Characterization of the Response of 9L and U-251N Orthotopic Brain Tumors to 3D Conformal Radiation Therapy

Valadie, O. Grahm; Brown, Stephen L.; Farmer, Katelynn; Nagaraja, Tavarekere N.; Cabral, Glauber; Shadaia, Sheldon; Divine, George; Knight, Robert A.; Lee, Ian Y.; Dolan, Jennifer L.; Rusu, Sam; Joiner, Michael C.; Ewing, James R.

doi:10.1667/rade-22-00048.1

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…2.1 Imaging and animal population: Using previously published methods 4 , sixty-six athymic RNU rats were implanted with human U-251N cancer cells to form orthotopic gliomas. A DCE-MRI study (Dual-Echo Gradient-Echo -DGE -400 acquisitions, with 1.55s apart) tail-vein, with contrast injection (Magnevist 0.25 mmol/kg at undiluted concentration no flush) ~ 60 sec after the start of the experiment Two T1 mapping sequences (TOMROP, T-One-by-Multiple-Read-Out-Pulses) were acquired from all rat brains 5 before and after the DCE-MRI experiment.…”

Section: -Methodsmentioning

confidence: 99%

Probabilistic Nested Model Selection in Pharmacokinetic Analysis of DCE-MRI Data in Animal Model of Cerebral Tumor

Bagher-Ebadian,

Brown,

Ghassemi

et al. 2024

Preprint

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Purpose Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel’s CA time-trace. This study introduces an unsupervised feature engineering technique (Kohonen-Self-Organizing-Map (K-SOM)) to estimate the voxel-wise probability of each nested model. Methods Sixty-six immune-compromised-RNU rats were implanted with human U-251N cancer cells, and DCE-MRI data were acquired from all the rat brains. The time-trace of change in the longitudinal-relaxivity (ΔR1) for all animals’ brain voxels was calculated. DCE-MRI pharmacokinetic (PK) analysis was performed using NMS to estimate three model regions: Model-1: normal vasculature without leakage, Model-2: tumor tissues with leakage without back-flux to the vasculature, Model-3: tumor vessels with leakage and back-flux. Approximately two hundred thirty thousand (229,314) normalized ΔR1 profiles of animals’ brain voxels along with their NMS results were used to build a K-SOM (topology-size: 8x8, with competitive-learning algorithm) and probability map of each model. K-fold nested-cross-validation (NCV, k = 10) was used to evaluate the performance of the K-SOM probabilistic-NMS (PNMS) technique against the NMS technique. Results The K-SOM PNMS’s estimation for the leaky tumor regions were strongly similar (Dice-Similarity-Coefficient, DSC = 0.774 [CI: 0.731–0.823], and 0.866 [CI: 0.828–0.912] for Models 2 and 3, respectively) to their respective NMS regions. The mean-percent-differences (MPDs, NCV, k = 10) for the estimated permeability parameters by the two techniques were: -28%, + 18%, and + 24%, for vp, Ktrans, and ve, respectively. The KSOM-PNMS technique produced microvasculature parameters and NMS regions less impacted by the arterial-input-function dispersion effect. Conclusion This study introduces an unsupervised model-averaging technique (K-SOM) to estimate the contribution of different nested-models in PK analysis and provides a faster estimate of permeability parameters.

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Section: -Methodsmentioning

confidence: 99%