Advances in Radiomic, Genomic and Radiogenomic Techniques in Pediatric Medulloblastoma
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".
Deadline for manuscript submissions: 15 February 2025 | Viewed by 1490
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue will focus on introducing novel machine learning radiomic approaches as well as genomic and radiogenomic approaches for the management of pediatric medulloblastoma. Specifically, works that aim to develop approaches to improve risk-stratification, outcome prediction, tumor segmentation, or molecular subgroup classification are welcome to be submitted. The goal of this Special Issue is to show works that utilize novel machine learning and deep learning tools that incorporate features from routine imaging and histology, individually or combined, that add value to the current approaches used in the clinical setting, towards improving patient outcomes. This Special Issue will extend on the current literature in terms of the opportunities and advances available in the fields of radiomics and radiogenomics in pediatric brain tumors, an area that is still underserved and is in dire need of more work to aid in the understanding of this disease etiology.
Dr. Marwa Ismail
Guest Editor
Manuscript Submission Information
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Keywords
- medulloblastoma
- pediatrics
- radiomics
- genomics
- segmentation
- deep learning
- classification
- risk-stratification
- molecular subgroups
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