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


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Guest Editor
Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Interests: medulloblastoma

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

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Keywords

  • medulloblastoma
  • pediatrics
  • radiomics
  • genomics
  • segmentation
  • deep learning
  • classification
  • risk-stratification
  • molecular subgroups

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Published Papers (1 paper)

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Research

13 pages, 4553 KiB  
Article
A Radiomic Approach for Evaluating Intra-Subgroup Heterogeneity in SHH and Group 4 Pediatric Medulloblastoma: A Preliminary Multi-Institutional Study
by Marwa Ismail, Hyemin Um, Ralph Salloum, Fauzia Hollnagel, Raheel Ahmed, Peter de Blank and Pallavi Tiwari
Cancers 2024, 16(12), 2248; https://doi.org/10.3390/cancers16122248 - 18 Jun 2024
Viewed by 1131
Abstract
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 [...] Read more.
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. Full article
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