Document Type
Article
Publication Date
9-13-2023
Abstract
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.
Recommended Citation
Familiar, Ariana M.; Mahtabfar, Aria; Fathi Kazerooni, Anahita; Kiani, Mahsa; Vossough, Arastoo; Viaene, Angela; Storm, Philip B.; Resnick, Adam C.; and Nabavizadeh, Ali, "Radio-Pathomic Approaches in Pediatric Neurooncology: Opportunities and Challenges" (2023). Department of Neurosurgery Faculty Papers. Paper 216.
https://jdc.jefferson.edu/neurosurgeryfp/216
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Language
English
Comments
This article is the author's final published version in Neuro-Oncology Advances, Volume 5, Issue 1, January-December 2023, Article number vdad119.
The final published version is available at https://doi.org/10.1093/noajnl/vdad119. Copyright © The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.