Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor

• Published in: Scientific reports 2025-01, Vol.15 (1), p.1786-14, Article 1786
• Main Authors: Bagher-Ebadian, Hassan, Brown, Stephen L., Ghassemi, Mohammad M., Acharya, Prabhu C., Chetty, Indrin J., Movsas, Benjamin, Ewing, James R., Thind, Kundan
• Format: Article
• Language: English
• Subjects: 631/67/2321; 639/705; 639/766/747; Animal models; Animals; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - metabolism; Brain Neoplasms - pathology; Cell Line, Tumor; Contrast Media - pharmacokinetics; Disease Models, Animal; Dynamic Contrast Enhanced MRI; Humanities and Social Sciences; Humans; Leakage; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Microvasculature; Model Averaging; Models, Statistical; multidisciplinary; Nested Model Selection; Permeability; Pharmacokinetics; Physiological Tissue Characterization; Probability learning; Rats; Science; Science (multidisciplinary); Topology; Tumors; Unsupervised Kohonen Self Organizing Map
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-83306-6

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. Sixty-six immune-compromised-RNU rats were implanted with human U-251 N cancer cells, and DCE-MRI data were acquired from all the rat brains. The time-trace of change in the longitudinal-relaxivity (ΔR 1 ) 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 ΔR 1 profiles of animals’ brain voxels along with their NMS results were used to build a K-SOM (topology-size: 8 × 8, 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. 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 v p , K trans , and v e , respectively. The KSOM-PNMS technique produced microvasculature parameters and NMS regions less impacted by the arterial-input-function dispersion effect. 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.