Radiomic subtyping improves disease stratification beyond key molecular, clinical and standard imaging characteristics in patients with glioblastoma
The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation set) beyond the assessed molecular, clinical and standard imaging parameters (p≤0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (as compared to 29% and 27% with molecular + clinical features alone). The radiomic signature was – along with MGMT-status – the only parameter with independent significance on multivariate analysis (p≤0.01).
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