In this study, radiomics derived from advanced MRI such as DWI and PWI was used to predict survival in patients with lower-grade gliomas. We observed that radiomics derived from both DWI and PWI, rather than single layer radiomics, performed better for OS prediction. The optimal RRS cut-off derived from the training set divided the test set into two groups with significantly different survival outcomes, demonstrating the RRS’ prognostic value. Further, adding DWI and PWI radiomics to clinical features significantly increased model performance for OS prediction, as validated in the test set. Calibration curves proved the prognostic accuracy of a nomogram constructed from clinical features and radiomics. Our study, therefore, suggests that DWI and PWI radiomics may allow non-invasive risk stratification of patients with lower-grade gliomas and can be used as a potential imaging biomarker.
Little has been studied regarding the prognostic value of advanced MRI radiomics, from either DWI or PWI. Previous studies used advanced MRI radiomics to predict tumor grade and specific genetic mutation status in lower-grade gliomas. The radiomics derived from an ADC map, not FLAIR, provided the highest prediction accuracy for determining the IDH mutation status17. Furthermore, most of the top contributing features for the prediction of tumor grades were derived from the ADC map16. Multiparametric MRI radiomics including conventional MRI, ADC, and CBV also outperformed conventional MRI radiomics in tumor grading16. Hence, we focused on the added prognostic role of advanced MRI radiomics, rather than conventional MRI radiomics, over clinical features in patients with lower-grade gliomas. We observed that the RRS from advanced MRI radiomics was one of the independent risk factors for survival prediction. The combined clinical and radiomics model achieved superior performance for OS prediction compared to the clinical model, with iAUC being 0.883 in the test set. Further, the performance of the combined model was comparable to that of the clinicopathologic model including the IDH mutation status, one of the most powerful prognostic factors that can be obtained after invasive surgery.
Numerous studies investigated the prognostic role of perfusion MRI in patients with gliomas, mostly focusing on patients with glioblastomas. The rCBV and Ktrans, obtained from DSC and DCE MRI respectively, were frequently reported to significantly negatively correlate with survival, thereby having potential as imaging biomarkers for risk stratification27–29. Recently, radiomics has been applied to the rCBV or Ktrans map, showing that perfusion MRI radiomics provided useful information for predicting survival30, improved prognostication over clinical features31, had significant association with recurrence or progression, and enabled prediction of recurrence pattern32 in patients with glioblastomas. Even perfusion MRI radiomics derived from nonenhancing, T2 hyperintense lesions of glioblastomas can also predict prognosis and had a significant association with progression-free survival or OS33. However, little is known about the prognostic role of perfusion MRI radiomics in lower-grade gliomas. In our study, a single layer perfusion MRI radiomics alone, derived from both rCBV and Ktrans map, could accurately predict OS with C-index of 0.753 in the training set. Furthermore, among 14 selected features which constituted a combined DWI and PWI radiomics, 10 were from either rCBV or Ktrans map. Therefore, we believe that PWI radiomics play a significant role in OS prediction not only in glioblastomas but also in patients with lower-grade gliomas. Finally, a combined DWI and PWI radiomics model achieved high accuracy for OS prediction when added to clinical features, which proved the added prognostic value of PWI radiomics in patients with lower-grade gliomas.
We then constructed a nomogram from the combined clinical and radiomics model for OS prediction, including age, gender, KPS, and radiomics derived from both DWI and PWI. A previous study using an independently validated nomogram in lower-grade gliomas concluded that grade 2 tumor, younger age at diagnosis, having a high KPS, and the IDH mutant, 1p19q-codeleted molecular subtype increased the probability of survival34. This nomogram included clinically relevant pathologic features such as WHO grade and molecular subtype. In our study, we only included features available in the preoperative setting, so that the nomogram can calculate individualized survival probabilities before surgery. In addition, DWI and PWI radiomics from preoperative MRI were added. It was proven to have an effective tool for providing individualized survival probabilities with a C-index of 0.833 in the test set and good calibration.
The top contributing feature for the OS prediction was the skewness from ADC, a first order feature. Skewness, a histogram parameter, denotes an asymmetric distribution. As lower ADC values and their heterogeneity reflect increased tumor cellularity and heterogeneity35, ADC skewness may have a significant association with survival. Among 14 selected features, half were texture features, which quantify the image pattern on the basis of the spatial relationship or co-occurrence of pixel values36 and provide information on intratumoral heterogeneity10. As in gliomas intratumoral heterogeneity has been reportedly associated with aggressive tumor behaviour and drug resistance37, texture features may play a key role in predicting prognosis.
There are several limitations in this study. First, it is a retrospective study with a relatively small sample size, because only patients with lower-grade glioma with both preoperative DSC and DCE MRI were included. Identification of an external validation set with patients with those same characteristics was not feasible; therefore, we performed temporal validation. Further studies using a larger cohort are required to validate our results. Second, important prognostic molecular markers such as epidermal growth factor receptor amplification or telomerase reverse transcriptase gene promoter mutation were not included in the analysis due to lack of information in a considerable number of patients. Future studies may validate the prognostic role of advanced MRI radiomics in consideration of those important molecular markers.
In conclusion, diffusion- and perfusion-weighted MRI radiomics enables non-invasive risk stratification and can improve survival prediction when added to the clinical features in patients with lower-grade gliomas.