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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 longitudinalrelaxivity Δ R 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 Δ R 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. 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 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. 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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Mechanical stress and fluid flow influence glioma cell phenotype in vitro, but measuring these quantities in vivo continues to be challenging. The purpose of this study was to predict these quantities in vivo, thus providing insight into glioma physiology and potential mechanical biomarkers that may improve glioma detection, diagnosis, and treatment. Image-based finite element models of human U251N orthotopic glioma in athymic rats were developed to predict structural stress and interstitial flow in and around each animal's tumor. In addition to accounting for structural stress caused by tumor growth, our approach has the advantage of capturing fluid pressure-induced structural stress, which was informed by in vivo interstitial fluid pressure (IFP) measurements. Because gliomas and the brain are soft, elevated IFP contributed substantially to tumor structural stress, even inverting this stress from compressive to tensile in the most compliant cases. The combination of tumor growth and elevated IFP resulted in a concentration of structural stress near the tumor boundary where it has the greatest potential to influence cell proliferation and invasion. MRI-derived anatomical geometries and tissue property distributions resulted in heterogeneous interstitial fluid flow with local maxima near cerebrospinal fluid spaces, which may promote tumor invasion and hinder drug delivery. In addition, predicted structural stress and interstitial flow varied markedly between irradiated and radiation-naïve animals. Our modeling suggests that relative to tumors in stiffer tissues, gliomas experience unusual mechanical conditions with potentially important biological (e.g., proliferation and invasion) and clinical consequences (e.g., drug delivery and treatment monitoring).
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PURPOSE: This multicenter phase II basket trial investigated the efficacy, safety and pharmacokinetics of Debio 1347, an investigational, oral, highly selective, ATP-competitive, small molecule inhibitor of FGFR1-3, in patients with solid tumors harboring a functional FGFR1-3 fusion. PATIENTS AND METHODS: Eligible adults had a previously treated locally advanced (unresectable) or metastatic biliary tract (cohort 1), urothelial (cohort 2) or other histologic cancer type (cohort 3). Debio 1347 was administered at 80 mg once daily, continuously, in 28-day cycles. The primary endpoint was the objective response rate (ORR). Secondary endpoints included duration of response, progression-free survival, overall survival, pharmacokinetics, and incidence of adverse events. RESULTS: Between March 22, 2019 and January 8, 2020, 63 patients were enrolled and treated, 30 in cohort 1, four in cohort 2, and 29 in cohort 3. An unplanned preliminary statistical review showed that the efficacy of Debio 1347 was lower than predicted and the trial was terminated. Three of 58 evaluable patients had partial responses, representing an ORR of 5%, with a further 26 (45%) having stable disease (≥6 weeks duration). Grade ≥3 treatment-related adverse events occurred in 22 (35%) of 63 patients, with the most common being hyperphosphatemia (13%) and stomatitis (5%). Two patients (3%) discontinued treatment due to adverse events. CONCLUSIONS: Debio 1347 had manageable toxicity; however, the efficacy in patients with tumors harboring FGFR fusions did not support further clinical evaluation in this setting. Our transcriptomic-based analysis characterized in detail the incidence and nature of FGFR fusions across solid tumors.