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AJNR Am J Neuroradiol. 2019 Mar;40(3):418-425. doi: 10.3174/ajnr.A5981. Epub 2019 Feb 28.

Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning.

Author information

1
From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.) Hu.Leland@Mayo.Edu.
2
Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona.
3
Departments of Pathology (J.M.E.).
4
Radiology (L.C.B., A.N., J.P.K.).
5
Department of Biostatistics (A.C.D.), Mayo Clinic in Arizona, Scottsdale, Arizona.
6
Neurosurgery (K.A.S., P.N., N.S.).
7
Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.).
8
Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.).
9
Neuroimaging Research (C.Q.), Barrow Neurological Institute, Phoenix, Arizona.
10
Department of Neuro-Oncology (A.B.P.-U., M.M.M., A.S.).
11
From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).
12
H. Lee Moffitt Cancer Center and Research Institute (J.R.M.), Tampa, Florida.
13
Department of Cancer Biology (N.L.T.), Mayo Clinic in Arizona, Phoenix, Arizona.

Abstract

BACKGROUND AND PURPOSE:

MR imaging-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 patient'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.

PMID:
30819771
DOI:
10.3174/ajnr.A5981

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