NIMG-37. JOINT LEARNING OF IMAGING AND GENOMIC DATA REVEALS DISTINCT GLIOBLASTOMA SUBTYPES
نویسندگان
چکیده
Abstract PURPOSE The significant heterogeneity of glioblastoma is typically displayed on both phenotypical and molecular levels. Non-invasive in vivo approaches to characterize this would potentially facilitate personalized therapies. Here we leverage advanced unsupervised machine learning integrate radiomic imaging features genomics identify distinct subtypes glioblastoma. METHODS A retrospective cohort 571 IDH-wildtype patients were collected with pre-operative multi-parametric MRI (T1, T1CE, T2, T2-FLAIR, DSC, DTI) scans (available 462/571 patients) targeted next-generation sequencing (NGS) data 355/571 patients). Radiomic (n= 971) extracted from these a subset 12 selected by L21-norm minimization. total 14 key driver genes the 5 main pathways that are most frequently altered chosen. Subtypes identified joint approach called Anchor-based Partial Multi-modal Clustering (APMC) genomic modalities. RESULTS Three discovered APMC based 14-dimension NGS together representing characteristics histograms, shape, volumetric measures for different tumor sub-regions. 1) high-risk; 2) medium-risk; 3) low-risk, terms their overall survival outcome Kaplan-Meier analysis (p= 5.52e-6, log-rank test; HR= 1.51, 95%CI:1.20-1.74, Cox proportional hazard model). three also characteristics: subtype 1 exhibited increased frequency mutation [EGFR, PIK3CA, PTEN, NF1], 3 showed mutated [PDGFRA, ATRX], while 2 did not show differences mutations any genes. CONCLUSION Our results revealed synergistic value integrated signatures subtyping. Joint modalities could help better understand basis further provide insights into biologic underpinnings formation progression.
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ژورنال
عنوان ژورنال: Neuro-oncology
سال: 2022
ISSN: ['1523-5866', '1522-8517']
DOI: https://doi.org/10.1093/neuonc/noac209.655