A Knowledge-Based Artificial Intelligence (AI) to Perform Nested Model Selection From Dynamic Contrast Enhanced (DCE)-MRI Pharmacokinetic Analyses of Brain Tumors in An Animal Model
Recommended Citation
Bagher-Ebadian H, Nagaraja T, Cabral G, Farmer KG, Valadie O, Acharya P, Movsas B, Brown SL, Chetty I, Ewing J. A Knowledge-Based Artificial Intelligence (AI) to Perform Nested Model Selection From Dynamic Contrast Enhanced (DCE)-MRI Pharmacokinetic Analyses of Brain Tumors in An Animal Model. Medical Physics 2022; 49(6):e500.
Document Type
Conference Proceeding
Publication Date
7-13-2022
Publication Title
Medical Physics
Keywords
gadolinium pentetate, animal experiment, animal model, area under the curve, artificial intelligence, artificial neural network, brain region, brain tumor, cancer cell, cancer transplantation, conference abstract, controlled study, dynamic contrast-enhanced magnetic resonance imaging, entropy, glioma, male, nested cross validation, nonhuman, pharmacokinetics, pilot study, prediction, rat, Rowett nude rat, tail vein, U-251MG cell line, vascularization
Abstract
Purpose: Our group has shown that a nested model selection (NMS) technique utilizing an extended Patlak graphical method illuminates pharmacokinetic (PK) compartmental analyses of DCE-MRI data. NMS generates maps of brain regions that reflect the number of parameters needed to describe their vascular physiology based on DCE-MRI. However, identification of model choice regions requires a series of computationally intensive processing. Furthermore, prediction of model-1 region is biased by dispersion of the arterial input function (AIF). In this study, we introduce a knowledge-based adaptive model for real time prediction of MS maps from DCE-MRI raw information while minimizing AIF dispersion error. Methods: Thirty-nine immune compromised RNU rats were implanted with human U-251n cancer cells orthotopic glioma (IACUC: #1509). Sixty-six DCE-MRI studies (28 days after tumor implantation, 7T-Dual- Gradient-Echo, FOV:32x32mm2, TR/(TE1-TE2)=24ms/(2ms-4ms), flip-angle=18, 400-acquisitions/1.55sec-interval, Magnevist/tail-vein) were used to perform PK analysis using NMS to distinguish three different brain regions: Normal vasculature (Model-1: No-leakage), leaky tumor tissues with no back-flux to vasculature (Model-2), and leaky tumor tissues with back-flux (Model-3). Normalized time-traces of DCE-MRI (1st-and 2nd-echoes) for three model regions (92698 profiles/examples) were used to train (target: PK-MS results) and validate (10-fold nested cross validation, NCV) an interconnected or nested artificial neural networks (ANNs with feed forward architecture: 76:10:1, Levenberg- Marquardt optimization, loss: cross-entropy). To suppress the AIF-dispersion errors, a knowledge-based optimization was performed on the ANNs' response distributions. Results: The NCV performance (outer-loop) of the trained-ANNs for the prediction of Model-1 versus Models-2 & 3, and Model-2 versus Model-3 were: AUC/F1-Score/Balanced- Accuracy= 0.909/0.833/0.850 and 0.955/0.884/0.890, respectively. Compared to the conventional NMS analysis, the Model-1 regions predicted by the ANNs were less impacted by the AIF-dispersion effects (less miss-classification for Model-1 and 2). Conclusion: This pilot study demonstrates the use of adaptive models for PK analyses of DCE-MRI data that characterizes the vascular physiology of embedded tumors.
Volume
49
Issue
6
First Page
e500
