Machine Learning Improves Classification of Preclinical Models of Pancreatic Cancer with Chemical Exchange Saturation Transfer MRI - Goldenberg, Joshua M. (Magn Reson Med, 2018)
PURPOSE: We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T(1) relaxation, Chemical Exchange Saturation Transfer (CEST), and dynamic contrast-enhanced (DCE) MRI. METHODS: The T(1) relaxation time constants, % CEST at five saturation frequencies, and vascular permeability constants from DCE MRI were measured from Hs 766T, MIA PaCa-2 and SU.86.86 pancreatic tumor models. We used each of these measurements as predictors for machine learning classifier algorithms. We also used principal component analysis (PCA) to reduce the dimensionality of entire CEST spectra and DCE signal evolutions, which were then analyzed using classification methods. RESULTS: The T(1) relaxation time constants, % CEST amplitudes at specific saturation frequencies, and the relative K(trans) and k(ep) values from DCE MRI could not classify all three tumor types. PCA was used to analyze entire CEST spectra, which predicted the correct tumor model with 87.5% accuracy. However, the AUC from DCE signal evolutions could classify each tumor type. PCA was used to analyze the entire CEST spectrum and DCE signal evolutions, which predicted the correct tumor model with 87.5% and 85.1% accuracy, respectively. CONCLUSIONS: Machine learning applied to the entire CEST spectrum improved the classification of the three tumor models, relative to classifications that used % CEST values at single saturation frequencies. A similar improvement was not attained with machine learning applied to T(1) relaxation times or DCE signal evolutions, relative to more simplistic analysis methods.