Predicting progression-free survival in glioblastoma with neuroimaging and machine learning.
Authors
Affiliations (10)
Affiliations (10)
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA. [email protected].
- Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. [email protected].
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA. [email protected].
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO, 63130, USA.
- Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, St. Louis, MO, 63130, USA.
- Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Brain Laser Center, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- National Center for Adaptive Neurotechnologies, 660 South Euclid Avenue Campus Box 8057, St. Louis, MO, 63110, USA.
Abstract
Glioblastoma (GBM) is the most prevalent and aggressive form of malignant glioma. Reliable estimation of progression-free survival (PFS) prior to medical intervention could strengthen clinical decision-making and improve patient care. Here, we utilize machine learning (ML) to predict PFS in GBM patients using resting state network (RSN) connectivity before medical intervention. GBM patients (N = 45, mean age 62.1 ± 10.3 years, mean PFS 9.5 ± 5.6 months, 62.2% male) were retrospectively recruited from Washington University Medical Center. All patients completed structural neuroimaging and resting-state functional MRI before surgery. Deep neural networks were trained on resting-state functional connectivity to predict PFS. Feature selection identified the 15 strongest predictive features prior to training. Sex (p = 0.0037), overall survival (p = 0.0003), MGMT promoter methylation status (p = 0.0064), presentation of weakness (p = 0.0037), and presentation of memory impairment (p = 0.045) were significantly associated with PFS. Tumor frequency and spatial correlation analyses associated dorsal attention, visual, frontal-parietal, and default mode networks with shorter PFS. Conversely, right-temporal lobe tumors were associated with better outcomes. RSN spatial maps revealed widespread alterations in association networks in GBM patients relative to controls. MRMR feature selection identified thalamic and association network connectivity, including somatomotor, ventral and dorsal attention, and default mode/parietal memory as the strongest predictors of PFS. Using leave-one-out validation, the model predicted PFS with an RMSE of 1.26 months, MAE of 1.08 months, and R² of 0.96 (p < 0.001). Our findings indicate that GBM alters functional brain organization on a widespread scale, and these global effects are informative of patient outcomes.