Exploring the Inter-voxel Information in Pharmacokinetic Maps for Cervical Carcinoma Prediction

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Exploring the Inter-voxel Information in Pharmacokinetic Maps for Cervical Carcinoma Prediction
Title:
Exploring the Inter-voxel Information in Pharmacokinetic Maps for Cervical Carcinoma Prediction
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IEEE conf. EMBS 2020
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Publication Date:
21 August 2020
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Abstract:
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.
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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
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