Physiological parameters can be estimated from
dynamic contrast enhanced magnetic resonance imaging (DCEMRI)
data using pharmacokinetic models. This work evaluates
the performance of various pharmacokinetic models through a
retrospective study on cervix cancer, including two generalized
kinetic models and three 2-compartment exchange models
(2CXMs). In the current clinical practice, region of interest
(ROI) is treated as a whole and the models are assessed by
their top pharmacokinetic parameters. We explore various
texture features extracted from pharmacokinetic parameter
maps to discover the inter-voxel relationship and demonstrate
that, for those insignificant parameters, texture features can
largely improve their discriminative power. Multi-parametric
classifiers are developed to fuse the information carried by
physiological parameters and their texture features. Assessed
merely by the top parameter, the DP (distributed parameter)
model is the best one with an area under the ROC (receiver
operating characteristic) curve (AUC) of 0.80; by combining
multiple pharmacokinetic parameters, the ExTofts model is the
winner, with an AUC of 0.837. The models with additional
texture features on the AATH (adiabatic approximation to the
tissue homogeneity) model achieves an AUC of 0.92.
Clinical Relevance - Using data from 36 cervical cancer patient
and 17 normal subjects, this work quantitatively compared
the various pharmacokinetic models and provided recommendations
for model selection in cervical cancer diagnosis.
License type:
http://creativecommons.org/licenses/by-nc-sa/4.0/
Funding Info:
This research is supported by the Agency for Science, Technology and Research, Singapore (A*STAR). under grant number ETPL/17-GAP020-R20H