Accurate Patient-Specific Machine Learning Models Of Glioblastoma Invasion Using Transfer Learning
BACKGROUND AND PURPOSE: MR imagingâ€“based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patientâ€™s own histologic data. MATERIALS AND METHODS: We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS: Tumor cell density significantly correlated with relative CBV (r 0.33, P .001), and T1-weighted postcontrast (r 0.36, P .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r 0.53, mean absolute error 15.19%) compared with one-model-fits-all (r 0.27, mean absolute error 17.79%). With multivariate modeling, transfer learning further improved performance (r 0.88, mean absolute error 5.66%) compared with one-model-fits-all (r 0.39, mean absolute error 16.55%). CONCLUSIONS: Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.
American Journal of Neuroradiology
Digital Object Identifier (DOI)
Hu, L. S.; Yoon, H.; Eschbacher, J. M.; Baxter, L. C.; Dueck, A. C.; Nespodzany, A.; Smith, K. A.; Nakaji, P.; Xu, Y.; Wang, L.; Karis, J. P.; Hawkins-Daarud, A. J.; Singleton, K. W.; Jackson, P. R.; Anderies, B. J.; Bendok, B. R.; Zimmerman, R. S.; Quarles, C.; Porter-Umphrey, A. B.; Mrugala, M. M.; Sharma, A.; Hoxworth, J. M.; Sattur, M. G.; Sanai, N.; Koulemberis, P. E.; Krishna, C.; Mitchell, J. R.; Wu, T.; Tran, N. L.; Swanson, K. R.; and Li, J., "Accurate Patient-Specific Machine Learning Models Of Glioblastoma Invasion Using Transfer Learning" (2019). Neurobiology. 347.