade I lesions (653.20 ± 145.07). On ROC analysis, we noted an area under the curve (AUC) of 0.80 for the lowest ADC value in the whole tumor region, which was a predictor of low-grade glioma with 95 % confidence interval (CI) of 0.675-0.926. The sensitivity of the lowest ADC value was 84.5% for high-grade lesions.

Conclusion

Given our findings that the means of the lowest ADC value are significantly different between low and high-grade gliomas with an AUC of 0.80 for ADC as a predictor of low-grade lesions and a sensitivity of 84.5% for high-grade lesions, ADC values contain some predictive properties of tumor grading. ADC values may be a valuable parameter in the assessment and treatment of tumors.