MR Imaging-Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma

Advances of artificial intelligence in clinical application and scientific research of neuro-oncology: Current knowledge and future perspectives

Brain tumors refer to the abnormal growths that occur within the brain's tissue, comprising both primary neoplasms and metastatic lesions. Timely detection, precise staging, suitable treatment, and standardized management are of significant clinical importance for extending the survival rates of brain tumor patients. Artificial intelligence (AI), a discipline within computer science, is leveraging its robust capacity for information identification and combination to revolutionize traditional paradigms of oncology care, offering substantial potential for precision medicine. This article provides an overview of the current applications of AI in brain tumors, encompassing the primary AI technologies, their working mechanisms and working workflow, the contributions of AI to brain tumor diagnosis and treatment, as well as the role of AI in brain tumor scientific research, particularly in drug innovation and revealing tumor microenvironment. Finally, the paper addresses the existing challenges, potential solutions, and the future application prospects. This review aims to enhance our understanding of the application of AI in brain tumors and provide valuable insights for forthcoming clinical applications and scientific inquiries.

Advancing medulloblastoma therapy: strategies and survival insights

Medulloblastoma, the most common malignant brain tumor in children, presents unique challenges due to its molecular and histological heterogeneity. Advances in molecular profiling have refined risk stratification, enabling personalized treatment strategies and improved survival outcomes. This review synthesizes recent developments in the multimodal management of medulloblastoma, encompassing surgery, craniospinal radiation therapy, and chemotherapy, tailored to patient age and risk classification. Key highlights include subgroup-specific therapies, the role of molecular-targeted treatments, and the integration of genetic testing for germline mutations to guide clinical decision-making. Special emphasis is placed on minimizing treatment-related toxicity while preserving long-term quality of life. Additionally, this manuscript discusses the implications of novel therapeutic approaches for high-risk subgroups, including intensified regimens and systemic therapies for young children. Despite significant progress, challenges remain in addressing long-term complications such as neurocognitive impairments, endocrine dysfunction, and secondary malignancies. Future directions prioritize optimizing therapeutic efficacy while reducing morbidity, underscoring the importance of translating molecular discoveries into clinical practice.

Radiological Predictors of Cognitive Impairment in Paediatric Brain Tumours Using Multiparametric Magnetic Resonance Imaging: A Review of Current Practice, Challenges and Future Directions

Paediatric brain tumours and their treatments are associated with long-term cognitive impairment. While the aetiology of cognitive impairment is complex and multifactorial, multiparametric Magnetic Resonance Imaging (MRI) can identify many risk factors including tumour location, damage to eloquent structures and tumour phenotype. Hydrocephalus and raised intracranial pressure can be observed, along with risk factors for post-operative paediatric cerebellar mutism syndrome or epilepsy. MRI can also identify complications of surgery or radiotherapy and monitor treatment response. Advanced imaging sequences provide valuable information about tumour and brain physiology, but clinical use is limited by extended scanning times and difficulties in processing and analysis. Brain eloquence classifications exist, but focus on adults with neurological deficits and are outdated. For the analysis of childhood tumours, limited numbers within tumour subgroups and the investigation of long-term outcomes necessitate using historical scans and/or multi-site collaboration. Variable imaging quality and differing acquisition parameters limit the use of segmentation algorithms and radiomic analysis. Harmonisation can standardise imaging in collaborative research, but can be challenging, while data-sharing produces further logistical challenges. Consequently, most research consists of small single-centre studies limited to regional analyses of tumour location. Technological advances reducing scanning times increase the feasibility of clinical acquisition of high-resolution standardised imaging including advanced physiological sequences. The RAPNO and SIOPE paediatric brain tumour imaging guidelines have improved image standardisation, which will benefit future collaborative imaging research. Modern machine learning techniques provide more nuanced approaches for integration and analysis of the complex and multifactorial data involved in cognitive outcome prediction.

Artificial Intelligence for Neuroimaging in Pediatric Cancer

BACKGROUND/OBJECTIVES

Artificial intelligence (AI) is transforming neuroimaging by enhancing diagnostic precision and treatment planning. However, its applications in pediatric cancer neuroimaging remain limited. This review assesses the current state, potential applications, and challenges of AI in pediatric neuroimaging for cancer, emphasizing the unique needs of the pediatric population.

METHODS

A comprehensive literature review was conducted, focusing on AI's impact on pediatric neuroimaging through accelerated image acquisition, reduced radiation, and improved tumor detection. Key methods include convolutional neural networks for tumor segmentation, radiomics for tumor characterization, and several tools for functional imaging. Challenges such as limited pediatric datasets, developmental variability, ethical concerns, and the need for explainable models were analyzed.

RESULTS

AI has shown significant potential to improve imaging quality, reduce scan times, and enhance diagnostic accuracy in pediatric neuroimaging, resulting in improved accuracy in tumor segmentation and outcome prediction for treatment. However, progress is hindered by the scarcity of pediatric datasets, issues with data sharing, and the ethical implications of applying AI in vulnerable populations.

CONCLUSIONS

To overcome current limitations, future research should focus on building robust pediatric datasets, fostering multi-institutional collaborations for data sharing, and developing interpretable AI models that align with clinical practice and ethical standards. These efforts are essential in harnessing the full potential of AI in pediatric neuroimaging and improving outcomes for children with cancer.

Multiparametric MRI-based machine learning system of molecular subgroups and prognosis in medulloblastoma

OBJECTIVES

We aimed to use artificial intelligence to accurately identify molecular subgroups of medulloblastoma (MB), predict clinical outcomes, and incorporate deep learning-based imaging features into the risk stratification.

METHODS

The MRI features were extracted for molecular subgroups by a novel multi-parameter convolutional neural network (CNN) called Bi-ResNet-MB. Then, MR features were used to establish a prognosis model based on XGBoost. Finally, a novel risk stratification system to stratify the patients based on the M2R Score (Machine learning-based Medulloblastoma Risk Score) was proposed.

RESULTS

A total of 139 MB patients (36 female, average age 7.27 ± 3.62 years) were treated at Beijing Tiantan Hospital. The Bi-ResNet-MB model excelled in molecular subgroup classification, achieving an average AUC of 0.946 (95% CI: 0.899-0.993). For prognostic prediction, our models achieved AUCs of 0.840 (95% CI: 0.792-0.888), 0.949 (95% CI: 0.899-0.999), and 0.960 (95% CI: 0.915-1.000) for OS, and 0.946 (95% CI: 0.905-0.987), 0.932 (95% CI: 0.875-0.989), and 0.964 (95% CI: 0.921-1.000) for PFS at 1, 3, and 5 years. In an independent validation dataset of 108 patients (33 female, average age 7.11 ± 2.92 years), the average AUC of molecular subgroup classification reached 0.894 (95% CI: 0.797-1.000). For PFS prediction at 1, 3, and 5 years, the AUCs were 0.832 (95% CI: 0.724-0.920), 0.875 (95% CI: 0.781-0.967), and 0.907 (95% CI: 0.760-1.000), respectively.

CONCLUSIONS

Based on machine learning and MRI data, models for MB molecular subgroups and prognosis prediction and the novel risk stratification system may significantly benefit patients.

KEY POINTS

Question Medulloblastoma exhibits significant heterogeneity, leading to considerable variations in patient prognosis and there is a lack of effective risk assessment strategies. Findings We have constructed a comprehensive machine learning system that excels in subgrouping diagnosis, prognosis assessment, and risk stratification for medulloblastoma patients preoperatively. Clinical relevance The utilization of non-invasive preoperative diagnosis and assessment is advantageous for clinicians in creating personalized treatment plans, particularly for high-risk patients. Additionally, it lays a foundation for the subsequent implementation of neoadjuvant therapy for medulloblastoma.

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.

The Role of Machine Learning Approaches in Pediatric Oncology: A Systematic Review

To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biological risk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search for relevant studies on four distinct databases (Scopus, Web of Science, PubMed, and Cochrane Library). A total of 1536 relevant studies were retrieved to the EndNote library (Clarivate, Philadelphia, USA) where duplicates were removed and the rest of the studies were assessed for eligibility based on titles, abstracts, and the availability of full-text articles. After assessing the studies for eligibility, we found 42 studies eligible for inclusion in this systematic review. We found nine studies on liquid tumors, 13 on solid tumors, and 20 on central nervous system (CNS) tumors. ML goals included classification, treatment response prediction, and dose optimization. Neural networks, k-nearest neighbors, random forests, support vector machines, and naive Bayes were among the techniques employed. The identified studies' strengths included treatment response prediction and automated analysis that matched or outperformed physician comparators. Significant variation in clinical applicability, criteria for reporting, limited sample numbers, and the absence of external validation cohorts were among the common issues. We found places where ML can improve clinical care in manners that would not be possible otherwise. Even though ML has great promise for enhancing pediatric cancer diagnosis, decision-making, and monitoring, the discipline is still in its infancy, and standards and recommendations will support future research to guarantee robust methodologic design and maximize therapeutic applicability.

Pediatric brain tumor classification using deep learning on MR images with age fusion

Purpose

To implement and evaluate deep learning-based methods for the classification of pediatric brain tumors (PBT) in magnetic resonance (MR) data.

Methods

A subset of the "Children's Brain Tumor Network" dataset was retrospectively used (n = 178 subjects, female = 72, male = 102, NA = 4, age range [0.01, 36.49] years) with tumor types being low-grade astrocytoma (n = 84), ependymoma (n = 32), and medulloblastoma (n = 62). T1w post-contrast (n = 94 subjects), T2w (n = 160 subjects), and apparent diffusion coefficient (ADC: n = 66 subjects) MR sequences were used separately. Two deep learning models were trained on transversal slices showing tumor. Joint fusion was implemented to combine image and age data, and 2 pre-training paradigms were utilized. Model explainability was investigated using gradient-weighted class-activation mapping (Grad-CAM), and the learned feature space was visualized using principal component analysis (PCA).

Results

The highest tumor-type classification performance was achieved when using a vision transformer model pre-trained on ImageNet and fine-tuned on ADC images with age fusion (Matthews correlation coefficient [MCC]: 0.77 ± 0.14, Accuracy: 0.87 ± 0.08), followed by models trained on T2w (MCC: 0.58 ± 0.11, Accuracy: 0.73 ± 0.08) and T1w post-contrast (MCC: 0.41 ± 0.11, Accuracy: 0.62 ± 0.08) data. Age fusion marginally improved the model's performance. Both model architectures performed similarly across the experiments, with no differences between the pre-training strategies. Grad-CAMs showed that the models' attention focused on the brain region. PCA of the feature space showed greater separation of the tumor-type clusters when using contrastive pre-training.

Conclusion

Classification of PBT on MR images could be accomplished using deep learning, with the top-performing model being trained on ADC data, which radiologists use for the clinical classification of these tumors.

Application of radiomics for diagnosis, subtyping, and prognostication of medulloblastomas: a systematic review

Applications of radiomics for clinical management of medulloblastomas are expanding. This systematic review aims to aggregate and overview the current role of radiomics in the diagnosis, subtyping, and prognostication of medulloblastomas. The present systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed/MEDLINE were searched using a standardized search term. Articles found within the database from the inception until November 2022 were considered for screening. Retrieved records were screened independently by two authors based on their titles and abstracts. The full text of selected articles was reviewed to finalize the eligibility. Due to the heterogeneity of included studies, no formal data synthesis was conducted. Of the 249 screened citations, 21 studies were included and analyzed. Radiomics demonstrated promising performance for discriminating medulloblastomas from other posterior fossa tumors, particularly ependymomas and pilocytic astrocytomas. It was also efficacious in determining the subtype (i.e., WNT+, SHH+, group 3, and group 4) of medulloblastomas non-invasively. Regarding prognostication, radiomics exhibited some ability to predict overall survival and progression-free survival of patients with medulloblastomas. Our systematic review revealed that radiomics represents a promising tool for diagnosis and prognostication of medulloblastomas. Further prospective research measuring the clinical value of radiomics in this setting is warranted.

-New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates

Gliomas and Glioblastomas represent a significant portion of central nervous system (CNS) tumors associated with high mortality rates and variable prognosis. In 2021, the World Health Organization (WHO) updated its Glioma classification criteria, most notably incorporating molecular markers including CDKN2A/B homozygous deletion, TERT promoter mutation, EGFR amplification, + 7/-10 chromosome copy number changes, and others into the grading and classification of adult and pediatric Gliomas. The inclusion of these markers and the corresponding introduction of new Glioma subtypes has allowed for more specific tailoring of clinical interventions and has inspired a new wave of Radiogenomic studies seeking to leverage medical imaging information to explore the diagnostic and prognostic implications of these new biomarkers. Radiomics, deep learning, and combined approaches have enabled the development of powerful computational tools for MRI analysis correlating imaging characteristics with various molecular biomarkers integrated into the updated WHO CNS-5 guidelines. Recent studies have leveraged these methods to accurately classify Gliomas in accordance with these updated molecular-based criteria based solely on non-invasive MRI, demonstrating the great promise of Radiogenomic tools. In this review, we explore the relative benefits and drawbacks of these computational frameworks and highlight the technical and clinical innovations presented by recent studies in the landscape of fast evolving molecular-based Glioma subtyping. Furthermore, the potential benefits and challenges of incorporating these tools into routine radiological workflows, aiming to enhance patient care and optimize clinical outcomes in the evolving field of CNS tumor management, have been highlighted.

Challenges and Outlooks in Precision Medicine: Expectations Versus Reality

Recent developments in technology have led to rapid advances in precision medicine, especially due to the rise of next-generation sequencing and molecular profiling. These technological advances have led to rapid advances in research, including increased tumor subtype resolution, new therapeutic agents, and mechanistic insights. Certain therapies have even been approved for molecular biomarkers across histopathological diagnoses; however, translation of research findings to the clinic still faces a number of challenges. In this review, the authors discuss several key challenges to the clinical integration of precision medicine, including the blood-brain barrier, both a lack and excess of molecular targets, and tumor heterogeneity/escape from therapy. They also highlight a few key efforts to address these challenges, including new frontiers in drug delivery, a rapidly expanding treatment repertoire, and improvements in active response monitoring. With continued improvements and developments, the authors anticipate that precision medicine will increasingly become the gold standard for clinical care.

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.

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.

Advancing presurgical non-invasive molecular subgroup prediction in medulloblastoma using artificial intelligence and MRI signatures

Global investigation of medulloblastoma has been hindered by the widespread inaccessibility of molecular subgroup testing and paucity of data. To bridge this gap, we established an international molecularly characterized database encompassing 934 medulloblastoma patients from thirteen centers across China and the United States. We demonstrate how image-based machine learning strategies have the potential to create an alternative pathway for non-invasive, presurgical, and low-cost molecular subgroup prediction in the clinical management of medulloblastoma. Our robust validation strategies-including cross-validation, external validation, and consecutive validation-demonstrate the model's efficacy as a generalizable molecular diagnosis classifier. The detailed analysis of MRI characteristics replenishes the understanding of medulloblastoma through a nuanced radiographic lens. Additionally, comparisons between East Asia and North America subsets highlight critical management implications. We made this comprehensive dataset, which includes MRI signatures, clinicopathological features, treatment variables, and survival data, publicly available to advance global medulloblastoma research.

Neurosurgical short-term outcomes for pediatric medulloblastoma patients and molecular correlations: a 10-year single-center observation cohort study

This study examined the risk factors for short-term outcomes, focusing particularly on the associations among molecular subgroups. The analysis focused on the data of pediatric patients with medulloblastoma between 2013 and 2023, as well as operative complications, length of stay from surgery to adjuvant treatment, 30-day unplanned reoperation, unplanned readmission, and mortality. 148 patients were included. Patients with the SHH TP53-wildtype exhibited a lower incidence of complications (45.2% vs. 66.0%, odds ratio [OR] 0.358, 95% confidence interval [CI] 0.160 - 0.802). Female sex (0.437, 0.207 - 0.919) was identified as an independent protective factor for complications, and brainstem involvement (1.900, 1.297 - 2.784) was identified as a risk factor. Surgical time was associated with an increased risk of complications (1.004, 1.001 - 1.008), duration of hospitalization (1.006, 1.003 - 1.010), and reoperation (1.003, 1.001 - 1.006). Age was found to be a predictor of improved outcomes, as each additional year was associated with a 14.1% decrease in the likelihood of experiencing a prolonged length of stay (0.859, 0.772 - 0.956). Patients without metastasis exhibited a reduced risk of reoperation (0.322, 0.133 - 0.784) and readmission (0.208, 0.074 - 0.581). There is a significant degree of variability in the occurrence of operative complications in pediatric patients with medulloblastoma. SHH TP53-wildtype medulloblastoma is commonly correlated with a decreased incidence of complications. The short-term outcomes of patients are influenced by various unmodifiable endogenous factors. These findings could enhance the knowledge of onconeurosurgeons and alleviate the challenges associated with patient/parent education through personalized risk communication. However, the importance of a dedicated center with expertise surgical team and experienced neurosurgeon in improving neurosurgical outcomes appears self-evident.

A Radiomic Approach for Evaluating Intra-Subgroup Heterogeneity in SHH and Group 4 Pediatric Medulloblastoma: A Preliminary Multi-Institutional Study

Medulloblastoma (MB) is the most frequent malignant brain tumor in children with extensive heterogeneity that results in varied clinical outcomes. Recently, MB was categorized into four molecular subgroups, WNT, SHH, Group 3, and Group 4. While SHH and Group 4 are known for their intermediate prognosis, studies have reported wide disparities in patient outcomes within these subgroups. This study aims to create a radiomic prognostic signature, medulloblastoma radiomics risk (mRRisk), to identify the risk levels within the SHH and Group 4 subgroups, individually, for reliable risk stratification. Our hypothesis is that this signature can comprehensively capture tumor characteristics that enable the accurate identification of the risk level. In total, 70 MB studies (48 Group 4, and 22 SHH) were retrospectively curated from three institutions. For each subgroup, 232 hand-crafted features that capture the entropy, surface changes, and contour characteristics of the tumor were extracted. Features were concatenated and fed into regression models for risk stratification. Contrasted with Chang stratification that did not yield any significant differences within subgroups, significant differences were observed between two risk groups in Group 4 (p = 0.04, Concordance Index (CI) = 0.82) on the cystic core and non-enhancing tumor, and SHH (p = 0.03, CI = 0.74) on the enhancing tumor. Our results indicate that radiomics may serve as a prognostic tool for refining MB risk stratification, towards improved patient care.

Singular Value Decomposition-Based Penalized Multinomial Regression for Classifying Imbalanced Medulloblastoma Subgroups Using Methylation Data

Medulloblastoma (MB) is a molecularly heterogeneous brain malignancy with large differences in clinical presentation. According to genomic studies, there are at least four distinct molecular subgroups of MB: sonic hedgehog (SHH), wingless/INT (WNT), Group 3, and Group 4. The treatment and outcomes depend on appropriate classification. It is difficult for the classification algorithms to identify these subgroups from an imbalanced MB genomic data set, where the distribution of samples among the MB subgroups may not be equal. To overcome this problem, we used singular value decomposition (SVD) and group lasso techniques to find DNA methylation probe features that maximize the separation between the different imbalanced MB subgroups. We used multinomial regression as a classification method to classify the four different molecular subgroups of MB using the reduced DNA methylation data. Coordinate descent is used to solve our loss function associated with the group lasso, which promotes sparsity. By using SVD, we were able to reduce the 321,174 probe features to just 200 features. Less than 40 features were successfully selected after applying the group lasso, which we then used as predictors for our classification models. Our proposed method achieved an average overall accuracy of 99% based on fivefold cross-validation technique. Our approach produces improved classification performance compared with the state-of-the-art methods for classifying MB molecular subgroups.

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas

BACKGROUND

We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC.

METHOD

We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort.

RESULTS

A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset.

CONCLUSIONS AND RELEVANCE

We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.

The Site of Origin of Medulloblastoma: Surgical Observations Correlated to Molecular Groups

Developmental gene expression data from medulloblastoma (MB) suggest that WNT-MB originates from the region of the embryonic lower rhombic lip (LRL), whereas SHH-MB and non-WNT/non-SHH MB arise from cerebellar precursor matrix regions. This study aimed to analyze detailed intraoperative data with regard to the site of origin (STO) and compare these findings with the hypothesized regions of origin associated with the molecular group. A review of the institutional database identified 58 out of 72 pediatric patients who were operated for an MB at our department between 1996 and 2020 that had a detailed operative report and a surgical video as well as clinical and genetic classification data available for analysis. The STO was assessed based on intraoperative findings. Using the intraoperatively defined STO, "correct" prediction of molecular groups was feasible in 20% of WNT-MB, 60% of SHH-MB and 71% of non-WNT/non-SHH MB. The positive predictive values of the neurosurgical inspection to detect the molecular group were 0.21 (95% CI 0.08-0.48) for WNT-MB, 0.86 (95% CI 0.49-0.97) for SHH-MB and 0.73 (95% CI 0.57-0.85) for non-WNT/non-SHH MB. The present study demonstrated a limited predictive value of the intraoperatively observed STO for the prediction of the molecular group of MB.

Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment

PURPOSES

To review the uses of AI for magnetic resonance (MR) imaging assessment of primary pediatric cancer and identify common literature topics and knowledge gaps. To assess the adherence of the existing literature to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines.

MATERIALS AND METHODS

A scoping literature search using MEDLINE, EMBASE and Cochrane databases was performed, including studies of > 10 subjects with a mean age of < 21 years. Relevant data were summarized into three categories based on AI application: detection, characterization, treatment and monitoring. Readers independently scored each study using CLAIM guidelines, and inter-rater reproducibility was assessed using intraclass correlation coefficients.

RESULTS

Twenty-one studies were included. The most common AI application for pediatric cancer MR imaging was pediatric tumor diagnosis and detection (13/21 [62%] studies). The most commonly studied tumor was posterior fossa tumors (14 [67%] studies). Knowledge gaps included a lack of research in AI-driven tumor staging (0/21 [0%] studies), imaging genomics (1/21 [5%] studies), and tumor segmentation (2/21 [10%] studies). Adherence to CLAIM guidelines was moderate in primary studies, with an average (range) of 55% (34%-73%) CLAIM items reported. Adherence has improved over time based on publication year.

CONCLUSION

The literature surrounding AI applications of MR imaging in pediatric cancers is limited. The existing literature shows moderate adherence to CLAIM guidelines, suggesting that better adherence is required for future studies.

Current state of radiomics in pediatric neuro-oncology practice: a systematic review

BACKGROUND

Radiomics is the process of converting radiological images into high-dimensional data that may be used to create machine learning models capable of predicting clinical outcomes, such as disease progression, treatment response and survival. Pediatric central nervous system (CNS) tumors differ from adult CNS tumors in terms of their tissue morphology, molecular subtype and textural features. We set out to appraise the current impact of this technology in clinical pediatric neuro-oncology practice.

OBJECTIVES

The aims of the study were to assess radiomics' current impact and potential utility in pediatric neuro-oncology practice; to evaluate the accuracy of radiomics-based machine learning models and compare this to the current standard which is stereotactic brain biopsy; and finally, to identify the current limitations of radiomics applications in pediatric neuro-oncology.

MATERIALS AND METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards, a systematic review of the literature was carried out with protocol number CRD42022372485 in the prospective register of systematic reviews (PROSPERO). We performed a systematic literature search via PubMed, Embase, Web of Science and Google Scholar. Studies involving CNS tumors, studies that utilized radiomics and studies involving pediatric patients (age<18 years) were included. Several parameters were collected including imaging modality, sample size, image segmentation technique, machine learning model used, tumor type, radiomics utility, model accuracy, radiomics quality score and reported limitations.

RESULTS

The study included a total of 17 articles that underwent full-text review, after excluding duplicates, conference abstracts and studies that did not meet the inclusion criteria. The most commonly used machine learning models were support vector machines (n=7) and random forests (n=6), with an area under the curve (AUC) range of 0.60-0.94. The included studies investigated several pediatric CNS tumors, with ependymoma and medulloblastoma being the most frequently studied. Radiomics was primarily used for lesion identification, molecular subtyping, survival prognostication and metastasis prediction in pediatric neuro-oncology. The low sample size of studies was a commonly reported limitation.

CONCLUSION

The current state of radiomics in pediatric neuro-oncology is promising, in terms of distinguishing between tumor types; however, its utility in response assessment requires further evaluation which, given the relatively low number of pediatric tumors, calls for multicenter collaboration.

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.

Clinical, Histological, and Molecular Prognostic Factors in Childhood Medulloblastoma: Where Do We Stand?

Medulloblastomas, highly aggressive neoplasms of the central nervous system (CNS) that present significant heterogeneity in clinical presentation, disease course, and treatment outcomes, are common in childhood. Moreover, patients who survive may be diagnosed with subsequent malignancies during their life or could develop treatment-related medical conditions. Genetic and transcriptomic studies have classified MBs into four subgroups: wingless type (WNT), Sonic Hedgehog (SHH), Group 3, and Group 4, with distinct histological and molecular profiles. However, recent molecular findings resulted in the WHO updating their guidelines and stratifying medulloblastomas into further molecular subgroups, changing the clinical stratification and treatment management. In this review, we discuss most of the histological, clinical, and molecular prognostic factors, as well the feasibility of their application, for better characterization, prognostication, and treatment of medulloblastomas.

Radiomics-based machine learning models for prediction of medulloblastoma subgroups: a systematic review and meta-analysis of the diagnostic test performance

BACKGROUND

Medulloblastomas are a major cause of cancer-related mortality in the pediatric population. Four molecular groups have been identified, and these molecular groups drive risk stratification, prognostic modeling, and the development of novel treatment modalities. It has been demonstrated that radiomics-based machine learning (ML) models are effective at predicting the diagnosis, molecular class, and grades of CNS tumors.

PURPOSE

To assess radiomics-based ML models' diagnostic performance in predicting medulloblastoma subgroups and the methodological quality of the studies.

MATERIAL AND METHODS

A comprehensive literature search was performed on PubMed; the last search was conducted on 1 May 2022. Studies that predicted all four medulloblastoma subgroups in patients with histopathologically confirmed medulloblastoma and reporting area under the curve (AUC) values were included in the study. The quality assessments were conducted according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM). A meta-analysis of radiomics-based ML studies' diagnostic performance for the preoperative evaluation of medulloblastoma subgrouping was performed.

RESULTS

Five studies were included in this meta-analysis. Regarding patient selection, two studies indicated an unclear risk of bias according to the QUADAS-2. The five studies had an average CLAIM score and compliance score of 23.2 and 0.57, respectively. The meta-analysis showed pooled AUCs of 0.88, 0.82, 0.83, and 0.88 for WNT, SHH, group 3, and group 4 for classification, respectively.

CONCLUSION

Radiomics-based ML studies have good classification performance in predicting medulloblastoma subgroups, with AUCs >0.80 in every subgroup. To be applied to clinical practice, they need methodological quality improvement and stability.

Multimodal data fusion for cancer biomarker discovery with deep learning

Technological advances now make it possible to study a patient from multiple angles with high-dimensional, high-throughput multi-scale biomedical data. In oncology, massive amounts of data are being generated ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has significantly advanced the analysis of biomedical data. However, most approaches focus on single data modalities leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalised medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability, and standardisation of datasets.

Changes to pediatric brain tumors in 2021 World Health Organization classification of tumors of the central nervous system

New tumor types are continuously being described with advances in molecular testing and genomic analysis resulting in better prognostics, new targeted therapy options and improved patient outcomes. As a result of these advances, pathological classification of tumors is periodically updated with new editions of the World Health Organization (WHO) Classification of Tumors books. In 2021, WHO Classification of Tumors of the Central Nervous System, 5^th edition (CNS5), was published with major changes in pediatric brain tumors officially recognized including pediatric gliomas being separated from adult gliomas, ependymomas being categorized based on anatomical compartment and many new tumor types, most of them seen in children. Additional general changes, such as tumor grading now being done within tumor types rather than across entities and changes in definition of glioblastoma, are also relevant to pediatric neuro-oncology practice. The purpose of this manuscript is to highlight the major changes in pediatric brain tumors in CNS5 most relevant to radiologists. Additionally, brief descriptions of newly recognized entities will be presented with a focus on imaging findings.

Imaging genomics: data fusion in uncovering disease heritability

Sequencing of the human genome in the early 2000s enabled probing of the genetic basis of disease on a scale previously unimaginable. Now, two decades later, after interrogating millions of markers in thousands of individuals, a significant portion of disease heritability still remains hidden. Recent efforts to unravel this 'missing heritability' have focused on garnering new insight from merging different data types, including medical imaging. Imaging offers promising intermediate phenotypes to bridge the gap between genetic variation and disease pathology. In this review we outline this fusion and provide examples of imaging genomics in a range of diseases, from oncology to cardiovascular and neurodegenerative disease. Finally, we discuss how ongoing revolutions in data science and sharing are primed to advance the field.

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.

Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters

Purpose

To construct a machine learning model based on radiomics of multiparametric magnetic resonance imaging (MRI) combined with clinical parameters for predicting Sonic Hedgehog (SHH) and Group 4 (G4) molecular subtypes of pediatric medulloblastoma (MB).

Methods

The preoperative MRI images and clinical data of 95 patients with MB were retrospectively analyzed, including 47 cases of SHH subtype and 48 cases of G4 subtype. Radiomic features were extracted from T1-weighted imaging (T1), contrast-enhanced T1 weighted imaging (T1c), T2-weighted imaging (T2), T2 fluid-attenuated inversion recovery imaging (T2FLAIR), and apparent diffusion coefficient (ADC) maps, using variance thresholding, SelectKBest, and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The optimal features were filtered using LASSO regression, and a logistic regression (LR) algorithm was used to build a machine learning model. The receiver operator characteristic (ROC) curve was plotted to evaluate the prediction accuracy, and verified by its calibration, decision and nomogram. The Delong test was used to compare the differences between different models.

Results

A total of 17 optimal features, with non-redundancy and high correlation, were selected from 7,045 radiomics features, and used to build an LR model. The model showed a classification accuracy with an under the curve (AUC) of 0.960 (95% CI: 0.871-1.000) in the training cohort and 0.751 (95% CI: 0.587-0.915) in the testing cohort, respectively. The location of the tumor, pathological type, and hydrocephalus status of the two subtypes of patients differed significantly (p < 0.05). When combining radiomics features and clinical parameters to construct the combined prediction model, the AUC improved to 0.965 (95% CI: 0.898-1.000) in the training cohort and 0.849 (95% CI: 0.695-1.000) in the testing cohort, respectively. There was a significant difference in the prediction accuracy, as measured by AUC, between the testing cohorts of the two prediction models, which was confirmed by Delong's test (p = 0.0144). Decision curves and nomogram further validate that the combined model can achieve net benefits in clinical work.

Conclusion

The combined prediction model, constructed based on radiomics of multiparametric MRI and clinical parameters can potentially provide a non-invasive clinical approach to predict SHH and G4 molecular subtypes of MB preoperatively.

Novel MRI deformation-heterogeneity radiomic features are associated with molecular subgroups and overall survival in pediatric medulloblastoma: Preliminary findings from a multi-institutional study

Introduction

Medulloblastoma (MB) is a malignant, heterogenous brain tumor. Advances in molecular profiling have led to identifying four molecular subgroups of MB (WNT, SHH, Group 3, Group 4), each with distinct clinical behaviors. We hypothesize that (1) aggressive MB tumors, growing heterogeneously, induce pronounced local structural deformations in the surrounding parenchyma, and (b) these local deformations as captured on Gadolinium (Gd)-enhanced-T1w MRI are independently associated with molecular subgroups, as well as overall survival in MB patients.

Methods

In this work, a total of 88 MB studies from 2 institutions were analyzed. Following tumor delineation, Gd-T1w scan for every patient was registered to a normal age-specific T1w-MRI template via deformable registration. Following patient-atlas registration, local structural deformations in the brain parenchyma were obtained for every patient by computing statistics from deformation magnitudes obtained from every 5mm annular region, 0 < d < 60 mm, where d is the distance from the tumor infiltrating edge.

Results

Multi-class comparison via ANOVA yielded significant differences between deformation magnitudes obtained for Group 3, Group 4, and SHH molecular subgroups, observed up to 60-mm outside the tumor edge. Additionally, Kaplan-Meier survival analysis showed that the local deformation statistics, combined with the current clinical risk-stratification approaches (molecular subgroup information and Chang's classification), could identify significant differences between high-risk and low-risk survival groups, achieving better performance results than using any of these approaches individually.

Discussion

These preliminary findings suggest there exists significant association of our tumor-induced deformation descriptor with overall survival in MB, and that there could be an added value in using the proposed radiomic descriptor along with the current risk classification approaches, towards more reliable risk assessment in pediatric MB.

MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study

Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified. There were 1800 features extracted from T2- and contrast-enhanced T1-weighted preoperative MRI scans. A two-stage sequential classifier was designed-one that first identifies non-wingless (WNT) and non-sonic hedgehog (SHH) MB and then differentiates therapeutically relevant WNT from SHH. Further, a classifier that distinguishes high-risk group 3 from group 4 MB was developed. An independent, binary subgroup analysis was conducted to uncover radiomics features unique to infantile versus childhood SHH subgroups. The best-performing models from six candidate classifiers were selected, and performance was measured on holdout test sets. CIs were obtained by bootstrapping the test sets for 2000 random samples. Model accuracy score was compared with the no-information rate using the Wald test. Results The study cohort comprised 263 patients (mean age ± SD at diagnosis, 87 months ± 60; 166 boys). A two-stage classifier outperformed a single-stage multiclass classifier. The combined, sequential classifier achieved a microaveraged F1 score of 88% and a binary F1 score of 95% specifically for WNT. A group 3 versus group 4 classifier achieved an area under the receiver operating characteristic curve of 98%. Of the Image Biomarker Standardization Initiative features, texture and first-order intensity features were most contributory across the molecular subgroups. Conclusion An MRI-based machine learning decision path allowed identification of the four clinically relevant molecular pediatric medulloblastoma subgroups. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chaudhary and Bapuraj in this issue.

Advanced Neuroimaging Approaches to Pediatric Brain Tumors

Simple Summary

After leukemias, brain tumors are the most common cancers in children, and early, accurate diagnosis is critical to improve patient outcomes. Beyond the conventional imaging methods of computed tomography (CT) and magnetic resonance imaging (MRI), advanced neuroimaging techniques capable of both structural and functional imaging are moving to the forefront to improve the early detection and differential diagnosis of tumors of the central nervous system. Here, we review recent developments in neuroimaging techniques for pediatric brain tumors.

Abstract

Central nervous system tumors are the most common pediatric solid tumors; they are also the most lethal. Unlike adults, childhood brain tumors are mostly primary in origin and differ in type, location and molecular signature. Tumor characteristics (incidence, location, and type) vary with age. Children present with a variety of symptoms, making early accurate diagnosis challenging. Neuroimaging is key in the initial diagnosis and monitoring of pediatric brain tumors. Conventional anatomic imaging approaches (computed tomography (CT) and magnetic resonance imaging (MRI)) are useful for tumor detection but have limited utility differentiating tumor types and grades. Advanced MRI techniques (diffusion-weighed imaging, diffusion tensor imaging, functional MRI, arterial spin labeling perfusion imaging, MR spectroscopy, and MR elastography) provide additional and improved structural and functional information. Combined with positron emission tomography (PET) and single-photon emission CT (SPECT), advanced techniques provide functional information on tumor metabolism and physiology through the use of radiotracer probes. Radiomics and radiogenomics offer promising insight into the prediction of tumor subtype, post-treatment response to treatment, and prognostication. In this paper, a brief review of pediatric brain cancers, by type, is provided with a comprehensive description of advanced imaging techniques including clinical applications that are currently utilized for the assessment and evaluation of pediatric brain tumors.

Machine learning in neuro-oncology: toward novel development fields

PURPOSE

Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology.

METHODS

We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments.

RESULTS

Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data.

CONCLUSION

It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.

Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics

Objective:

Image-based prediction of molecular subgroups of Medulloblastoma (MB) has the potential to optimize and personalize therapy. The objective of the study is to distinguish between broad molecular subgroups of MB using MR–Texture analysis.

Methods:

Thirty-eight MB patients treated between 2007 and 2020 were retrospectively analyzed. Texture analysis was performed on contrast enhanced T1(T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on all slices and radiomic features were extracted which included first order, second order (GLCM - Grey level co-occurrence matrix) and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression and thereafter Support Vector Machine (SVM) and a 10-fold cross-validation strategy was used for model development. The area under Receiver Operator Characteristic (ROC) curve was used to evaluate the model.

Results:

A total of 174 and 170 images were obtained for analysis from the Axial T1C and T2W image datasets. One hundred and sixty-four MR based texture features were extracted. The best model was arrived at by using a combination of 30 GLCM and six shape features on T1C MR sequence. A 10-fold cross-validation demonstrated an AUC of 0.93, 0.9, 0.93, and 0.93 in predicting WNT, SHH, Group 3, and Group 4 MB subgroups, respectively.

Conclusion:

Radiomic analysis of MR images in MB can predict molecular subgroups with acceptable degree of accuracy. The strategy needs further validation in an external dataset for its potential use in ab initio management paradigms of MBs.

Advances in knowledge:

Medulloblastoma can be classified into four distinct molecular subgroups using radiomic feature classifier from non-invasive Multiparametric Magnetic resonance imaging. This can have future ramifications in the extent of surgical resection of Medulloblastoma which can ultimately result in reduction of morbidity.

Radiomics signature for the prediction of progression-free survival and radiotherapeutic benefits in pediatric medulloblastoma

PURPOSE

To develop and validate a radiomics signature for progression-free survival (PFS) and radiotherapeutic benefits in pediatric medulloblastoma.

MATERIALS AND METHODS

We retrospectively enrolled 253 consecutive children with medulloblastoma from two hospitals. A total of 1294 radiomic features were extracted from the region of tumor on the T1-weighted and contrast-enhanced T1-weighted (CE-T1w) MRI. Radiomic feature selection and machine learning modelling were performed to build radiomics signature for the prediction of PFS on the training set. Moreover, the prognostic performance of the clinical parameters was investigated for PFS. The Concordance index (a value of 0.5 indicates no predictive discrimination, and a value of 1 indicates perfect predictive discrimination) was used to measure and compare the prognostic performance of these models.

RESULTS

The radiomics signature for the prediction of the PFS yielded Concordance indices of 0.711, 0.707, and 0.717 on the training and held-out test sets 1 and 2, respectively. The radiomics nomogram integrating the radiomics signature, age, and metastasis performed better than the nomogram incorporating only clinicopathological factors (C-index, 0.723 vs. 0.665 and 0.722 vs. 0.677 on the held-out test sets 1 and 2, respectively), which was also validated by the good calibration and decision curve analysis. Further analysis demonstrated that patients with lower value of radiomics signature were associated with better clinical outcomes after postoperative radiotherapy (p < 0.001).

CONCLUSION

The radiomics signature and nomogram performed well for the prediction of PFS and could stratify patients underwent postoperative radiotherapy into the high- and low-risk groups with significantly different clinical outcomes.

Imaging diagnosis and treatment selection for brain tumors in the era of molecular therapeutics

Currently, most CNS tumors require tissue sampling to discern their molecular/genomic landscape. However, growing research has shown the powerful role imaging can play in non-invasively and accurately detecting the molecular signature of these tumors. The overarching theme of this review article is to provide neuroradiologists and neurooncologists with a framework of several important molecular markers, their associated imaging features and the accuracy of those features. A particular emphasis is placed on those tumors and mutations that have specific or promising imaging correlates as well as their respective therapeutic potentials.

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.

Multidisciplinary Management of Medulloblastoma: Consensus, Challenges, and Controversies

Medulloblastoma is a highly aggressive "small round blue cell tumor" of the posterior fossa predominantly seen in children. Historically aggressive multimodality regimens have achieved encouraging outcomes with the caveat of severe long-term toxicities. The last decade has unleashed a revolution in terms of evolved understanding of this heterogeneous disease entity in terms of molecular biology. Medulloblastoma as of today is grouped into one of four canonical molecular subgroups (WNT, SHH, Group 3, and Group 4) each characterized by different putative cells of origin, characteristic aberrations at the molecular level, radiogenomics, and outcomes. Our understanding continues to grow in this regard. The future promises much in terms of personalized medicine in tailoring therapy to the needs of individual patients based on their clinical and molecular profile in order to maximize individual and population based outcomes at the cost of minimizing toxicity.

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Simple Summary

Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

Abstract

The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

Pearls and Pitfalls of Imaging in Pediatric Brain Tumors

The central nervous system (CNS) tumors constitute the most common type of solid tumors in the pediatric population. The cerebral and cerebellar parenchyma are the most common site of pediatric CNS neoplasms. Imaging plays an important role in detection, characterization, staging and prognostication of brain tumors. The focus of the current article is pediatric brain tumor imaging with emphasis on pearls and pitfalls of conventional and advanced imaging in various pediatric brain tumor subtypes. The article also elucidates changes in brain tumor terms and entities as applicable to pediatric patients, updated as per World Health Organization (WHO) 2016 classification of primary CNS tumors. This classification introduced the genetic and/or molecular information of primary CNS neoplasms as part of comprehensive tumor pathology report in the routine clinical workflow. The concepts from 2016 classification have been further refined based on current research, by the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) group and published in the form of updates. The updates serve as guidelines in the time interval between WHO updates and expect to be broadly adopted in the subsequent WHO classification. The current review covers most pediatric brain tumors except pituitary tumors, meningeal origin tumors, nerve sheath tumors and CNS lymphoma/leukemia.

Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma

BACKGROUND

Medulloblastoma (MB) is a common central nervous system tumor in children with extensive heterogeneity and different prognoses. This study aimed to classify the Ki-67 index in MB with radiomic characteristics based on multi-parametric magnetic resonance imaging to guide treatment and assess the prognosis of patients.

METHODS

Three sequences of T1W, CE-T1W, and T2W were used as test data. Two experienced radiologists manually segmented the tumors according to T2W images from 90 patients. The patients were divided into training and test sets at a ratio of 7:3, and 833 dimensional image features were extracted for each patient. Five models were trained using the feature set selected in three ways. Finally, the area under the curve (AUC) and accuracy (ACC) were used on the test set to evaluate the performance of the different models.

RESULTS

A random forest (RF) model combining three sequence features achieved the best performance (ACC: 0.771, 95% CI: 0.727 to 0.816; AUC: 0.697, 95% CI: 0.614 to 0.78). The voting model that combined a RF and a support vector machine (SVM) had higher performance than the other models (ACC: 0.796, 95% CI: 0.76 to 0.833; AUC: 0.689, 95% CI: 0.615 to 0.763). The best prediction model that used only one sequence feature was voting in the T2W sequence (ACC: 0.736, 95% CI: 0.705 to 0.766; AUC: 0.636, 95% CI: 0.585 to 0.688). The ensemble model was better than the single training model, and a multi-sequence combination was better than a single sequence prediction. The multiple feature selection methods were better than a combination of the two methods.

CONCLUSIONS

A model obtained by machine learning could help doctors predict the Ki-67 values of patients more efficiently to make targeted judgments for subsequent treatments.

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.

Magnetic Resonance Imaging in the Contemporary Management of Medulloblastoma: Current and Emerging Applications

Medulloblastoma, the most common malignant primary brain tumor in children, is now considered to comprise of four distinct molecular subgroups-wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 medulloblastoma, each associated with distinct developmental origins, unique transcriptional profiles, diverse phenotypes, and variable clinical behavior. Due to its exquisite anatomic resolution, multiparametric nature, and ability to image the entire craniospinal axis, magnetic resonance imaging (MRI) is the preferred and recommended first-line imaging modality for suspected brain tumors including medulloblastoma. Preoperative MRI can reliably differentiate medulloblastoma from other common childhood posterior fossa masses such as ependymoma, pilocytic astrocytoma, and brainstem glioma. On T1-weighted images, medulloblastoma is generally iso- to hypointense, while on T2-weighted images, the densely packed cellular component of the tumor is significantly hypointense and displays restricted diffusion on diffusion-weighted imaging. Following intravenous gadolinium, medulloblastoma shows significant but variable and heterogeneous contrast enhancement. Given the propensity of neuraxial spread in medulloblastoma, sagittal fat-suppressed T1-postcontrast spinal MRI is recommended to rule out leptomeningeal metastases for accurate staging. Following neurosurgical excision, postoperative MRI done within 24-48 h confirms the extent of resection, accurately quantifying residual tumor burden imperative for risk assignment. Post-treatment MRI is needed to assess response and effectiveness of adjuvant radiotherapy and systemic chemotherapy. After completion of planned therapy, surveillance MRI is recommended periodically on follow-up for early detection of recurrence for timely institution of salvage therapy, as well as for monitoring treatment-related late complications. Recent studies suggest that preoperative MRI can reliably identify SHH and Group 4 medulloblastoma but has suboptimal predictive accuracy for WNT and Group 3 tumors. In this review, we focus on the role of MRI in the diagnosis, staging, and quantifying residual disease; post-treatment response assessment; and periodic surveillance, and provide a brief summary on radiogenomics in the contemporary management of medulloblastoma.

Radiomics and radiogenomics in pediatric neuro-oncology: A review

The current era of advanced computing has allowed for the development and implementation of the field of radiomics. In pediatric neuro-oncology, radiomics has been applied in determination of tumor histology, identification of disseminated disease, prognostication, and molecular classification of tumors (ie, radiogenomics). The field also comes with many challenges, such as limitations in study sample sizes, class imbalance, generalizability of the methods, and data harmonization across imaging centers. The aim of this review paper is twofold: first, to summarize existing literature in radiomics of pediatric neuro-oncology; second, to distill the themes and challenges of the field and discuss future directions in both a clinical and technical context.

Anatomic Neuroimaging Characteristics of Posterior Fossa Type A Ependymoma Subgroups

BACKGROUND AND PURPOSE

Posterior fossa type A (PFA) ependymomas have 2 molecular subgroups (PFA-1 and PFA-2) and 9 subtypes. Gene expression profiling suggests that PFA-1 and PFA-2 tumors have distinct developmental origins at different rostrocaudal levels of the brainstem. We, therefore, tested the hypothesis that PFA-1 and PFA-2 ependymomas have different anatomic MR imaging characteristics at presentation.

MATERIALS AND METHODS

Two neuroradiologists reviewed the preoperative MR imaging examinations of 122 patients with PFA ependymomas and identified several anatomic characteristics, including extension through the fourth ventricular foramina and encasement of major arteries and tumor type (midfloor, roof, or lateral). Deoxyribonucleic acid methylation profiling assigned ependymomas to PFA-1 or PFA-2. Information on PFA subtype from an earlier study was also available for a subset of tumors. Associations between imaging variables and subgroup or subtype were evaluated.

RESULTS

No anatomic imaging variable was significantly associated with the PFA subgroup, but 5 PFA-2c subtype ependymomas in the cohort had a more circumscribed appearance and showed less tendency to extend through the fourth ventricular foramina or encase blood vessels, compared with other PFA subtypes.

CONCLUSIONS

PFA-1 and PFA-2 ependymomas did not have different anatomic MR imaging characteristics, and these results do not support the hypothesis that they have distinct anatomic origins. PFA-2c ependymomas appear to have a more anatomically circumscribed MR imaging appearance than the other PFA subtypes; however, this needs to be confirmed in a larger study.