Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor
Abstract 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, correspon...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-83306-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832594694678249472 |
|---|---|
| author | Hassan Bagher-Ebadian Stephen L. Brown Mohammad M. Ghassemi Prabhu C. Acharya Indrin J. Chetty Benjamin Movsas James R. Ewing Kundan Thind |
| author_facet | Hassan Bagher-Ebadian Stephen L. Brown Mohammad M. Ghassemi Prabhu C. Acharya Indrin J. Chetty Benjamin Movsas James R. Ewing Kundan Thind |
| author_sort | Hassan Bagher-Ebadian |
| collection | DOAJ |
| description | Abstract 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 (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: 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 vp, Ktrans, and ve, 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. |
| format | Article |
| id | doaj-art-64a85085c4ad4d86ba6a203fdce3b8e8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-64a85085c4ad4d86ba6a203fdce3b8e82025-01-19T12:24:00ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-83306-6Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumorHassan Bagher-Ebadian0Stephen L. Brown1Mohammad M. Ghassemi2Prabhu C. Acharya3Indrin J. Chetty4Benjamin Movsas5James R. Ewing6Kundan Thind7Department of Radiation Oncology, Henry Ford HospitalDepartment of Radiation Oncology, Henry Ford HospitalDepartment of Computer Science and Engineering, Michigan State UniversityDepartment of Physics, Oakland UniversityDepartment of Physics, Oakland UniversityDepartment of Radiation Oncology, Henry Ford HospitalDepartment of Radiology, Michigan State UniversityDepartment of Radiation Oncology, Henry Ford HospitalAbstract 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 (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: 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 vp, Ktrans, and ve, 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.https://doi.org/10.1038/s41598-024-83306-6Nested Model SelectionDynamic Contrast Enhanced MRIModel AveragingUnsupervised Kohonen Self Organizing MapPhysiological Tissue Characterization |
| spellingShingle | Hassan Bagher-Ebadian Stephen L. Brown Mohammad M. Ghassemi Prabhu C. Acharya Indrin J. Chetty Benjamin Movsas James R. Ewing Kundan Thind Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor Scientific Reports Nested Model Selection Dynamic Contrast Enhanced MRI Model Averaging Unsupervised Kohonen Self Organizing Map Physiological Tissue Characterization |
| title | Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor |
| title_full | Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor |
| title_fullStr | Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor |
| title_full_unstemmed | Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor |
| title_short | Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor |
| title_sort | probabilistic nested model selection in pharmacokinetic analysis of dce mri data in animal model of cerebral tumor |
| topic | Nested Model Selection Dynamic Contrast Enhanced MRI Model Averaging Unsupervised Kohonen Self Organizing Map Physiological Tissue Characterization |
| url | https://doi.org/10.1038/s41598-024-83306-6 |
| work_keys_str_mv | AT hassanbagherebadian probabilisticnestedmodelselectioninpharmacokineticanalysisofdcemridatainanimalmodelofcerebraltumor AT stephenlbrown probabilisticnestedmodelselectioninpharmacokineticanalysisofdcemridatainanimalmodelofcerebraltumor AT mohammadmghassemi probabilisticnestedmodelselectioninpharmacokineticanalysisofdcemridatainanimalmodelofcerebraltumor AT prabhucacharya probabilisticnestedmodelselectioninpharmacokineticanalysisofdcemridatainanimalmodelofcerebraltumor AT indrinjchetty probabilisticnestedmodelselectioninpharmacokineticanalysisofdcemridatainanimalmodelofcerebraltumor AT benjaminmovsas probabilisticnestedmodelselectioninpharmacokineticanalysisofdcemridatainanimalmodelofcerebraltumor AT jamesrewing probabilisticnestedmodelselectioninpharmacokineticanalysisofdcemridatainanimalmodelofcerebraltumor AT kundanthind probabilisticnestedmodelselectioninpharmacokineticanalysisofdcemridatainanimalmodelofcerebraltumor |