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- Title
Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.
- Authors
Yan, Jing; Sun, Qiuchang; Tan, Xiangliang; Liang, Chaofeng; Bai, Hongmin; Duan, Wenchao; Mu, Tianhao; Guo, Yang; Qiu, Yuning; Wang, Weiwei; Yao, Qiaoli; Pei, Dongling; Zhao, Yuanshen; Liu, Danni; Duan, Jingxian; Chen, Shifu; Sun, Chen; Wang, Wenqing; Liu, Zhen; Hong, Xuanke
- Abstract
Objectives: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS. Methods: The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS. Results: The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01). Conclusions: Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment. Key Points: • MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.
- Subjects
DEEP learning; GENOMES; GENOMICS; MAGNETIC resonance imaging; APOPTOSIS
- Publication
European Radiology, 2023, Vol 33, Issue 2, p904
- ISSN
0938-7994
- Publication type
Article
- DOI
10.1007/s00330-022-09066-x