Multiparametric differentiation of posterior fossa tumors in children using diffusion-weighted imaging and short echo-time 1H-MR spectroscopy

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.

A Comparison of 7 Tesla MR Spectroscopic Imaging and 3 Tesla MR Fingerprinting for Tumor Localization in Glioma Patients

This paper investigated the correlation between magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance fingerprinting (MRF) in glioma patients by comparing neuro-oncological markers obtained from MRSI to T1/T2 maps from MRF. Data from 12 consenting patients with gliomas were analyzed by defining hotspots for T1, T2, and various metabolic ratios, and comparing them using Sørensen-Dice similarity coefficients (DSCs) and the distances between their centers of intensity (COIDs). The median DSCs between MRF and the tumor segmentation were 0.73 (T1) and 0.79 (T2). The DSCs between MRSI and MRF were the highest for Gln/tNAA (T1: 0.75, T2: 0.80, tumor: 0.78), followed by Gly/tNAA (T1: 0.57, T2: 0.62, tumor: 0.54) and tCho/tNAA (T1: 0.61, T2: 0.58, tumor: 0.45). The median values in the tumor hotspot were T1 = 1724 ms, T2 = 86 ms, Gln/tNAA = 0.61, Gly/tNAA = 0.28, Ins/tNAA = 1.15, and tCho/tNAA = 0.48, and, in the peritumoral region, were T1 = 1756 ms, T2 = 102 ms, Gln/tNAA = 0.38, Gly/tNAA = 0.20, Ins/tNAA = 1.06, and tCho/tNAA = 0.38, and, in the NAWM, were T1 = 950 ms, T2 = 43 ms, Gln/tNAA = 0.16, Gly/tNAA = 0.07, Ins/tNAA = 0.54, and tCho/tNAA = 0.20. The results of this study constitute the first comparison of 7T MRSI and 3T MRF, showing a good correspondence between these methods.

Characterizing Various Posterior Fossa Tumors in Children and Adults With Diffusion-Weighted Imaging and Spectroscopy

Background The posterior fossa is situated between the tentorium cerebelli above and the foramen magnum below. Vital structures like the cerebellum, the pons, and the medulla are situated within it; hence, tumors within the posterior fossa are considered one of the most critical brain lesions. Children are more likely to develop posterior fossa tumors than adults. Diffusion-weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) sequences along with the conventional MRI help in providing additional information in the characterization of the various posterior fossa tumors. We hereby present a series of 30 patients with clinically suspected posterior fossa masses who underwent preoperative MRI. Objectives This study aims to differentiate the neoplastic from non-neoplastic posterior fossa mass by evaluating the diffusion restriction pattern on DWI, quantifying the apparent diffusion coefficient (ADC) map in various posterior fossa tumors, and comparing the different metabolites of various posterior fossa tumors on MRS. Results Out of the 30 patients with posterior fossa lesions, 18 were males and 12 were females. Eight of them were in the pediatric age group, while twenty-two of them were adults. Metastasis was the most common posterior fossa lesion in our study sample and was found in six patients (20%), followed by vestibular schwannomas (17%) and arachnoid cysts (13%), meningiomas, medulloblastoma, and pilocytic astrocytoma (10% each) and epidermoid, ependymoma, and hemangioblastoma (7% each). The mean ADC value of benign tumors was higher than that of malignant tumors, and this difference was found to be significant (p = 0.012). The cut-off ADC value 1.21x 10^-3mm²/s had a sensitivity of 81.82% and specificity of 80.47%. MRS metabolites played an additional role in differentiating benign from malignant tumors. Conclusion A combination of conventional MRI, DWI, ADC values, and MRS metabolites showed good diagnostic accuracy to differentiate between the various posterior fossa neoplastic tumors both in adults and children.

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.

Imaging Findings and Clinical Analysis of Primary Intracranial Pure Yolk Sac Tumors in Children and Adolescents: A Retrospective Study from China

BACKGROUND AND PURPOSE

Primary intracranial pure yolk sac tumor is very rare. Our aim was to summarize the characteristics of primary intracranial pure yolk sac tumors from the clinical and imaging aspects in a retrospective study.

MATERIALS AND METHODS

We studied 5 patients with primary intracranial pure yolk sac tumors in Guangzhou Women and Children's Medical Center from January 2015 to June 2021. A comprehensive literature search was performed on the electronic database of the China National Knowledge Infrastructure (1990 to June 2021). Clinical data based on age, sex, treatment, CT, and MR imaging findings were collected and analyzed.

RESULTS

A total of 25 patients were included in the study, 21 boys and 4 girls. Twenty-one patients underwent plain MR imaging and an enhanced examination, 9 patients underwent DWI, and 12 patients underwent plain CT and/or an enhanced examination. The tumors were posterior fossa in 9 cases and supratentorial in 16 cases. All tumors showed marked enhancement after enhanced scanning by MR imaging or CT. The signal on DWI was similar to that of the cerebral cortex, and the ADC map was similar to or slightly higher than that of the cerebral cortex. Among the cases, 13 were followed up from 2 months to 5 years. There was no recurrence or metastasis in 9 patients with postoperative chemotherapy or chemoradiotherapy followed up for 1.5-5 years. Four patients died 2 months to 1.5 years after only an operation, or chemoradiotherapy but no operation.

CONCLUSIONS

There are some relatively specific imaging findings of primary intracranial yolk sac tumors that could assist in their diagnosis. Surgery combined with radiation therapy and/or chemotherapy can achieve a better prognosis.

MR Imaging of Pediatric Brain Tumors

Primary brain tumors are the most common solid neoplasms in children and a leading cause of mortality in this population. MRI plays a central role in the diagnosis, characterization, treatment planning, and disease surveillance of intracranial tumors. The purpose of this review is to provide an overview of imaging methodology, including conventional and advanced MRI techniques, and illustrate the MRI appearances of common pediatric brain 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.

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.

Differentiation of low- and high-grade pediatric gliomas with amide proton transfer imaging: added value beyond quantitative relaxation times

OBJECTIVES

To evaluate whether amide proton transfer (APT) MRI can be used to characterize gliomas in pediatric patients and whether it provides added value beyond relaxation times.

METHODS

In this prospective study, APT imaging and relaxation time mapping were performed in 203 pediatric patients suspected of gliomas from February 2018 to December 2019. The region of interest (ROI) in the tumor was automatically generated with artifact detection and ROI-shrinking algorithms. Several APT-related metrics (CESTR, CESTR^nr, MTR_Rex, AREX, and APT^#) and quantitative T1 and T2 were compared between low-grade and high-grade gliomas using the student's t-test or Mann-Whitney U-test. The performance of these parameters was assessed using the receiver operating characteristic (ROC) analysis. A stepwise multivariate logistic regression model was used to combine the imaging parameters.

RESULTS

Forty-eight patients (mean age: 6 ± 4 years; 23 males and 25 females) were included in the final analysis. All the APT-related metrics except APT^# had significantly (p < 0.05) higher values in the high-grade group than the low-grade group. Under different ROI-shrinking cutoffs, the quantitative T1 (p = 0.045-0.200) and T2 (p = 0.037-0.171) values of high-grade gliomas were typically lower than those of low-grade ones. The stepwise multivariate logistic regression revealed that CESTR^nr and APT^# were combined significant predictors of glioma grades (p < 0.05), with an area under the ROC curve (AUC) of 0.86 substantially larger than those of T1 (AUC = 0.69) and T2 (AUC = 0.68).

CONCLUSIONS

APT imaging can be used to differentiate high-grade and low-grade gliomas in pediatric patients and provide added value beyond quantitative relaxation times.

KEY POINTS

• Amide proton transfer (APT) MRI showed significantly (p < 0.05) higher values in pediatric patients with high-grade gliomas than those with low-grade ones. • The area under the curve was 0.86 for APT MRI to differentiate low-grade and high-grade gliomas in pediatric patients, which was substantially higher than that for quantitative T1 (0.69) and T2 (0.68). • APT MRI demonstrated added value beyond quantitative T1 and T2 mapping in characterizing 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.

Classification of paediatric brain tumours by diffusion weighted imaging and machine learning

To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P < 0.001). A cut off value for mean ADC between Ependymomas and Medulloblastomas was found to be of 0.984 × 10⁻³ mm² s⁻¹ with sensitivity 80.8% and specificity 80.0%. Overall classification for the ADC histogram metrics were 85% using Naïve Bayes and 84% for Random Forest classifiers. The most commonly occurring posterior fossa paediatric brain tumours can be classified using Apparent Diffusion Coefficient histogram values to a high accuracy on a multicentre basis.

Role of diffusion-weighted imaging in differentiation between posterior fossa brain tumors

Background

Diffusion-weighted imaging (DWI) is an imaging modality using multi-section single-shot spin echo planar imaging (EPI) sequence which is extremely sensitive for detection of water motion within intra, extra, and transcellular regions. This character is important to differentiate between brain tumors either low (benign) or highly (malignant) cellular tumors.

Objective

To evaluate the role of DWI and apparent diffusion coefficient (ADC) in evaluation and differentiation between different brain posterior fossa tumors in children and adults.

Patients and methods

The study included 34 patients with different brain posterior fossa tumors for evaluation by conventional MRI (using 1.5 T MRI PHILIPS Achieva 2.1 Best Netherland) and DWI.

Results

Our study showed that mean ADC values were significantly different between the four groups of posterior fossa tumors in children: juvenile pilocytic astrocytoma (JPA), medulloblastoma, ependymoma, and brain stem glioma while mean ADC values were not significantly different between posterior fossa tumors in the adult group. Regions of interest were manually positioned, and all values were automatically calculated and expressed in 10−3 mm2/s.

Conclusion

DWI is an ideal additional imaging technique, which is a rapid, easy, non-invasive imaging modality, with no contrast injection needed. It has been widely applied in the differentiation between posterior fossa brain tumors and in the diagnosis of various intracranial diseases.

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.

Diagnostic accuracy and added value of qualitative radiological review of 1H-magnetic resonance spectroscopy in evaluation of childhood brain tumors

Background

¹H-magnetic resonance spectroscopy (MRS) facilitates noninvasive diagnosis of pediatric brain tumors by providing metabolite profiles. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. We aimed to evaluate diagnostic accuracy of MRS for childhood brain tumors and determine added clinical value compared with conventional MRI.

Methods

Children presenting to a tertiary pediatric center with brain lesions from December 2015 through 2017 were included. MRI and single-voxel MRS were acquired on 52 tumors and sequentially interpreted by 3 radiologists, blinded to histopathology. Proportions of correct diagnoses and interrater agreement at each stage were compared. Cases were reviewed to determine added value of qualitative radiological review of MRS through increased certainty of correct diagnosis, reduced number of differentials, or diagnosis following spectroscopist evaluation. Final diagnosis was agreed by the tumor board at study end.

Results

Radiologists' principal MRI diagnosis was correct in 69%, increasing to 77% with MRS. MRI + MRS resulted in significantly more additional correct diagnoses than MRI alone (P = .035). There was a significant increase in interrater agreement when correct with MRS (P = .046). Added value following radiologist interpretation of MRS occurred in 73% of cases, increasing to 83% with additional spectroscopist review. First histopathological diagnosis was available a median of 9.5 days following imaging, with 25% of all patients managed without conclusive histopathology.

Conclusions

MRS can improve the accuracy of noninvasive diagnosis of pediatric brain tumors and add value in the diagnostic pathway. Incorporation into practice has the potential to facilitate early diagnosis, guide treatment planning, and improve patient care.

The role of diffusion and perfusion magnetic resonance imaging in differentiation of haemangioblastomas and pilocytic astrocytomas

Purpose

Haemangioblastomas (HABLs) and pilocytic astrocytomas (PAs) are brain tumours presenting similar appearance and location in conventional magnetic resonance (MR) imaging. The purpose of our study was to determine whether a detailed analysis of diffusion (DWI) and perfusion (PWI) characteristics can be useful in preoperative differentiation of these tumours.

Material and methods

The study group consisted of biopsy proven six HABLs and six PAs, which underwent preoperative standard MR examinations including PWI and DWI. In PWI relative cerebral blood volume (rCBV) and the shape of perfusion curves (parameters of peak height - rPH and percentage of signal recovery - rPSR) were analysed. All perfusion parameters were measured for the entire tumour core (mean rCBV, mean rPH, mean rPSR) and in regions with maximal values (max rCBV, max rPH, max rPSR). In DWI parameters of apparent diffusion coefficient (ADC) from the entire tumour core (mean ADC) and in regions with minimal values (min ADC) were evaluated.

Results

Compared to PAs, HABLs presented significantly higher rCBV and rPH values and lower mean rPSR value. PAs showed significantly lower rCBV and rPH values and higher mean rPSR value. Mean rCBV showed no overlap in the values between HABLs and PAs, and thus it provided the highest accuracy in differentiating between them. Max rPSR, mean ADC, and min ADC did not show any significant differences.

Conclusions

High rCBV values and deep perfusion curves with only partial return to the baseline are characteristic features of HABLs differentiating them from PAs, which show lower rCBV values and perfusion curves overshooting the baseline. Diffusion parameters are not useful in differentiation of these tumours.

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.

Magnetic resonance spectroscopy in posterior fossa tumours: the tumour spectroscopic signature may improve discrimination in adults among haemangioblastoma, ependymal tumours, medulloblastoma, and metastasis

OBJECTIVES

Assessing a posterior fossa tumour in an adult can be challenging. Metastasis, haemangioblastoma, ependymal tumours, and medulloblastoma are the most common diagnostic possibilities. Our aim was to evaluate the contribution of magnetic resonance spectroscopy (MRS) in the diagnosis of these entities.

METHODS

We retrospectively evaluated 56 consecutive patients with a posterior fossa tumour and histological diagnosis of ependymal tumour, medulloblastoma, haemangioblastoma, and metastasis in which good-quality spectra at short (TE 30 ms) or/and intermediate (TE, 136 ms) TE were available. Spectra were compared using the Mann-Whitney U non-parametric test in order to select the spectral datapoints and the intensity ratios that showed significant differences between groups of lesions. Performance of these datapoints and their ratios were assessed with ROC curves.

RESULTS

The most characteristic signatures on spectroscopy were high choline (Cho) in medulloblastoma (p < 0.001), high myoinositol (mIns) in ependymal tumours (p < 0.05), and high lipids (LIP) in haemangioblastoma (p < 0.01) and metastasis (p < 0.01). Selected ratios between normalised intensity signals of resonances provided accuracy values between 79 and 95% for pairwise comparisons. Intensity ratio NI_{3.21ppm/3.55ppm} provided satisfactory discrimination between medulloblastoma and ependymal tumours (accuracy, 92%), ratio NI_{2.11ppm/1.10ppm} discriminated ependymal tumours from haemangioblastoma (accuracy, 94%), ratio NI_{3.21ppm/1.13ppm} discriminated haemangioblastoma from medulloblastoma (accuracy, 95%), and ratio NI_{1.28ppm/2.02pmm} discriminated haemangioblastoma from metastasis (accuracy, 83%).

CONCLUSIONS

MRS may improve the non-invasive diagnosis of posterior fossa tumours in adults.

KEY POINTS

• High choline suggests a medulloblastoma in a posterior fossa tumour. • High myoinositol suggests an ependymal lesion in a posterior fossa tumour. • High lipids suggest a metastasis or a haemangioblastoma in a posterior fossa tumour.

Use of Apparent Diffusion Coefficient Histogram in Differentiating Between Medulloblastoma and Pilocytic Astrocytoma in Children

Background

This research aimed to investigate the value of apparent diffusion coefficient (ADC) histogram in differentiating between medulloblastoma and pilocytic astrocytoma in children.

Material/Methods

Thirty-three children with posterior cranial fossa tumor confirmed by operation and pathology participated in this retrospective study, including 18 children with medulloblastoma and 15 children with pilocytic astrocytoma. ADC images of the maximum lay of tumors were selected, and the region of interest was delineated by Mazda software and analyzed by histogram. Histogram characteristic parameters of the 2 tumors were statistically analyzed to determine the significantly different characteristic parameters between the 2 tumor types.

Results

There were significant differences in the mean value, variance, skewness, kurtosis, and 1^th, 10^th, 50^th, and 90^th percentiles of 9 characteristic parameters extracted by histogram (P<0.05). The corresponding receiver operating characteristic (ROC) curves were drawn, in which the mean value and 50^th percentile were best identified. When the maximum area under the ROC curve was 1 and the optimal threshold was 137.7 and 125.5, the specificity and sensitivity were both 100%.

Conclusions

ADC histograms can be used to differentiate between medulloblastoma and pilocytic astrocytoma in children and provide reliable and objective evidence for the differentiation.

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.

Clinical applications of diffusion weighted imaging in neuroradiology

Abstract

Diffusion-weighted imaging (DWI) has revolutionised stroke imaging since its introduction in the mid-1980s, and it has also become a pillar of current neuroimaging. Diffusion abnormalities represent alterations in the random movement of water molecules in tissues, revealing their microarchitecture, and occur in many neurological conditions. DWI provides useful information, increasing the sensitivity of MRI as a diagnostic tool, narrowing the differential diagnosis, providing prognostic information, aiding in treatment planning and evaluating response to treatment. Recently, there have been several technical improvements in DWI, leading to reduced acquisition time and artefacts and enabling the development of diffusion tensor imaging (DTI) as a tool for assessing white matter. We aim to review the main clinical uses of DWI, focusing on the physiological mechanisms that lead to diffusion abnormalities. Common pitfalls will also be addressed.

Teaching Points

• DWI includes EPI, TSE, RESOLVE or EPI combined with reduced volume excitation.

• DWI is the most sensitive sequence in stroke diagnosis and provides information about prognosis.

• DWI helps in the detection of intramural haematomas (arterial dissection).

• In diffusion imaging, ADC is inversely proportional to tumour cellularity.

• DWI and DTI derived parameters can be used as biomarkers in different pathologies.

Diagnostic accuracy of diffusion weighted imaging for differentiation of supratentorial pilocytic astrocytoma and pleomorphic xanthoastrocytoma

Purpose

Supratentorial pilocytic astrocytoma (PA) may mimic pleomorphic xanthoastrocytoma (PXA) on conventional MR imaging, and a differentiation is clinically important because of distinct recurrence rate and anaplastic transformation rate. The purpose of this study was to investigate the diagnostic potential of diffusion-weighted imaging (DWI) in differentiating supratentorial PA from PXA.

Methods

We retrospectively reviewed DWI and conventional MR imaging of 16 patients with supratentorial PA and 8 patients with PXA. Variables of mean ADC values (ADC_mean) and minimum ADC values (ADC_min) were calculated from the ROIs containing the contrast-enhancing lesion on DWI. ADC_mean values and ADC_min values were compared among all supratentorial PA and PXA as well as between the subgroup of lobar PA and PXA by using an unpaired Student’s t test. The optimum threshold, sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) were determined.

Results

Both ADC_mean values (1542 ± 186 vs 1084 ± 201 × 10⁻⁶ mm²/s; P < 0.001) and ADC_min values (1355 ± 183 vs 988 ± 180 × 10⁻⁶ mm²/s; P < 0.001) were significantly higher in supratentorial PA compared with PXA. The ADC_mean values and ADC_min values were also significantly higher in lobar PA than those in PXA. The ADC_mean values were useful for differentiating supratentorial PA from PXA, with a threshold value of > 1189.8 × 10⁻⁶ mm²/s (sensitivity, 93.8%; specificity, 100%). The optimal threshold values of > 1189.8 × 10⁻⁶ mm²/s for ADC_mean values provide sensitivity and specificity of 85.7 and 100%, respectively, for discriminating lobar PA from PXA. The optimum threshold value for ADC_min was > 1063.5 × 10⁻⁶ mm²/s.

Conclusion

DWI is helpful in characterization and differentiation of supratentorial PA from PXA.

Electronic supplementary material

The online version of this article (10.1007/s00234-018-2036-y) contains supplementary material, which is available to authorized users.

Early detection of recurrent medulloblastoma: the critical role of diffusion-weighted imaging

Background

Imaging diagnosis of medulloblastoma recurrence relies heavily on identifying new contrast-enhancing lesions on surveillance imaging, with diffusion-weighted imaging (DWI) being used primarily for detection of complications. We propose that DWI is more sensitive in detecting distal and leptomeningeal recurrent medulloblastoma than T1-weighted postgadolinium imaging.

Methods

We identified 53 pediatric patients with medulloblastoma, 21 of whom developed definitive disease recurrence within the brain. MRI at diagnosis of recurrence and 6 months prior was evaluated for new lesions with reduced diffusion on DWI, contrast enhancement, size, and recurrence location.

Results

All recurrent medulloblastoma lesions demonstrated reduced diffusion. Apparent diffusion coefficient (ADC) measurements were statistically significantly lower (P = .00001) in recurrent lesions (mean=0.658, SD=0.072) as compared to contralateral normal region of interest (mean=0.923, SD=0.146). Sixteen patients (76.2%) with disease recurrence demonstrated contrast enhancement within the recurrent lesions. All 5 patients with nonenhancing recurrence demonstrated reduced diffusion, with a mean ADC of 0.695 ± 0.101 (normal=0.893 ± 0.100, P = .0027). While group 3 and group 4 molecular subtypes demonstrated distal recurrence more frequently, nonenhancing metastatic disease was found in all molecular subtypes.

Conclusion

Recurrent medulloblastoma lesions do not uniformly demonstrate contrast enhancement on MRI, but all demonstrate reduced diffusion. Our findings support that DWI is more sensitive than contrast enhancement for detection of medulloblastoma recurrence, particularly in cases of leptomeningeal nonenhancing disease and distal nonenhancing focal disease. As such, recurrent medulloblastoma can present as a reduced diffusion lesion in a patient with normal postgadolinium contrast MRI.

The Significance of Lactate and Lipid Peaks for Predicting Primary Neuroepithelial Tumor Grade with Proton MR Spectroscopy

Purpose:

¹H-MRS is a non-invasive technique used to assess the metabolic activity of brain tumors. The technique is useful for the preoperative prediction of tumor grade, which is important for treatment planning and accurate prognosis. We used ¹H-MRS to study the lactate peak, which appears in various conditions, including hyperglycemia, ischemia, and hypoxia and lipid peak, which is associated with necrotic cells. The purpose of this study was to retrospectively examine the frequency and significance of lactate and lipid peaks in relation to brain tumor grade.

Materials and Methods:

Fifty-five patients diagnosed with neuroepithelial tumors of Grades I (3 cases), II (11 cases), III (15 cases), and IV (26 cases) were enrolled. Biopsies were excluded. Single voxel (TE = 144 ms) point resolved ¹H-MRS spectroscopy sequences were retrospectively analyzed. An inverted doublet peak at 1.3 ppm was defined as lactate, a negative and positive peak was defined as combined lactate and lipid, and a clear upward peak was defined as lipid.

Results:

Lactate peaks were detected in all grades of brain tumors and were least common in Grade II tumors (9.1%). The frequency of combined lactate-lipid peaks was 0% (Grades I and II), 8.3% (Grade III), and 44% (Grade IV). Grade IV tumors were significantly different to the other grades. There were three cases with a lipid peak. All were glioblastoma.

Conclusions:

The presence of a lac peak may be useful to largely rule out the Grade II tumors, and allow the subsequent differentiation of Grade I tumors from Grade III or IV tumors by conventional imaging. The presence of a lipid peak may be associated with Grade IV tumors.

Adult Brain Tumors: Clinical Applications of Magnetic Resonance Spectroscopy

Proton magnetic resonance spectroscopy (H-MRS) may be helpful in suggesting tumor histology and tumor grade and may better define tumor extension and the ideal site for biopsy compared with conventional magnetic resonance (MR) imaging. A multifunctional approach with diffusion-weighted imaging, perfusion-weighted imaging, and permeability maps, along with H-MRS, may enhance the accuracy of the diagnosis and characterization of brain tumors and estimation of therapeutic response. Integration of advanced imaging techniques with conventional MR imaging and the clinical history help to improve the accuracy, sensitivity, and specificity in differentiating tumors and nonneoplastic lesions.

Posterior Fossa Tumors

Pediatric brain tumors are the leading cause of death from solid tumors in childhood. The most common posterior fossa tumors in children are medulloblastoma, atypical teratoid/rhabdoid tumor, cerebellar pilocytic astrocytoma, ependymoma, and brainstem glioma. Location, and imaging findings on computed tomography (CT) and conventional MR (cMR) imaging may provide important clues to the most likely diagnosis. Moreover, information obtained from advanced MR imaging techniques increase diagnostic confidence and help distinguish between different histologic tumor types. Here we discuss the most common posterior fossa tumors in children, including typical imaging findings on CT, cMR imaging, and advanced MR imaging studies.

MR imaging of brain pilocytic astrocytoma: beyond the stereotype of benign astrocytoma

BACKGROUND

Pilocytic astrocytoma (PA) is the most common pediatric brain glioma and is considered the prototype of benign circumscribed astrocytoma. Despite its low malignancy, the CT and MRI features of brain PA may resemble those of much more aggressive brain tumors. Misdiagnosis of PA is particularly easy when it demonstrates MR morphological and non-morphological findings that are inconsistent with its non-aggressive nature and that overlap with the features of more aggressive brain tumors.

METHOD

Basing on the evidence that the variation in the histological, genetic, and metabolic "fingerprint" for brain PA is dependent on tumor location, and the hypothesis that tumor location is related to the broad spectrum of morphological and non-morphological MR imaging findings, the authors discuss the MR imaging appearance of brain PA using a location-based approach to underline the typical and less typical imaging features and the main differential diagnosis of brain PA. A brief summary of the main pathological and clinical features, the natural history, and the treatment of brain PA is also provided.

RESULT

A combination of morphological and non-morphological MR imaging features and a site-based approach to differential diagnosis are required for a pre-operative diagnosis. The new "cutting-edge" MR imaging sequences have the potential to impact the ease and confidence of pediatric brain tumor interpretation and offer a more efficient diagnostic work-up.

CONCLUSIONS

Although the typical imaging features of brain pilocytic astrocytoma make radiological diagnosis relatively easy, an atypical and more aggressive appearance can lead to misdiagnosis. Knowing the broad spectrum of imaging characteristics on conventional and advanced MR imaging is important for accurate pre-operative radiological diagnosis and correctly interpreting changes during follow-up.

Magnetic resonance imaging based functional imaging in paediatric oncology

Imaging is central to management of solid tumours in children. Conventional magnetic resonance imaging (MRI) is the standard imaging modality for tumours of the central nervous system (CNS) and limbs and is increasingly used in the abdomen. It provides excellent structural detail, but imparts limited information about tumour type, aggressiveness, metastatic potential or early treatment response. MRI based functional imaging techniques, such as magnetic resonance spectroscopy, diffusion and perfusion weighted imaging, probe tissue properties to provide clinically important information about metabolites, structure and blood flow. This review describes the role of and evidence behind these functional imaging techniques in paediatric oncology and implications for integrating them into routine clinical practice.

Advanced MR imaging in hemispheric low-grade gliomas before surgery; the indications and limits in the pediatric age

INTRODUCTION

Advanced magnetic resonance imaging (MRI) techniques is an umbrella term that includes diffusion (DWI) and diffusion tensor (DTI), perfusion (PWI), spectroscopy (MRS), and functional (fMRI) imaging. These advanced modalities have improved the imaging of brain tumors and provided valuable additional information for treatment planning. Despite abundant literature on advanced MRI techniques in adult brain tumors, few reports exist for pediatric brain ones, potentially because of technical challenges.

REVIEW OF THE LITERATURE

The authors review techniques and clinical applications of DWI, PWI, MRS, and fMRI, in the setting of pediatric hemispheric low-grade gliomas.

PERSONAL EXPERIENCE

The authors propose their personal experience to highlight benefits and limits of advanced MR imaging in diagnosis, grading, and presurgical planning of pediatric hemispheric low-grade gliomas.

DISCUSSION

Advanced techniques should be used as complementary tools to conventional MRI, and in theory, the combined use of the three techniques should ensure achieving the best results in the diagnosis of hemispheric low-grade glioma and in presurgical planning to maximize tumor resection and preserve brain function.

FUTURE PERSPECTIVES

In the setting of pediatric neurooncology, these techniques can be used to distinguish low-grade from high-grade tumor. However, these methods have to be applied on a large scale to understand their real potential and clinical relapse, and further technical development is required to reduce the excessive scan times and other technical limitations.

Refined modelling of the short-T2 signal component and ensuing detection of glutamate and glutamine in short-TE, localised, (1) H MR spectra of human glioma measured at 3 T

Short-TE (1) H MRS has great potential for brain cancer diagnostics. A major difficulty in the analysis of the spectra is the contribution from short-T2 signal components, mainly coming from mobile lipids. This complicates the accurate estimation of the spectral parameters of the resonance lines from metabolites, so that a qualitative to semi-quantitative interpretation of the spectra dominates in practice. One solution to overcome this difficulty is to measure and estimate the short-T2 signal component and to subtract it from the total signal, thus leaving only the metabolite signals. The technique works well when applied to spectra obtained from healthy individuals, but requires some optimisation during data acquisition. In the clinical setting, time constraints hardly allow this. Here, we propose an iterative estimation of the short-T2 signal component, acquired in a single acquisition after measurement of the full spectrum. The method is based on QUEST (quantitation based on quantum estimation) and allows the refinement of the estimate of the short-T2 signal component after measurement. Thus, acquisition protocols used on healthy volunteers can also be used on patients without further optimisation. The aim is to improve metabolite detection and, ultimately, to enable the estimation of the glutamine and glutamate signals distinctly. These two metabolites are of great interest in the characterisation of brain cancer, gliomas in particular. When applied to spectra from healthy volunteers, the new algorithm yields similar results to QUEST and direct subtraction of the short-T2 signal component. With patients, up to 12 metabolites and, at least, seven can be quantified in each individual brain tumour spectrum, depending on the metabolic state of the tumour. The refinement of the short-T2 signal component significantly improves the fitting procedure and produces a separate short-T2 signal component that can be used for the analysis of mobile lipid resonances. Thus, in brain tumour spectra, distinct estimates of signals from glutamate and glutamine are possible. Copyright © 2016 John Wiley & Sons, Ltd.

Differentiation of Low- and High-Grade Gliomas Using High b-Value Diffusion Imaging with a Non-Gaussian Diffusion Model

BACKGROUND AND PURPOSE

Imaging-based tumor grading is highly desirable but faces challenges in sensitivity, specificity, and diagnostic accuracy. A recently proposed diffusion imaging method by using a fractional order calculus model offers a set of new parameters to probe not only the diffusion process itself but also intravoxel tissue structures, providing new opportunities for noninvasive tumor grading. This study aimed to demonstrate the feasibility of using the fractional order calculus model to differentiate low- from high-grade gliomas in adult patients and illustrate its improved performance over a conventional diffusion imaging method using ADC (or D).

MATERIALS AND METHODS

Fifty-four adult patients (18-70 years of age) with histology-proved gliomas were enrolled and divided into low-grade (n = 24) and high-grade (n = 30) groups. Multi-b-value diffusion MR imaging was performed with 17 b-values (0-4000 s/mm(2)) and was analyzed by using a fractional order calculus model. Mean values and SDs of 3 fractional order calculus parameters (D, β, and μ) were calculated from the normal contralateral thalamus (as a control) and the tumors, respectively. On the basis of these values, the low- and high-grade glioma groups were compared by using a Mann-Whitney U test. Receiver operating characteristic analysis was performed to assess the performance of individual parameters and the combination of multiple parameters for low- versus high-grade differentiation.

RESULTS

Each of the 3 fractional order calculus parameters exhibited a statistically higher value (P ≤ .011) in the low-grade than in the high-grade gliomas, whereas there was no difference in the normal contralateral thalamus (P ≥ .706). The receiver operating characteristic analysis showed that β (area under the curve = 0.853) produced a higher area under the curve than D (0.781) or μ (0.703) and offered a sensitivity of 87.5%, specificity of 76.7%, and diagnostic accuracy of 82.1%.

CONCLUSIONS

The study demonstrated the feasibility of using a non-Gaussian fractional order calculus diffusion model to differentiate low- and high-grade gliomas. While all 3 fractional order calculus parameters showed statistically significant differences between the 2 groups, β exhibited a better performance than the other 2 parameters, including ADC (or D).

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

Childhood brain tumors show great histological variability. The goal of this retrospective study was to assess the diagnostic accuracy of multimodal MR imaging (diffusion, perfusion, MR spectroscopy) in the distinction of pediatric brain tumor grades and types. Seventy-six patients (range 1 month to 18 years) with brain tumors underwent multimodal MR imaging. Tumors were categorized by grade (I-IV) and by histological type (A-H). Multivariate statistical analysis was performed to evaluate the diagnostic accuracy of single and combined MR modalities, and of single imaging parameters to distinguish the different groups. The highest diagnostic accuracy for tumor grading was obtained with diffusion-perfusion (73.24%) and for tumor typing with diffusion-perfusion-MR spectroscopy (55.76%). The best diagnostic accuracy was obtained for tumor grading in I and IV and for tumor typing in embryonal tumor and pilocytic astrocytoma. Poor accuracy was seen in other grades and types. ADC and rADC were the best parameters for tumor grading and typing followed by choline level with an intermediate echo time, CBV for grading and Tmax for typing. Multiparametric MR imaging can be accurate in determining tumor grades (primarily grades I and IV) and types (mainly pilocytic astrocytomas and embryonal tumors) in children.

Histogram Analysis of Diffusion Tensor Imaging Parameters in Pediatric Cerebellar Tumors

BACKGROUND AND PURPOSE

Apparent diffusion coefficient (ADC) values have been shown to assist in differentiating cerebellar pilocytic astrocytomas and medulloblastomas. Previous studies have applied only ADC measurements and calculated the mean/median values. Here we investigated the value of diffusion tensor imaging (DTI) histogram characteristics of the entire tumor for differentiation of cerebellar pilocytic astrocytomas and medulloblastomas.

METHODS

Presurgical DTI data were analyzed with a region of interest (ROI) approach to include the entire tumor. For each tumor, histogram-derived metrics including the 25th percentile, 75th percentile, and skewness were calculated for fractional anisotropy (FA) and mean (MD), axial (AD), and radial (RD) diffusivity. The histogram metrics were used as primary predictors of interest in a logistic regression model. Statistical significance levels were set at p < .01.

RESULTS

The study population included 17 children with pilocytic astrocytoma and 16 with medulloblastoma (mean age, 9.21 ± 5.18 years and 7.66 ± 4.97 years, respectively). Compared to children with medulloblastoma, children with pilocytic astrocytoma showed higher MD (P = .003 and P = .008), AD (P = .004 and P = .007), and RD (P = .003 and P = .009) values for the 25th and 75th percentile. In addition, histogram skewness showed statistically significant differences for MD between low- and high-grade tumors (P = .008).

CONCLUSIONS

The 25th percentile for MD yields the best results for the presurgical differentiation between pediatric cerebellar pilocytic astrocytomas and medulloblastomas. The analysis of other DTI metrics does not provide additional diagnostic value. Our study confirms the diagnostic value of the quantitative histogram analysis of DTI data in pediatric neuro-oncology.

Differentiation of Low- and High-Grade Pediatric Brain Tumors with High b-Value Diffusion-weighted MR Imaging and a Fractional Order Calculus Model

PURPOSE

To demonstrate that a new set of parameters (D, β, and μ) from a fractional order calculus (FROC) diffusion model can be used to improve the accuracy of MR imaging for differentiating among low- and high-grade pediatric brain tumors.

MATERIALS AND METHODS

The institutional review board of the performing hospital approved this study, and written informed consent was obtained from the legal guardians of pediatric patients. Multi-b-value diffusion-weighted magnetic resonance (MR) imaging was performed in 67 pediatric patients with brain tumors. Diffusion coefficient D, fractional order parameter β (which correlates with tissue heterogeneity), and a microstructural quantity μ were calculated by fitting the multi-b-value diffusion-weighted images to an FROC model. D, β, and μ values were measured in solid tumor regions, as well as in normal-appearing gray matter as a control. These values were compared between the low- and high-grade tumor groups by using the Mann-Whitney U test. The performance of FROC parameters for differentiating among patient groups was evaluated with receiver operating characteristic (ROC) analysis.

RESULTS

None of the FROC parameters exhibited significant differences in normal-appearing gray matter (P ≥ .24), but all showed a significant difference (P < .002) between low- (D, 1.53 μm(2)/msec ± 0.47; β, 0.87 ± 0.06; μ, 8.67 μm ± 0.95) and high-grade (D, 0.86 μm(2)/msec ± 0.23; β, 0.73 ± 0.06; μ, 7.8 μm ± 0.70) brain tumor groups. The combination of D and β produced the largest area under the ROC curve (0.962) in the ROC analysis compared with individual parameters (β, 0.943; D,0.910; and μ, 0.763), indicating an improved performance for tumor differentiation.

CONCLUSION

The FROC parameters can be used to differentiate between low- and high-grade pediatric brain tumor groups. The combination of FROC parameters or individual parameters may serve as in vivo, noninvasive, and quantitative imaging markers for classifying pediatric brain tumors.

Evaluating the apparent diffusion coefficient in MRI studies as a means of determining paediatric brain tumour stages

BACKGROUND

The apparent diffusion coefficient (ADC) in MRI seems to be related to cellularity in brain tumours. Its utility as a tool for distinguishing between histological types and tumour stages remains controversial.

PROCEDURES

We retrospectively evaluated children diagnosed with CNS tumours between January 2008 and December 2013. Data collected were age, sex, histological diagnosis, and location of the tumour. We evaluated the ADC and ADC ratio and correlated those values with histological diagnoses.

RESULTS

The study included 55 patients with a median age of 6 years. Histological diagnoses were pilocytic astrocytoma (40%), anaplastic ependymoma (16.4%), ganglioglioma (10.9%), glioblastoma (7.3%), medulloblastoma (5.5%), and other (20%). Tumours could also be classified as low-grade (64%) or high-grade (36%). Mean ADC was 1.3 for low-grade tumours and 0.9 for high-grade tumours (p=.004). Mean ADC ratios were 1.5 and 1.2 for low and high-grade tumours respectively (p=.025). There were no significant differences in ADC/ADC ratio between different histological types.

CONCLUSION

ADC and ADC ratio may be useful in imaging-study based differential diagnosis of low and high-grade tumours, but they are not a substitute for an anatomical pathology study.

Comparison of perfusion, diffusion, and MR spectroscopy between low-grade enhancing pilocytic astrocytomas and high-grade astrocytomas

BACKGROUND AND PURPOSE

The differentiation of pilocytic astrocytomas and high-grade astrocytomas is sometimes difficult. There are limited comparisons in the literature of the advanced MR imaging findings of pilocytic astrocytomas versus high-grade astrocytomas. The purpose of this study was to assess the MR imaging, PWI, DWI, and MR spectroscopy characteristics of pilocytic astrocytomas compared with high-grade astrocytomas.

MATERIALS AND METHODS

Sixteen patients with pilocytic astrocytomas and 22 patients with high-grade astrocytomas (8-66 years of age; mean, 36 ± 17 years) were evaluated by using a 1.5T MR imaging unit. MR imaging, PWI, DWI, and MR spectroscopy were used to determine the differences between pilocytic astrocytomas and high-grade astrocytomas. The sensitivity, specificity, and the area under the receiver operating characteristic curve of all analyzed parameters at respective cutoff values were determined.

RESULTS

The relative cerebral blood volume values were significantly lower in pilocytic astrocytomas compared with the high-grade astrocytomas (1.4 ± 0.9 versus 3.3 ± 1.4; P = .0008). The ADC values were significantly higher in pilocytic astrocytomas compared with high-grade astrocytomas (1.5 × 10(-3) ± 0.4 versus 1.2 × 10(-3) ± 0.3; P = .01). The lipid-lactate in tumor/creatine in tumor ratios were significantly lower in pilocytic astrocytomas compared with high-grade astrocytomas (8.3 ± 11.2 versus 43.3 ± 59.2; P = .03). The threshold values ≥1.33 for relative cerebral blood volume provide sensitivity, specificity, positive predictive values, and negative predictive values of 100%, 67%, 87%, and 100%, respectively, for differentiating high-grade astrocytomas from pilocytic astrocytomas. The optimal threshold values were ≤1.60 for ADC, ≥7.06 for lipid-lactate in tumor/creatine in tumor, and ≥2.11 for lipid-lactate in tumor/lipid-lactate in normal contralateral tissue.

CONCLUSIONS

Lower relative cerebral blood volume and higher ADC values favor a diagnosis of pilocytic astrocytoma, while higher lipid-lactate in tumor/creatine in tumor ratios plus necrosis favor a diagnosis of high-grade astrocytomas.

Conventional magnetic resonance imaging in the differentiation between high and low-grade brain tumours in paediatric patients

OBJECTIVE

It has been described that hyperintensity in diffusion-weighted imaging (DWI) correlates with high-grade tumours, and high signal-intensity in T2-weighted (T2w) images identifies low-grade tumours. We aimed to investigate the potential of routine conventional MRI sequences, such as DWI and T2-w, to pre-operatively distinguish between low-grade and high-grade brain tumours in paediatric patients.

MATERIAL AND METHODS

Two raters, blinded to the histological diagnosis, rated the aspect and signal intensity of MR images (T2w and DWI) from 37 children with newly diagnosed brain tumours. Histological diagnoses included 18 low-grade and 19 high-grade brain tumours.

RESULTS

The inter-rater agreement was 81-95%. High-grade tumours were never hypointense on DWI and low-grade tumours were usually hyperintense on T2w. Specificity was 100% for low-grade tumours and 90% for high-grade tumours. About 95% of the high-grade tumours and about 70% of the low-grade tumours were correctly diagnosed.

CONCLUSION

The combination of general morphological aspect of the tumours and signals on T2-w and DWI yield a high accuracy of pre-operative differentiation between low-grade and high-grade paediatric tumours.

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors

BACKGROUND AND PURPOSE

Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features.

MATERIALS AND METHODS

This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data.

RESULTS

ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix.

CONCLUSIONS

Support vector machine-based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology.

Apparent diffusion coefficient of pediatric cerebellar tumors: a biomarker of tumor grade?

BACKGROUND

The role of diffusion weighted imaging (DWI) to reliably differentiate tumor types and grades in pediatric cerebellar tumors is controversial. We aimed to clarify the discrepancy reported in previous articles.

PROCEDURES

We retrospectively evaluated the apparent diffusion coefficient (ADC) values of the enhancing, solid parts of cerebellar tumors and correlated the absolute tumor ADC values and cerebellar and thalamic ratios with histology in a cohort of children with cerebellar tumors.

RESULTS

Twenty-four children (12 females) were included in the study. The median age at pre-surgical MRI was 10 years (range 29 days-18.5 years). Absolute ADC values (mean 1.49, SD 0.25 vs. 0.63 ± 0.18), cerebellar (2.04 ± 0.33 vs. 0.83 ± 0.25), and thalamic ratio (1.98 ± 0.35 vs. 0.79 ± 0.23) were significantly higher in low- than in high-grade tumors (P < 0.0001). Absolute ADC values and cerebellar and thalamic ratios were significantly higher in low-grade astrocytomas than in MBs. Overlap was seen for WHO grade II and III ependymomas. One hundred percent specific cutoff ADC values of >1.2 × 10(3) and <0.8 × 10(-3) mm(2) /s were established for low- and high-grade tumors.

CONCLUSION

ADC analysis of the solid, contrast enhancing components of pediatric cerebellar tumors may facilitate differentiation between various tumor histologies.