Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors

Abstract

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.

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

“…This underscores the need for quantifying the tumor heterogeneity, which can help identify patients, within the same subgroup, with low risk that can benefit from de-escalated therapy from those with high risk and in need of intensified treatment strategies. While there are many radiomic approaches in the literature that aimed to carry out molecular subgroup classification [ 46 ], our study uniquely seeks to utilize those radiomic tools to further sub-stratify patients within the individual subgroups. Further optimization of mRRisk with rigorous validation on large multi-institutional cohorts would allow for an enriched risk stratification.…”

Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication

“…Although these models may prove to be robust predictors of a given outcome, clinical integration demands further evidence of a biologic relationship and/or molecular mechanism. As the field of radiomics continues to grow and develop, intersectionality with histology [39,40], pathology [41,42], and genomics [43,44] offers high potential for biologic validation and improved clinical interpretability of radiomic models. Correlation with local pathologic analysis, for example, can provide a direct comparison of quantitative, pathologic features to explain structural characteristics underlying radiologic textures.…”

Multi-Institutional Development and Validation of a Radiomic Model to Predict Prostate Cancer Recurrence Following Radical Prostatectomy

“…As the field of radiomics continues to grow and develop, intersectionality with histology [31,32], pathology [33,34], and genomics [35,36] offers high potential for biologic validation and improved clinical interpretability of radiomic models. Correlation with local pathologic analysis, for example, can provide a direct comparison of quantitative pathologic features to explain structural characteristics underlying radiologic textures.…”