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

Incidental brain tumor findings in children: prevalence, natural history, management, controversies, challenges, and dilemmas

Incidental brain tumor findings in children involve the unexpected discovery of brain lesions during imaging for unrelated reasons. These findings differ significantly from those in adults, requiring a focus on pediatric-specific approaches in neurosurgery, neuroimaging, and neuro-oncology. Understanding the prevalence, progression, and management of these incidentalomas is crucial for informed decision-making, balancing patient welfare with the risks and benefits of intervention. Incidental brain tumors are observed in about 0.04-5.7% of cases, with most suspected low-grade lesions in children showing a benign course, though up to 3% may undergo malignant transformation. Treatment decisions are influenced by factors such as patient age, tumor characteristics, and family anxiety, with conservative management through surveillance often preferred. However, upfront surgery may be considered in cases with low surgical risk. Initial follow-up typically involves a comprehensive MRI after three months, with subsequent scans spaced out if the lesion remains stable. Changes in imaging or symptoms during follow-up could indicate malignant transformation, prompting consideration of surgery or biopsy. Several challenges and controversies persist, including the role of upfront biopsy for molecular profiling, the use of advanced imaging techniques like PET-CT and magnetic resonance spectroscopy, and the implications of the child's age at diagnosis. These issues highlight the need for further research to guide management and improve outcomes in pediatric patients with incidental brain tumor findings.

Multi-modal 3-Dimensional Visualization of Pediatric Neuroblastoma: Aiding Surgical Planning Beyond Anatomical Information

BACKGROUND

Patient-specific 3D models of neuroblastoma and relevant anatomy are useful tools for surgical planning. However, these models do not represent the heterogenous biology of neuroblastoma. This heterogeneity is visualized with the ADC and 123I-MIGB-SPECT-CT imaging. Combining these multi-modal data into preoperative 3D heatmaps, may allow differentiation of the areas of vital and non-vital tumor tissue. We developed a workflow to create multi-modal preoperative 3D models for neuroblastoma surgery.

METHODS

We included 7 patients who underwent neuroblastoma surgery between 2022 and 2023. We developed 3D models based on the contrast enhanced T1-weighted MRI scans. Subsequently, we aligned the corresponding ADC and 123I-MIBG-SPECT-CT images using rigid transformation. We estimated registration precision using the Dice score and the target registration error (TRE). 3D heatmaps were computed based on ADC and 123I-MIBG uptake.

RESULTS

The registration algorithm had a median Dice score of 0.81 (0.75-0.90) for ADC and 0.77 (0.65-0.91) for 123I-MIBG-SPECT. For the ADC registration, the median TRE of renal vessels was 4.90 mm (0.86-10.18) and of the aorta 4.67 mm (1.59-12.20). For the 123I -MIBG-SPECT imaging the TRE of the renal vessels was 5.52 mm (1.71-10.97) and 5.28 mm (3.33-16.77) for the aorta.

CONCLUSIONS

We successfully developed a registration workflow to create multi-modal 3D models which allows the surgeon to visualize the tumor and its biological behavior in relation to the surrounding tissue. Future research will include linking of pathological results to imaging data, to validate these multi-modal 3D models.

LEVEL OF EVIDENCE

Level IV.

TYPE OF STUDY

Clinical Research.

Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI)

Non-invasive glioma grade classification is an exciting area in neuroimaging. The primary purpose of this study is to investigate the performance of different medical image fusion algorithms for glioma grading purposes by fusing advanced Magnetic Resonance Imaging (MRI) images. Ninety-six subjects underwent an Apparent diffusion coefficient (ADC) map and Susceptibility-weighted imaging (SWI) MRI scan. After preprocessing, the different medical image fusion methods used to fuse ADC maps and SWI were Principal Component Analysis (PCA), Structure-Aware, Discrete Cosine Harmonic Wavelet Transform (DCHWT), Deep-Convolutional Neural network (DNN), Dual-Discriminator conditional generative adversarial network (DDcGAN), and Laplacian Re-Decomposition (LRD). The Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Relative Signal Contrast (RSC) were calculated for qualitative and quantitative analysis. We found high fused image quality with LRD and DDcGAN methods. Further quantitative analysis showed that RSCs in fused images in Low-Grade glioma (LGG) were significantly higher than RSCs in High-Grade glioma (HGG) with PCA, DCHWT, LRD, and DDcGAN. The Receiver Operating Characteristic (ROC) curve test highlighted that LRD and DDcGAN have the highest performance for glioma grade classification. Our work suggests using the DDcGAN and LRD networks for glioma grade classification by fusing ADC maps and SWI images.

Histological and molecular classifications of pediatric glioma with time-dependent diffusion MRI-based microstructural mapping

BACKGROUND

Gliomas are the most common type of central nervous system tumors in children, and the combination of histological and molecular classification is essential for prognosis and treatment. Here, we proposed a newly developed microstructural mapping technique based on diffusion-time-dependent diffusion MRI td-dMRI theory to quantify tumor cell properties and tested these microstructural markers in identifying histological grade and molecular alteration of H3K27.

METHODS

This prospective study included 69 pediatric glioma patients aged 6.14 ± 3.25 years old, who underwent td-dMRI with pulsed and oscillating gradient diffusion sequences on a 3T scanner. dMRI data acquired at varying tds were fitted into a 2-compartment microstructural model to obtain intracellular fraction (fin), cell diameter, cellularity, etc. Apparent diffusivity coefficient (ADC) and T1 and T2 relaxation times were also obtained. H&E stained histology was used to validate the estimated microstructural properties.

RESULTS

For histological classification of low- and high-grade pediatric gliomas, the cellularity index achieved the highest area under the receiver-operating-curve (AUC) of 0.911 among all markers, while ADC, T1, and T2 showed AUCs of 0.906, 0.885, and 0.886. For molecular classification of H3K27-altered glioma in 39 midline glioma patients, cell diameter showed the highest discriminant power with an AUC of 0.918, and the combination of cell diameter and extracellular diffusivity further improved AUC to 0.929. The td-dMRI estimated fin correlated well with the histological ground truth with r = 0.7.

CONCLUSIONS

The td-dMRI-based microstructural properties outperformed routine MRI measurements in diagnosing pediatric gliomas, and the different microstructural features showed complementary strength in histological and molecular classifications.

Surgical Treatment of Pediatric Incidentally Found Brain Tumors: A Single-Center Experience

There remains much debate about the correct management of incidentally found brain tumors in the pediatric population. This study aimed to evaluate the efficacy and safety of surgical treatment of incidentally found pediatric brain tumors. A retrospective analysis of pediatric patients who underwent surgical resection of incidentally found brain tumors between January 2010 and April 2016 was performed. A total of seven patients were included. The median age at the time of diagnosis was 9.7 years. The reasons for performing neuroimaging were as follows: impeded speech development (n = 2), shunt control (n = 1), paranasal sinuses control (n = 1), behavior changes (n = 1), head trauma (n = 1), and preterm birth (n = 1). Five patients underwent gross total tumor resection (71.4%), while subtotal resection was performed in two patients (28.6%). There was no surgery-related morbidity. Patients were followed up for a mean of 79 months. One patient with atypical neurocytoma experienced tumor recurrence 45 months following primary resection. All patients remained neurologically intact. The majority of pediatric incidentally found brain tumors were histologically benign. Surgery remains a safe therapeutic approach associated with favorable long-term outcomes. Considering the expected long lifetime of pediatric patients, as well as the psychological burden associated with having a brain tumor as a child, surgical resection can be considered an initial approach.

Whole-tumor histogram analysis of diffusion and perfusion metrics for noninvasive pediatric glioma grading

PURPOSE

An accurate assessment of the World Health Organization grade is vital for patients with pediatric gliomas to direct treatment planning. We aim to evaluate the diagnostic performance of whole-tumor histogram analysis of diffusion-weighted imaging (DWI) and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) for differentiating pediatric high-grade gliomas from pediatric low-grade gliomas.

METHODS

Sixty-eight pediatric patients (mean age, 10.47 ± 4.37 years; 42 boys) with histologically confirmed gliomas underwent preoperative MR examination. The conventional MRI features and whole-tumor histogram features extracted from apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps were analyzed, respectively. Receiver operating characteristic curves and the binary logistic regression analysis were performed to determine the diagnostic performance of parameters.

RESULTS

For conventional MRI features, location, hemorrhage and tumor margin showed significant difference between pediatric high- and low-grade gliomas (all, P < .05). For advanced MRI parameters, ten histogram features of ADC and CBV showed significant differences between pediatric high- and low-grade gliomas (all, P < .05). The diagnostic performance of the combination of DSC-PWI and DWI (AUC = 0.976, sensitivity = 100%, NPV = 100%) is superior to conventional MRI or DWI model, respectively (AUC_cMRI = 0.700, AUC_DWI = 0.830; both, P < .05).

CONCLUSION

The whole-tumor histogram analysis of DWI and DSC-PWI is a promising method for grading pediatric gliomas.

Lipid Alterations in Glioma: A Systematic Review

Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.

Contribution of advanced neuro-imaging (MR diffusion, perfusion and proton spectroscopy) in differentiation between low grade gliomas GII and MR morphologically similar non neoplastic lesions

Background

Non-neoplastic brain lesions can be misdiagnosed as low-grade gliomas. Conventional magnetic resonance (MR) imaging may be non-specific. Additional imaging modalities such as spectroscopy (MRS), perfusion and diffusion imaging aid in diagnosis of such lesions. However, contradictory and overlapping results are still present. Hence, our purpose was to evaluate the role of advanced neuro-imaging in differentiation between low-grade gliomas (WHO grade II) and MR morphologically similar non-neoplastic lesions and to prove which modality has the most accurate results in differentiation.

Results

All patients were classified into two main groups: patients with low-grade glioma (n = 12; mean age, 38.8 ± 16; 8 males) and patients with non-neoplastic lesions (n = 27; mean age, 36.6 ± 15; 19 males) based on the histopathological and clinical–radiological diagnosis. Using ROC curve analysis, a threshold value of 0.93 for rCBV (AUC = 0.875, PPV = 92%, NPV = 71.4%) and a threshold value of 2.5 for Cho/NAA (AUC = 0.829, PPV = 92%, NPV = 71.4%) had 85.2% sensitivity and 83.3% specificity for predicting neoplastic lesions. The area under the curve (AUC) of ROC analysis was good for relative cerebral blood volume (rCBV) and Cho/NAA ratios (> 0.80) and fair for Cho/Cr and NAA/Cr ratios (0.70–0.80). When the rCBV measurements were combined with MRS ratios, significant improvement was observed in the area under the curve (AUC) (0.969) with improved diagnostic accuracy (89.7%) and sensitivity (88.9%).

Conclusions

Evaluation of rCBV and metabolite ratios at MRS, particularly Cho/NAA ratio, may be helpful in differentiating low-grade gliomas from non-neoplastic lesions. The combination of dynamic susceptibility contrast (DSC) perfusion and MRS can significantly improve the diagnostic accuracy and can help avoiding the need for an invasive biopsy.

Neuroimaging in cerebellar ataxia in childhood: A review

Ataxia is one of the most common pediatric movement disorders and can be caused by a large number of congenital and acquired diseases affecting the cerebellum or the vestibular or sensory system. It is mainly characterized by gait abnormalities, dysmetria, intention tremor, dysdiadochokinesia, dysarthria, and nystagmus. In young children, ataxia may manifest as the inability or refusal to walk. The diagnostic approach begins with a careful clinical history including the temporal evolution of ataxia and the inquiry of additional symptoms, is followed by a meticulous physical examination, and, depending on the results, is complemented by laboratory assays, electroencephalography, nerve conduction velocity, lumbar puncture, toxicology screening, genetic testing, and neuroimaging. Neuroimaging plays a pivotal role in either providing the final diagnosis, narrowing the differential diagnosis, or planning targeted further workup. In this review, we will focus on the most common form of ataxia in childhood, cerebellar ataxia (CA). We will discuss and summarize the neuroimaging findings of either the most common or the most important causes of CA in childhood or present causes of pediatric CA with pathognomonic findings on MRI. The various pediatric CAs will be categorized and presented according to (a) the cause of ataxia (acquired/disruptive vs. inherited/genetic) and (b) the temporal evolution of symptoms (acute/subacute, chronic, progressive, nonprogressive, and recurrent). In addition, several illustrative cases with their key imaging findings will be presented.

Standard clinical approaches and emerging modalities for glioblastoma imaging

Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. In addition, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.

Hydrogen Proton Magnetic Resonance Spectroscopy (MRS) in Differential Diagnosis of Intracranial Tumors: A Systematic Review

Meningioma, glioma, and metastases are the most common intracranial tumors in clinical practice. In order to improve the prognosis of patients, timely diagnosis and early treatment are crucial. Hydrogen proton magnetic resonance spectroscopy (1H-MRS) imaging can noninvasively display the biochemical information of tissues in vivo and has been applied to identify and diagnose intracranial tumors. We want to comprehensively evaluate 1H-MRS identify and diagnose intracranial tumors by meta-analysis. Some databases such as PubMed and Cochrane Library were used to systematically search articles that were about identifying and diagnosing intracranial tumors with 1H-MRS. Then, weighted mean difference (WMD) was used as an effect size to conduct meta-analysis. There are altogether nine articles, including 533 patients. Results of meta-analysis: The Cho/Cr and Cho/NAA ratios in the LGG group were significantly lower than those in the HGG group (WMD = -0.69, 95% CI (-0.92, -0.45), P < 0.001, WMD = -0.76, 95% CI (-1.03, -0.48), P < 0.001). The Cho/Cr ratio of tumor and peritumor in the HGG group was significantly different from that in the metastasis group (0.68, 95% CI (-1.27, 2.62), P < 0.001, WMD = 0.94, 95% CI (0.41, 1.47), P < 0.001). There was no significant difference in the tumor and peritumor NAA/Cr ratio between the HGG group and metastasis group (WMD = -0.64, 95% CI (-1.63, 0.34), P=0.31, WMD = -0.22, 95% CI (-0.59, 0.15), P=0.24). 1H-MRS can provide metabolic information of different intracranial tumors and can effectively diagnose and differentiate glioma and metastasis. 1H-MRS can also provide a reliable basis for the classification of glioma, and has certain clinical application value.

Proteomics and metabolomics approach in adult and pediatric glioma diagnostics

The diagnosis of glioma is mainly based on imaging methods that do not distinguish between stage and subtype prior to histopathological analysis. Patients with gliomas are generally diagnosed in the symptomatic stage of the disease. Additionally, healing scar tissue may be mistakenly identified based on magnetic resonance imaging (MRI) as a false positive tumor recurrence in postoperative patients. Current knowledge of molecular alterations underlying gliomagenesis and identification of tumoral biomarkers allow for their use as discriminators of the state of the organism. Moreover, a multiomics approach provides the greatest spectrum and the ability to track physiological changes and can serve as a minimally invasive method for diagnosing asymptomatic gliomas, preceding surgery and allowing for the initiation of prophylactic treatment. It is important to create a vast biomarker library for adults and pediatric patients due to their metabolic differences. This review focuses on the most promising proteomic, metabolomic and lipidomic glioma biomarkers, their pathways, the interactions, and correlations that can be considered characteristic of tumor grade or specific subtype.

Diffusion and perfusion imaging biomarkers of H3 K27M mutation status in diffuse midline gliomas

PURPOSE

H3K27M-mutant diffuse midline gliomas (M-DMGs) exhibit a clinically aggressive course. We studied diffusion-weighted imaging (DWI) and perfusion (PWI) MRI features of DMG with the hypothesis that DWI-PWI metrics can serve as biomarkers for the prediction of the H3K27M mutation status in DMGs.

METHODS

A retrospective review of the institutional database (imaging and histopathology) of patients with DMG (July 2016 to July 2020) was performed. Tumoral apparent diffusion coefficient (ADC) and peritumoral ADC (PT ADC) values and their normalized values (nADC and nPT ADC) were computed. Perfusion data were analyzed with manual arterial input function (AIF) and leakage correction (LC) Boxerman-Weiskoff models. Normalized maximum relative CBV (rCBV) was evaluated. Intergroup analysis of the imaging variables was done between M-DMGs and wild-type (WT-DMGs) groups.

RESULTS

Ninety-four cases (M-DMGs-n = 48 (51%) and WT-DMGs-n = 46(49%)) were included. Significantly lower PT ADC (mutant-1.1 ± 0.33, WT-1.23 ± 0.34; P = 0.033) and nPT ADC (mutant-1.64 ± 0.48, WT-1.83 ± 0.54; P = 0.040) were noted in the M-DMGs. The rCBV (mutant-25.17 ± 27.76, WT-13.73 ± 14.83; P = 0.018) and nrCBV (mutant-3.44 ± 2.16, WT-2.39 ± 1.25; P = 0.049) were significantly higher in the M-DMGs group. Among thalamic DMGs, the min ADC, PT ADC, and nADC and nPT ADC were lower in M-DMGs while nrCBV (corrected and uncorrected) was significantly higher. Receiver operator characteristic curve analysis demonstrated that PT ADC (cut-off-1.245), nPT ADC (cut-off-1.853), and nrCBV (cut-off-1.83) were significant independent predictors of H3K27M mutational status in DMGs.

CONCLUSION

DWI and PWI features hold value in preoperative prediction of H3K27M-mutation status in DMGs.