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Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1

E George1, E Flagg2, K Chang3

  • 1From the Department of Radiology and Biomedical Imaging (E.G.), University of California San Francisco, San Francisco, California.

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Machine learning using MR imaging radiomics can predict survival in glioblastoma patients receiving immunotherapy. Early on-treatment imaging features accurately predict progression-free and overall survival.

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Assessing immunotherapy response in glioblastoma is difficult due to overlapping imaging signs of treatment effects and tumor progression.
  • Novel imaging biomarkers are needed to accurately predict patient outcomes.

Purpose of the Study:

  • To evaluate if MR imaging radiomics and machine learning can predict progression-free survival (PFS) and overall survival (OS) in glioblastoma patients undergoing programmed death-ligand 1 (PD-L1) inhibition immunotherapy.

Main Methods:

  • A post hoc analysis of a multicenter trial (n=113) involving durvalumab for glioblastoma.
  • Radiomics features were extracted from pre-treatment and first on-treatment MR imaging.
  • A random survival forest algorithm was trained and externally validated to predict PFS and OS.

Main Results:

  • Pre-treatment MR imaging features showed poor predictive value for PFS and OS (concordance index [CI] = 0.472–0.524).
  • First on-treatment MR imaging features demonstrated high predictive value for OS (CI = 0.692–0.750) and PFS (CI = 0.680–0.715).

Conclusions:

  • A radiomics-based machine learning model utilizing first on-treatment MR imaging can effectively predict survival in glioblastoma patients receiving PD-L1 inhibition immunotherapy.