RT Journal Article
SR Electronic
T1 Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 675
OP 681
DO 10.3174/ajnr.A7488
VO 43
IS 5
A1 E. George
A1 E. Flagg
A1 K. Chang
A1 H.X. Bai
A1 H.J. Aerts
A1 M. Vallières
A1 D.A. Reardon
A1 R.Y. Huang
YR 2022
UL http://www.ajnr.org/content/43/5/675.abstract
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