Deep Learning-Based Studies on Pediatric Brain Tumors Imaging: Narrative Review of Techniques and Challenges

Delay in the Diagnosis of Pediatric Brain Tumors in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis

BACKGROUND AND OBJECTIVES

Vague symptoms and a lack of pathognomonic features hinder the timely diagnosis of pediatric brain tumors (PBTs). However, patients in low- and middle-income countries (LMICs) must also bear the brunt of a multitude of additional factors contributing to diagnostic delays and subsequently affecting survival. Therefore, this study aims to assess these factors and quantify the durations associated with diagnostic delays for PBTs in LMICs.

METHODS

A systematic review of extant literature regarding children from LMICs diagnosed with brain tumors was conducted. Articles published before June 2023 were identified using PubMed, Google Scholar, Scopus, Embase, Cumulative Index to Nursing and Allied Health Literature, and Web of Science. A meta-analysis was conducted using a random-effects model through R Statistical Software. Quality was assessed using the Newcastle Ottawa Scale.

RESULTS

A total of 40 studies including 2483 patients with PBT from 21 LMICs were identified. Overall, nonspecific symptoms (62.5%) and socioeconomic status (45.0%) were the most frequently reported factors contributing to diagnostic delays. Potential sources of patient-associated delay included lack of parental awareness (45.0%) and financial constraints (42.5%). Factors contributing to health care system delays included misdiagnoses (42.5%) and improper referrals (32.5%). A pooled mean prediagnostic symptomatic interval was calculated to be 230.77 days (127.58-333.96), the patient-associated delay was 146.02 days (16.47-275.57), and the health care system delay was 225.05 days (-64.79 to 514.89).

CONCLUSION

A multitude of factors contribute to diagnostic delays in LMICs. The disproportionate effect of these factors is demonstrated by the long interval between symptom onset and the definitive diagnosis of PBTs in LMICs, when compared with high-income countries. While evidence-based policy recommendations may improve the pace of diagnosis, policy makers will need to be cognizant of the unique challenges patients and health care systems face in LMICs.

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.

Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors

Recent advances in artificial intelligence have greatly impacted the field of medical imaging and vastly improved the development of computational algorithms for data analysis. In the field of pediatric neuro-oncology, radiomics, the process of obtaining high-dimensional data from radiographic images, has been recently utilized in applications including survival prognostication, molecular classification, and tumor type classification. Similarly, radiogenomics, or the integration of radiomic and genomic data, has allowed for building comprehensive computational models to better understand disease etiology. While there exist excellent review articles on radiomics and radiogenomic pipelines and their applications in adult solid tumors, in this review article, we specifically review these computational approaches in the context of pediatric medulloblastoma tumors. Based on our systematic literature research via PubMed and Google Scholar, we provide a detailed summary of a total of 15 articles that have utilized radiomic and radiogenomic analysis for survival prognostication, tumor segmentation, and molecular subgroup classification in the context of pediatric medulloblastoma. Lastly, we shed light on the current challenges with the existing approaches as well as future directions and opportunities with using these computational radiomic and radiogenomic approaches for pediatric medulloblastoma tumors.

Imaging of pediatric brain tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee/ASPNR White Paper

Tumors of the central nervous system are the most common solid malignancies in children and the most common cause of pediatric cancer-related mortality. Imaging plays a central role in diagnosis, staging, treatment planning, and response assessment of pediatric brain tumors. However, the substantial variability in brain tumor imaging protocols across institutions leads to variability in patient risk stratification and treatment decisions, and complicates comparisons of clinical trial results. This White Paper provides consensus-based imaging recommendations for evaluating pediatric patients with primary brain tumors. The proposed brain magnetic resonance imaging protocol recommendations balance advancements in imaging techniques with the practicality of deployment across most imaging centers.

Radio-pathomic approaches in pediatric neuro-oncology: Opportunities and challenges

With medical software platforms moving to cloud environments with scalable storage and computing, the translation of predictive artificial intelligence (AI) models to aid in clinical decision-making and facilitate personalized medicine for cancer patients is becoming a reality. Medical imaging, namely radiologic and histologic images, has immense analytical potential in neuro-oncology, and models utilizing integrated radiomic and pathomic data may yield a synergistic effect and provide a new modality for precision medicine. At the same time, the ability to harness multi-modal data is met with challenges in aggregating data across medical departments and institutions, as well as significant complexity in modeling the phenotypic and genotypic heterogeneity of pediatric brain tumors. In this paper, we review recent pathomic and integrated pathomic, radiomic, and genomic studies with clinical applications. We discuss current challenges limiting translational research on pediatric brain tumors and outline technical and analytical solutions. Overall, we propose that to empower the potential residing in radio-pathomics, systemic changes in cross-discipline data management and end-to-end software platforms to handle multi-modal data sets are needed, in addition to embracing modern AI-powered approaches. These changes can improve the performance of predictive models, and ultimately the ability to advance brain cancer treatments and patient outcomes through the development of such models.

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.

Magnetic resonance angiographic study of variations in course of paraclival and parasellar internal carotid artery in relation to expanded endonasal endoscopic approaches

AIMS

To study the variations in the course of the paraclival and parasellar carotid arteries in normal subjects using magnetic resonance angiography as is relevant from an endoscopic endonasal perspective.

METHODS

Two hundred MR angiographies of normal subjects were analyzed in a prospective study. The intercarotid distances were measured at fixed points along the paraclival and parasellar segments of the internal carotid artery. The intercarotid spaces thus obtained were categorized into trapezoid, square and hourglass shapes. The angle between the posterior ascending vertical and horizontal bend of the parasellar ICA was also measured and analyzed.

RESULTS

The trapezoid shape of intercarotid space is the most common (52.5%), followed by the square (35%) and the hourglass (12.5%) shaped spaces. Angle of < 80° between the posterior ascending vertical and horizontal bend of the parasellar ICA was found in 39% of subjects, angle between 80° and 100° was found in 9% subjects, angle > 100° was found in 43% while asymmetric angles on the two sides was found in 9% of subjects.

CONCLUSION

A thorough understanding of the course of the ICA is important in planning the approach and preventing injury to the ICA.