Advanced MRI for Pediatric Brain Tumors with Emphasis on Clinical Benefits

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.

MRI features of pediatric atypical teratoid rhabdoid tumors and medulloblastomas of the posterior fossa

BACKGROUND

Atypical teratoid rhabdoid tumor (AT/RT) occurs at a younger age and is associated with a worse prognosis than medulloblastoma; however, these two tumor entities are mostly indistinguishable on neuroimaging. The aim of our study was to differentiate AT/RT and medulloblastoma based on their clinical and MRI features to enhance treatment planning and outcome prediction.

METHODS

From 2005-2021, we retrospectively enrolled 16 patients with histopathologically diagnosed AT/RT and 57 patients with medulloblastoma at our institute. We evaluated their clinical data and MRI findings, including lesion signals, intratumoral morphologies, and peritumoral/distal involvement.

RESULTS

The age of children with AT/RT was younger than that of children with medulloblastoma (2.8 ± 4.9 [0-17] vs. 6.5 ± 4.0 [0-18], p < 0.001), and the overall survival rate was lower (21.4% vs. 66.0%, p = 0.005). Regarding lesion signals on MRI, AT/RT had a lower ADC_min (cutoff value ≤544.7 × 10^-6  mm² /s, p < 0.001), a lower ADC ratio (cutoff value ≤0.705, p < 0.001), and a higher DWI ratio (cutoff value ≥1.595, p < 0.001) than medulloblastoma. Regarding intratumoral morphology, the "tumor central vein sign" was mostly exclusive to medulloblastoma (24/57, 42.1%; AT/RT 1/16, 6.3%; p = 0.007). Regarding peritumoral invasion on T2WI, AT/RT was more prone to invasion of the brainstem (p < 0.001) and middle cerebellar peduncle (p < 0.001) than medulloblastoma.

CONCLUSIONS

MRI findings of a lower ADC value, more peritumoral invasion, and absence of the "tumor central vein sign" may be helpful to differentiate AT/RT from medulloblastoma. These distinct MRI findings together with the younger age of AT/RT patients may explain the worse outcomes in AT/RT patients.

[18F]FET-PET in children and adolescents with central nervous system tumors: does it support difficult clinical decision-making?

PURPOSE

Positron emission tomography (PET) with O-(2-[¹⁸F]fluoroethyl)-L-tyrosine ([¹⁸F]FET) is a well-established tool for non-invasive assessment of adult central nervous system (CNS) tumors. However, data on its diagnostic utility and impact on clinical management in children and adolescents are limited.

METHODS

Twenty-one children and young adults (13 males; mean age, 8.6 ± 5.2 years; range, 1-19 at initial diagnosis) with either newly diagnosed (n = 5) or pretreated (n = 16) CNS tumors were retrospectively analyzed. All patients had previously undergone neuro-oncological work-up including cranial magnetic resonance imaging. In all cases, [¹⁸F]FET-PET was indicated in a multidisciplinary team conference. The impact of PET imaging on clinical decision-making was assessed. Histopathology (n = 12) and/or clinical and imaging follow-up (n = 9) served as the standard of reference.

RESULTS

The addition of [¹⁸F]FET-PET to the available information had an impact on further patient management in 14 out of 21 subjects, with avoidance of invasive surgery or biopsy in four patients, biopsy guidance in four patients, change of further treatment in another five patients, and confirmation of diagnosis in one patient.

CONCLUSION

[¹⁸F]FET-PET may provide important additional information for treatment guidance in pediatric and adolescent patients with CNS tumors.

Revisiting the Role of Surgical Resection for Brain Metastasis

Brain metastasis (BM) is the most common type of brain tumor in adults. The contemporary management of BM remains challenging. Advancements in systemic cancer treatment have increased the survival of patients with cancer. Although the treatment of BM is still complicated, advances in radiotherapy, including stereotactic radiosurgery and chemotherapy, have improved treatment outcomes. Surgical resection is the traditional treatment for BM and its role in the surgical resection of BM has been well established. However, refinement of the surgical resection technique and strategy for BM is needed. Herein, we discuss the evolving role of surgery in patients with BM and the future of BM treatment.

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.

Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors

In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm (P < 0.05). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group (P < 0.05). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient's anxiety and ensure that high-quality MRI images were obtained after the examination.

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.

The Role of PET in Supratentorial and Infratentorial Pediatric Brain Tumors

OBJECTIVE

This review aims to provide a summary of the clinical indications and limitations of PET imaging with different radiotracers, including 18F-fluorodeoxyglucose (18F-FDG) and other radiopharmaceuticals, in pediatric neuro-oncology, discussing both supratentorial and infratentorial tumors, based on recent literature (from 2010 to present).

METHODS

A literature search of the PubMed/MEDLINE database was carried out searching for articles on the use of PET in pediatric brain tumors. The search was updated until December 2020 and limited to original studies published in English after 1 January 2010.

RESULTS

18F-FDG PET continues to be successfully employed in different settings in pediatric neuro-oncology, including diagnosis, grading and delineation of the target for stereotactic biopsy, estimation of prognosis, evaluation of recurrence, treatment planning and assessment of treatment response. Nevertheless, non-18F-FDG tracers, especially amino acid analogues seem to show a better performance in each clinical setting.

CONCLUSIONS

PET imaging adds important information in the diagnostic work-up of pediatric brain tumors. International or national multicentric studies are encouraged in order to collect larger amount of data.

Diffusion histology imaging differentiates distinct pediatric brain tumor histology

High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in their clinical management. We employed a novel Diffusion Histology Imaging (DHI) approach employing diffusion basis spectrum imaging (DBSI) derived metrics as the input classifiers for deep neural network analysis. DHI aims to detect, differentiate, and quantify heterogeneous areas in pediatric high-grade brain tumors, which include normal white matter (WM), densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis, and hemorrhage. Distinct diffusion metric combination would thus indicate the unique distributions of each distinct tumor histology features. DHI, by incorporating DBSI metrics and the deep neural network algorithm, classified pediatric tumor histology with an overall accuracy of 85.8%. Receiver operating analysis (ROC) analysis suggested DHI’s great capability in distinguishing individual tumor histology with AUC values (95% CI) of 0.984 (0.982–0.986), 0.960 (0.956–0.963), 0.991 (0.990–0.993), 0.950 (0.944–0.956), 0.977 (0.973–0.981) and 0.976 (0.972–0.979) for normal WM, densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis and hemorrhage, respectively. Our results suggest that DBSI-DNN, or DHI, accurately characterized and classified multiple tumor histologic features in pediatric high-grade brain tumors. If these results could be further validated in patients, the novel DHI might emerge as a favorable alternative to the current neuroimaging techniques to better guide biopsy and resection as well as monitor therapeutic response in patients with high-grade brain tumors.

Quantitative Imaging in Pediatric Hepatobiliary Disease

Pediatric hepatobiliary imaging is important for evaluation of not only congenital or structural disease but also metabolic or diffuse parenchymal disease and tumors. A variety of ultrasonography and magnetic resonance imaging (MRI) techniques can be used for these assessments. In ultrasonography, conventional ultrasound imaging as well as vascular imaging, elastography, and contrast-enhanced ultrasonography can be used, while in MRI, fat quantification, T2/T2^* mapping, diffusion-weighted imaging, magnetic resonance elastography, and dynamic contrast-enhanced MRI can be performed. These techniques may be helpful for evaluation of biliary atresia, hepatic fibrosis, nonalcoholic fatty liver disease, sinusoidal obstruction syndrome, and hepatic masses in children. In this review, we discuss each tool in the context of management of hepatobiliary disease in children, and cover various imaging techniques in the context of the relevant physics and their clinical applications for patient care.

Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study

The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types. The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.

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.

Safety of Gadolinium Administration in Children

The introduction of paramagnetic contrast in the late 1980s constituted a paradigm shift boosting the efficacy of magnetic resonance imaging. Due to its high magnetic moment, gadolinium-based contrast agent made its way smoothly as the flagship paramagnetic contrast. With the widespread application, reports of untoward effects started to surface. Allergic reactions, nephrogenic systemic sclerosis, and deposition in brain tissue dented the safety profile of gadolinium-based contrast agent. Better understanding of these adverse effects prompted preventive measures. This article elucidates the gadolinium-based contrast agent toxicity in the pediatric population based on the current available evidence.