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Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI.

ABSTRACT: 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 T1 relaxation, CEST, and DCE MRI. METHODS:The T1 relaxation time constants, % CEST at five saturation frequencies, and vascular permeability constants from DCE MRI were measured from Hs 766 T, 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 to reduce the dimensionality of entire CEST spectra and DCE signal evolutions, which were then analyzed using classification methods. RESULTS:The T1 relaxation time constants, % CEST amplitudes at specific saturation frequencies, and the relative Ktrans and kep values from DCE MRI could not classify all three tumor types. However, the area under the curve from DCE signal evolutions could classify each tumor type. Principal component analysis 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 T1 relaxation times or DCE signal evolutions, relative to more simplistic analysis methods.

SUBMITTER: Goldenberg JM

PROVIDER: S-EPMC6258293 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI.

Goldenberg Joshua M JM Cárdenas-Rodríguez Julio J Pagel Mark D MD

Magnetic resonance in medicine 20180917 1

<h4>Purpose</h4>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<sub>1</sub> relaxation, CEST, and DCE MRI.<h4>Methods</h4>The T<sub>1</sub> relaxation time constants, % CEST at five saturation frequencies, and vascular permeability constants from DCE MRI were measured from Hs 766 T, MIA PaCa-2, and SU.86.86 pancreatic tumor models. We used each of these mea ...[more]

PMID: 30277270

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Project description:Chemical exchange saturation transfer (CEST) has emerged as a novel MRI contrast mechanism that is well suited for molecular imaging studies. This new mechanism can be used to detect small amounts of contrast agent through the saturation of rapidly exchanging protons on these agents, allowing a wide range of applications. CEST technology has a number of indispensable features, such as the possibility of simultaneous detection of multiple 'colors' of agents and of changes in their environment (e.g. pH, metabolites, etc.) through MR contrast. Currently, a large number of new imaging schemes and techniques are being developed to improve the temporal resolution and specificity and to correct for the influence of B0 and B1 inhomogeneities. In this review, the techniques developed over the last decade are summarized with the different imaging strategies and post-processing methods discussed from a practical point of view, including the description of their relative merits for the detection of CEST agents. The goal of the present work is to provide the reader with a fundamental understanding of the techniques developed, and to provide guidance to help refine future applications of this technology. This review is organized into three main sections ('Basics of CEST contrast', 'Implementation' and 'Post-processing'), and also includes a brief Introduction and Summary. The 'Basics of CEST contrast' section contains a description of the relevant background theory for saturation transfer and frequency-labeled transfer, and a brief discussion of methods to determine exchange rates. The 'Implementation' section contains a description of the practical considerations in conducting CEST MRI studies, including the choice of magnetic field, pulse sequence, saturation pulse, imaging scheme, and strategies to separate magnetization transfer and CEST. The 'Post-processing' section contains a description of the typical image processing employed for B0 /B1 correction, Z-spectral interpolation, frequency-selective detection and improvement of CEST contrast maps.

Project description:ObjectiveChemical exchange-dependent saturation transfer (CEST) MRI is sensitive for detecting solid-like proteins and may detect changes in the levels of mobile proteins and peptides in tissues. The objective of this study was to evaluate the characteristics of chemical exchange proton pools using the CEST MRI technique in patients with dementia.Materials and methodsOur institutional review board approved this cross-sectional prospective study and informed consent was obtained from all participants. This study included 41 subjects (19 with dementia and 22 without dementia). Complete CEST data of the brain were obtained using a three-dimensional gradient and spin-echo sequence to map CEST indices, such as amide, amine, hydroxyl, and magnetization transfer ratio asymmetry (MTRasym) values, using six-pool Lorentzian fitting. Statistical analyses of CEST indices were performed to evaluate group comparisons, their correlations with gray matter volume (GMV) and Mini-Mental State Examination (MMSE) scores, and receiver operating characteristic (ROC) curves.ResultsAmine signals (0.029 for non-dementia, 0.046 for dementia, p = 0.011 at hippocampus) and MTRasym values at 3 ppm (0.748 for non-dementia, 1.138 for dementia, p = 0.022 at hippocampus), and 3.5 ppm (0.463 for non-dementia, 0.875 for dementia, p = 0.029 at hippocampus) were significantly higher in the dementia group than in the non-dementia group. Most CEST indices were not significantly correlated with GMV; however, except amide, most indices were significantly correlated with the MMSE scores. The classification power of most CEST indices was lower than that of GMV but adding one of the CEST indices in GMV improved the classification between the subject groups. The largest improvement was seen in the MTRasym values at 2 ppm in the anterior cingulate (area under the ROC curve = 0.981), with a sensitivity of 100 and a specificity of 90.91.ConclusionCEST MRI potentially allows noninvasive image alterations in the Alzheimer's disease brain without injecting isotopes for monitoring different disease states and may provide a new imaging biomarker in the future.

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Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI.

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Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI.

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