Radiomics may increase the prognostic value for survival in glioblastoma patients when combined with conventional clinical and genetic prognostic models

To evaluate the additional prognostic value of multiparametric MR-based radiomics in patients with glioblastoma when combined with conventional clinical and genetic prognostic factors.

In this single-center study, patients diagnosed with glioblastoma between October 2007 and December 2019 were retrospectively screened and grouped into training and test sets with a 7:3 distribution. Segmentations of glioblastoma using multiparametric MRI were performed automatically via a convolutional-neural network. Prognostic factors in the clinical model included age, sex, type of surgery/post-operative treatment, and tumor location; those in the genetic model included statuses of isocitrate dehydrogenase-1 mutation and O-6-methylguanine-DNA-methyltransferase promoter methylation. Univariate and multivariate Cox proportional hazards analyses were performed for overall survival (OS) and progression-free survival (PFS). Integrated time-dependent area under the curve (iAUC) for survival was calculated and compared between prognostic models via the bootstrapping method (performances were validated with prediction error curves).

Overall, 120 patients were included (training set, 85; test set, 35). The mean OS and PFS were 25.5 and 18.6 months, respectively. The prognostic performances of multivariate models improved when radiomics was added to the clinical model (iAUC: OS, 0.62 to 0.73; PFS, 0.58 to 0.66), genetic model (iAUC: OS, 0.59 to 0.67; PFS, 0.59 to 0.65), and combined model (iAUC: OS, 0.65 to 0.73; PFS, 0.62 to 0.67). In the test set, the combined model (clinical, genetic, and radiomics) demonstrated robust validation for risk prediction of OS and PFS.

Radiomics increased the prognostic value when combined with conventional clinical and genetic prognostic models for OS and PFS in glioblastoma patients.