Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients

Abstract

Background

Pseudoprogression is a diagnostic challenge in early post-treatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma.

Methods

The model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC), and cerebral blood volume (CBV) maps. Imaging features were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver-operating-characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and an internal prospective registry data (n = 23).

Results

Twelve significant radiomic features (3 from conventional, 2 from diffusion and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC 0.57-0.79, P<.05), and better than single radiomics model using conventional MRI (AUC 0.76, P =.012), ADC (AUC 0.78, P =.014), or CBV (AUC 0.80, P=.43). The multiparametric radiomics showed higher performance in the external validation (AUC 0.85) and internal validation (AUC 0.96) than any single approach, thus demonstrating robustness.

Conclusions

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.