Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study

Objectives To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme ((iBM), 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.

Automated summary

This study developed a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM) and investigated the biological pathways and genetic alterations underlying DLIS. DLIS was associated with survival and an independent predictor. The integrated nomogram incorporating DLIS achieved improved prediction performance compared to the clinicomolecular nomogram. DLIS was correlated with core pathways of GBM and key genetic alterations.