Purpose: To determine whether radiological phenotype can improve the predictive performance of the risk model based on molecular subtype and clinical risk factors in anaplastic glioma patients. Methods: This retrospective study was approved by our institutional review board with waiver of informed consent. MR images of 86 patients with pathologically diagnosed anaplastic glioma (WHO grade III) between January 2007 and February 2016 were analyzed according to the Visually Accessible Rembrandt Images (VASARI) features set. Significant imaging findings were selected to generate a radiological risk score (RRS) for overall survival (OS) and progression-free survival (PFS) using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The prognostic value of RRS was evaluated with multivariate Cox regression including molecular subtype and clinical risk factors. The C-indices of multivariate models with and without RRS were compared by bootstrapping. Results: Eight VASARI features contributed to RRS for OS and six contributed to PFS. Multifocality or multicentricity was the most influential feature, followed by restricted diffusion. RRS was significantly associated with OS and PFS (P <.001), as well as age and molecular subtype. The multivariate model with RRS demonstrated a significantly higher predictive performance than the model without (C-index difference: 0.074, 95% confidence interval [CI]: 0.031, 0.148 for OS; C-index difference: 0.054, 95% CI: 0.014, 0.123 for PFS). Conclusion: RRS derived from VASARI features was an independent predictor of survival in patients with anaplastic gliomas. The addition of RRS significantly improved the predictive performance of the molecular feature based model.
Bibliographical noteFunding Information:
Funding This study was funded from the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (Grant No. 2017R1D1A1B03030440) and the faculty research grants of the Yonsei University College of Medicine (Grant No. 6-2015-0079).
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All Science Journal Classification (ASJC) codes
- Clinical Neurology
- Cancer Research