Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers.

Abstract

Objectives Widespread clinical use of MRI radiomic tumor profiling for prognostication and treatment planning in cancers faces major obstacles due to limitations in standardization of radiomic features. The purpose of the current work was to assess the impact of different MRI scanning- and normalization protocols for the statistical analyses of tumor radiomic data in two patient cohorts with uterine endometrial- (EC) (n = 136) and cervical (CC) (n = 132) cancer. Material and methods 1.5 T and 3 T, T1-weighted MRI 2 minutes post-contrast injection, T2-weighted turbo spin echo imaging, and diffusion-weighted imaging were acquired. Radiomic features were extracted from within manually segmented tumors in 3D and normalized either using z-score normalization or a linear regression model (LRM) accounting for linear dependencies with MRI acquisition parameters. Patient clustering into two groups based on radiomic profile. Impact of MRI scanning parameters on cluster composition and prognostication by cluster groups were analyzed using Kruskal-Wallis tests, Kaplan-Meier plots, log-rank test and random survival forest time-dependent area under curve (tdAUC) (α = 0.05). Results A large proportion of the radiomic features was statistically associated with MRI scanning protocol in both cohorts (EC: 162/385 [42%]; CC: 180/292 [62%]). A substantial number of EC (49/136 [36%]) and CC (50/132 [38%]) patients changed cluster when clustering was performed after z-score- versus LRM normalization. Prognostic modeling based on cluster groups yielded similar outputs for the two normalization methods in the EC/CC cohorts (log-rank test; z-score: p = 0.02/0.33; LRM: p = 0.01/0.45). Mean tdAUC for prognostic modeling of disease-specific survival (DSS) by the radiomic features in EC/CC was similar for the two normalization methods (random survival forest; z-score: mean tdAUC = 0.77/0.78; LRM: mean tdAUC = 0.80/0.75). Conclusions Severe biases in tumor radiomics data due to MRI scanning parameters exist. Z-score normalization does not eliminate these biases, whereas LRM normalization effectively does. Still, radiomic cluster groups after z-score- and LRM normalization were associated with similar DSS in EC and CC patients.