Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.

Journal: AJNR. American journal of neuroradiology
PMID: 39542725

Abstract

This project aimed to develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center. We retrospectively analyzed data from 634 patients treated for glioblastoma at a single brain tumor center over a 5-year period (2017-2021). The mean age was 56 ± 13 years. 372/634 (59%) patients were male, and 262/634 (41%) patients were female. Study data consisted of 3,403 brain MRI exams and corresponding standardized, radiologist-based brain tumor response assessments (BT-RADS). An artificial intelligence (AI)-based brain tumor response assessment (AI-VTRA) algorithm was developed using automated, volumetric tumor segmentation. AI-VTRA results were evaluated for agreement with radiologist-based response assessments and ability to stratify patients by overall survival. Metrics were computed to assess the agreement using BT-RADS as the ground-truth, fixed-time point survival analysis was conducted to evaluate the survival stratification, and associated P-values were calculated. For all BT-RADS categories, AI-VTRA showed moderate agreement with radiologist response assessments (F1 = 0.587-0.755). Kaplan-Meier survival analysis revealed statistically worse overall fixed time point survival for patients assessed as image worsening equivalent to RANO progression by human alone compared to by AI alone (log-rank = .007). Cox proportional hazard model analysis showed a disadvantage to AI-based assessments for overall survival prediction ( = .012). In summary, our proposed AI-VTRA, following BT-RADS criteria, yielded moderate agreement for replicating human response assessments and slightly worse stratification by overall survival.

Authors

  • Jikai Zhang
    Department of Biostatistics and Bioinformatics (J.Z., R.H.), Duke University School of Medicine, Durham, NC.
  • Dominic Labella
    State University of New York-Upstate Medical Center, Syracuse, NY, USA.
  • Dylan Zhang
    Departments of Radiology (D.Z., J.L.H., M.A.M., E.C.), Duke University Medical Center, Durham, North Carolina.
  • Jessica L Houk
    Departments of Radiology (D.Z., J.L.H., M.A.M., E.C.), Duke University Medical Center, Durham, North Carolina.
  • Jeffrey D Rudie
    Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., L.X., A.K., J.M.E., T.C., I.M.N., S.M., J.C.G.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (J.D.R., A.M.R.); Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pa (X.L., J.W.); University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (M.T.D.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); Department of Radiology, University of Texas, Austin, Tex (R.N.B.); and Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pa (I.M.N.).
  • Haotian Zou
    Department of Biostatistics and Bioinformatics (H.Z., M.A.M.), Duke University School of Medicine, Durham, North Carolina.
  • Pranav Warman
    Caire Health, Tampa, Florida, USA.
  • Maciej A Mazurowski
    Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA.
  • Evan Calabrese
    Department of Radiology and Biomedical Imaging, University of California At San Francisco, 350 Parnassus Ave, Suite 307H, San Francisco, CA, 94143-0628, USA. evan.calabrese@ucsf.edu.

Keywords

External Resources

Popular Topics
Recent Journals