MR Imaging of Pediatric Brain Tumors

Imaging features of pediatric meningiomas: emphasis on unusual locations

PURPOSE

This article aims to analyze pediatric meningioma's imaging characteristics, especially those in unusual locations.

METHODS

Pediatric patients with pathologically confirmed meningiomas at our hospital from January 2010 to January 2024 were enrolled. Meningiomas located in the cerebral convexity, parasagittal falcine region, anterior skull base, middle skull base, sphenoid ridge, cerebellopontine angle (CPA), olfactory groove, or juxtasella were considered in usual locations. Meningiomas found in other areas were considered in unusual locations. Clinical information, pathology results, and imaging features of pediatric meningiomas in usual and usual locations were analyzed and compared.

RESULTS

A total of 18 patients (19 meningiomas) were enrolled, including 14 males and 4 females, with an average age of 14 years (ranging from 6 to 18 years). A total of 12 (63.2%) meningiomas were in the unusual location, including four (33.3%) were intraparenchymal, four (33.3%) were intraventricular, two (16.7%) were intraosseous, one (8.3%) case was in the paranasal sinus, and one (8.3%) was intraspinal. The meningiomas in unusual locations usually lacked the meningeal tail sign, and the misdiagnosis rate on preoperative imaging was significantly higher than that of meningiomas in usual locations.

CONCLUSION

Pediatric meningiomas are prone to occur in unusual locations. When they occur in usual locations, they often lack typical radiographic features of meningiomas, leading to potential misdiagnosis before surgery. Recognition of the imaging characteristics of meningiomas in unusual locations in children may facilitate accurate preoperative imaging diagnosis.

Approaches to supratentorial brain tumours in children

The differential diagnosis of supratentorial brain tumours in children can be challenging, especially considering the recent changes to the WHO classification of CNS tumours published in 2021. Many new tumour types have been proposed which frequently present in children and young adults and their imaging features are currently being described by the neuroradiology community. The purpose of this article is to provide guidance to residents and fellows new to the field of paediatric neuroradiology on how to evaluate an MRI of a patient with a newly diagnosed supratentorial tumour. Six different approaches are discussed including: 1. Tumour types, briefly discussing the main changes to the recent WHO classification of CNS tumours, 2. Patient age and its influence on incidence rates of specific tumour types, 3. Growth patterns, 4. Tumour location and how defining the correct location helps in narrowing down the differential diagnoses and 5. Imaging features of the tumour on DWI, SWI, FLAIR and post contrast sequences.

Advanced MRI imaging techniques in pediatric brain tumors

There is a diverse array of pediatric brain tumors with considerable associated morbidity. Like adult brain tumors, MRI serves as the primary imaging modality for pediatric brain tumors. In addition to standard sequences, more advanced MRI techniques can enhance the precision of diagnosis and assist in prognostication, and treatment planning. This paper discusses these various advanced techniques categorizing them into those that assist in identifying tissue characteristics, and those that evaluate the functional impact of tumors to aid in treatment planning.

Radiomics and artificial intelligence applications in pediatric brain tumors

BACKGROUND

The study of central nervous system (CNS) tumors is particularly relevant in the pediatric population because of their relatively high frequency in this demographic and the significant impact on disease- and treatment-related morbidity and mortality. While both morphological and non-morphological magnetic resonance imaging techniques can give important information concerning tumor characterization, grading, and patient prognosis, increasing evidence in recent years has highlighted the need for personalized treatment and the development of quantitative imaging parameters that can predict the nature of the lesion and its possible evolution. For this purpose, radiomics and the use of artificial intelligence software, aimed at obtaining valuable data from images beyond mere visual observation, are gaining increasing importance. This brief review illustrates the current state of the art of this new imaging approach and its contributions to understanding CNS tumors in children.

DATA SOURCES

We searched the PubMed, Scopus, and Web of Science databases using the following key search terms: ("radiomics" AND/OR "artificial intelligence") AND ("pediatric AND brain tumors"). Basic and clinical research literature related to the above key research terms, i.e., studies assessing the key factors, challenges, or problems of using radiomics and artificial intelligence in pediatric brain tumors management, was collected.

RESULTS

A total of 63 articles were included. The included ones were published between 2008 and 2024. Central nervous tumors are crucial in pediatrics due to their high frequency and impact on disease and treatment. MRI serves as the cornerstone of neuroimaging, providing cellular, vascular, and functional information in addition to morphological features for brain malignancies. Radiomics can provide a quantitative approach to medical imaging analysis, aimed at increasing the information obtainable from the pixels/voxel grey-level values and their interrelationships. The "radiomic workflow" involves a series of iterative steps for reproducible and consistent extraction of imaging data. These steps include image acquisition for tumor segmentation, feature extraction, and feature selection. Finally, the selected features, via training predictive model (CNN), are used to test the final model.

CONCLUSIONS

In the field of personalized medicine, the application of radiomics and artificial intelligence (AI) algorithms brings up new and significant possibilities. Neuroimaging yields enormous amounts of data that are significantly more than what can be gained from visual studies that radiologists can undertake on their own. Thus, new partnerships with other specialized experts, such as big data analysts and AI specialists, are desperately needed. We believe that radiomics and AI algorithms have the potential to move beyond their restricted use in research to clinical applications in the diagnosis, treatment, and follow-up of pediatric patients with brain tumors, despite the limitations set out.

Predictors of postoperative complications and functional outcomes in pediatric patients with surgically treated fourth ventricle tumors

BACKGROUND

Tumors of the fourth ventricle are frequently treated pathologies in pediatric neurosurgery. Data regarding predictors for permanent neurological deficits, long-term functional outcomes, cerebellar mutism (CM), the extent of resection (EOR), and oncological outcomes are scarce. We attempt to contribute to this topic with an analysis of our institutional cohort.

METHODS

A retrospective single-center study of patients aged ≤ 19 years who underwent primary surgical resection of a fourth ventricular tumor over a 15-year period (2006-2021). Predictors analyzed included age, gender, surgical approach, anatomical pattern, tumor grade, EOR, tumor volume, and others as appropriate.

RESULTS

One hundred six patients were included (64 males, mean age 7.3 years). The rate of permanent neurological deficit was 24.2%; lateral tumor extension (p = 0.036) and tumor volume greater than 38 cm3 (p = 0.020) were significant predictors. The presence of a deficit was the only significant predictor of reduced (less than 90) Lansky score (p = 0.005). CM occurred in 20.8% of patients and was influenced by medulloblastoma histology (p = 0.011), lateral tumor extension (p = 0.017), and male gender (p = 0.021). No significant difference between the transvermian and telovelar approach in the development of CM was detected (p = 0.478). No significant predictor was found for the EOR. EOR was not found to be a significant predictor of overall survival for both low-grade and high-grade tumors; however, gross total resection (GTR) was protective against tumor recurrence compared to near-total or subtotal resection (p < 0.001). In addition, survival was found to be better in older patients (≥ 7.0 years, p = 0.019).

CONCLUSION

The overall rate of postoperative complications remains high due to the eloquent localization. Older patients (> 7 years) have been found to have better outcomes and prognosis. Achieving GTR whenever feasible and safe has been shown to be critical for tumor recurrence. CM was more common in patients with medulloblastoma and in patients with tumors extending through the foramen of Luschka. The telovelar approach uses a safe and anatomically sparing corridor; however, it has not been associated with a lower incidence of CM and neurological sequelae in our series, showing that each case should be assessed on an individual basis.

A preliminary study on corticospinal tract morphology in incidental and symptomatic insular low-grade glioma: implications for post-surgical motor outcomes

OBJECTIVE

Our study aimed to investigate the shape and diffusion properties of the corticospinal tract (CST) in patients with insular incidental and symptomatic low-grade gliomas (LGGs), especially those in the incidental group, and evaluate their association with post-surgical motor function.

METHODS

We performed automatic fiber tracking on 41 LGG patients, comparing macroscopic shape and microscopic diffusion properties of CST between ipsilateral and contralateral tracts in both incidental and symptomatic groups. A correlation analysis was conducted between properties of CST and post-operative motor strength grades.

RESULTS

In the incidental group, no significant differences in mean diffusion properties were found between bilateral CST. While decreased anisotropy of the CST around the superior limiting sulcus and increased axial diffusivity of the CST near the midbrain level were noted, there was no significant correlation between pre-operative diffusion metrics and post-operative motor strength. In comparison, we found significant correlations between the elongation of the affected CST in the preoperative scans and post-operative motor strength in short-term and long-term follow ups (p = 1.810 × 10-4 and p = 9.560 × 10-4, respectively).

CONCLUSIONS

We found a significant correlation between CST shape measures and post-operative motor function outcomes in patients with incidental insular LGGs. CST morphology shows promise as a potential prognostic factor for identifying functional deficits in this patient population.

Predicting the WHO Grading of Pediatric Brain Tumors Based on Their MRI Appearance: A Retrospective Study

The treatment of central nervous system (CNS) tumors constitutes a significant part of a pediatric neurosurgeon's workload. The classification of such neoplasms spans many entities. These include low- and high-grade lesions, with both occurring in the population of patients under 18 years of age. Magnetic resonance imaging serves as the imaging method of choice for neoplastic lesions of the brain. Through its different modalities, such as T1, T2, T1 C+, apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), fluid-attenuated inversion recovery (FLAIR), etc., it allows the medical team to plan the therapeutic process accordingly while also possibly suggesting the specific tumor subtype prior to obtaining a definitive histological diagnosis. We conducted a retrospective study spanning 32 children treated surgically for brain tumors between July 2021 and January 2023 who had a precise histological diagnosis determined by using the 2021 WHO Classification of Tumors of the Central Nervous System. We divided them into two groups (high-grade and low-grade tumors, i.e., WHO grades 1 and 2, and grades 3 and 4, respectively) and analyzed their demographic data and preoperative MRI results. This was done using the following criteria: sub or supratentorial location of the tumor; lesion is circumscribed or infiltrating; solid, cystic, or mixed solid and cystic character of the tumor; number of compartments in cystic lesions; signal intensity (hypo-, iso-, hyperintensity sequences: T1, T2, T1 C+); presence of restricted diffusion; the largest diameter of the solid component and/or the largest diameter of the largest cyst in the transverse section. Then, we examined the results to find any correlation between the lesions' morphologies and their final assigned degree of malignancy. We found that the only radiological criteria correlating with the final WHO grade of the tumor were an infiltrative pattern of growth (25% of low-grade lesions, 75% of high-grade; p = 0.006) and the presence of a cystic component in the tumor (in 68.75% of low-grade tumors and 43.75% of high-grade tumors; p = 0.041). The only other feature close to attaining statistical significance was diffusion restriction (33.3% of low-grade tumors, 66.7% high-grade; p = 0.055). Older children tended to present with tumors of lower degrees of malignancy, and there was a predominance of female patients (21 female, 11 male).

Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application?

(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software.

Uncertainty-Aware Deep Learning Classification of Adamantinomatous Craniopharyngioma from Preoperative MRI

Diagnosis of adamantinomatous craniopharyngioma (ACP) is predominantly determined through invasive pathological examination of a neurosurgical biopsy specimen. Clinical experts can distinguish ACP from Magnetic Resonance Imaging (MRI) with an accuracy of 86%, and 9% of ACP cases are diagnosed this way. Classification using deep learning (DL) provides a solution to support a non-invasive diagnosis of ACP through neuroimaging, but it is still limited in implementation, a major reason being the lack of predictive uncertainty representation. We trained and tested a DL classifier on preoperative MRI from 86 suprasellar tumor patients across multiple institutions. We then applied a Bayesian DL approach to calibrate our previously published ACP classifier, extending beyond point-estimate predictions to predictive distributions. Our original classifier outperforms random forest and XGBoost models in classifying ACP. The calibrated classifier underperformed our previously published results, indicating that the original model was overfit. Mean values of the predictive distributions were not informative regarding model uncertainty. However, the variance of predictive distributions was indicative of predictive uncertainty. We developed an algorithm to incorporate predicted values and the associated uncertainty to create a classification abstention mechanism. Our model accuracy improved from 80.8% to 95.5%, with a 34.2% abstention rate. We demonstrated that calibration of DL models can be used to estimate predictive uncertainty, which may enable clinical translation of artificial intelligence to support non-invasive diagnosis of brain tumors in the future.

Intracranial Tumors in the First Year of Life

Intracranial tumors in the first year of life are rare and, in this age group, are the second most common type of pediatric cancer after leukemias. As the more common solid tumor in neonates and infants, they present some peculiarities such as the high incidence of malignancies. Routine ultrasonography made easier to detect intrauterine tumors, but diagnosis can be delayed due to the lack or scarcity of recognizable symptoms. These neoplasms are often very large and highly vascular. Their removal is challenging, and there is a higher rate of morbidity and mortality than seen in older children, adolescents, and adults. They also differ from older children with respect to location, histological features, clinical behavior, and management. Pediatric low-grade gliomas represent 30% of the tumors in this age group and comprise circumscribed and diffuse tumors. They are followed by medulloblastoma and ependymoma. Other non-medulloblastoma embryonal neoplasms, former known as PNETS, are also commonly diagnosed in neonates and infants. Teratomas have an expressive incidence in newborns but decline gradually until the end of the first year of life. Immunohistochemical, molecular, and genomic advances are impacting the understanding and targeting of the treatment of some tumors, but, despite all these advances, the extent of resection remains the most important factor in the prognosis and survival of almost all types of tumors. The outcome is difficult to estimate, and 5-year survival ranges from one-quarter to three-quarters of the patients.

Advances in the Treatment of Pediatric Brain Tumors

Pediatric brain tumors are the most common solid malignancies in children. Advances in the treatment of pediatric brain tumors have come in the form of imaging, biopsy, surgical techniques, and molecular profiling. This has led the way for targeted therapies and immunotherapy to be assessed in clinical trials for the most common types of pediatric brain tumors. Here we review the latest efforts and challenges in targeted molecular therapy, immunotherapy, and newer modalities such as laser interstitial thermal therapy.