Abstract
Background
High incidence and malignancy call for non-invasive pre-surgery survival prediction in gliomas. Radiomics serves as a mature solution bridging this gap.
Methods
We retrospectively collected preoperative MRI from 353 patients with diffuse gliomas, comprising 108 from our institution (Center1) and 137 from The Cancer Genome Atlas dataset (TCGA) as the training cohort, with an external 108 cases from Center1 serving as an independent test cohort. Radiomic features were automatically extracted from MRI, including structural image of T1WI, T2WI, T1CE, FLAIR, and functional image of DWI (b = 1000), and ADC maps. Following a series of feature engineering and machine learning procedures, features were selected to construct the prognostic model, culminating in the radiomics survival biomarker (RadSurv). The efficacy of individual structural and functional sequences and their combinations were evaluated at all glioma, glioblastoma (GBM) and non-GBM levels using the concordance index (C-index). The optimal all-sequence combination model's RadSurv then underwent Kaplan-Meyer analysis and Cox regression analysis, and we finally developed nomograms.
Results
T1CE, ADC and FLAIR led single-sequence performance, while DWI lagged. T1CE was optimal for all glioma (C-index = 0.799) and GBM (C-index = 0.706), whereas ADC for non-GBM (C-index = 0.917). Multi-sequence combinations didn't improve predictions (C-index Glioma = 0.787, C-index GBM = 0.689, C-index non−GBM = 0.893), but them win over adaptability. RadSurv effectively stratified risk for the all glioma, GBM and non-GBM populations through three predetermined cut-off values. Multivariate Cox regression confirmed RadSurv as an independent prognostic factor. The nomogram, constructed from RadSurv and age, accurately predicted survival probabilities and median survival times for glioma patients at various time points, especially for GBM.
Conclusions
The preoperative radiomics model's prognostic biomarker, RadSurv, effectively stratifies risk in glioma patients and, through nomograms, enables precise and quantifiable predictions of patient survival outcomes, warranting its utilization in clinical practice.