PT  - JOURNAL ARTICLE
AU  - George, E.
AU  - Flagg, E.
AU  - Chang, K.
AU  - Bai, H.X.
AU  - Aerts, H.J.
AU  - Vallières, M.
AU  - Reardon, D.A.
AU  - Huang, R.Y.
TI  - Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma
AID  - 10.3174/ajnr.A7488
DP  - 2022 Apr 28
TA  - American Journal of Neuroradiology
4099  - http://www.ajnr.org/content/early/2022/04/28/ajnr.A7488.short
4100  - http://www.ajnr.org/content/early/2022/04/28/ajnr.A7488.full
AB  - BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points.RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715).CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.AUCarea under the curveC-indexconcordance indexETenhancing tumorIQRinterquartile rangeOSoverall survivalPFSprogression-free survivalPD-1programmed cell death protein 1PD-L1programmed death-ligand 1WTwhole tumor