Disposable point-of-care portable perfusion phantom for quantitative DCE-MRI.

PURPOSE: To develop a disposable point-of-care portable perfusion phantom (DP4) and validate its clinical utility in a multi-institutional setting for quantitative dynamic contrast-enhanced magnetic resonance imaging (qDCE-MRI). METHODS: The DP4 phantom was designed for single-use and imaged concurrently with a human subject so that the phantom data can be utilized as the reference to detect errors in qDCE-MRI measurement of human tissues. The change of contrast-agent concentration in the phantom was measured using liquid chromatography-mass spectrometry. The repeatability of the contrast enhancement curve (CEC) was assessed with five phantoms in a single MRI scanner. Five healthy human subjects were recruited to evaluate the reproducibility of qDCE-MRI measurements. Each subject was imaged concurrently with the DP4 phantom at two institutes using three 3T MRI scanners from three different vendors. Pharmacokinetic (PK) parameters in the regions of liver, spleen, pancreas, and paravertebral muscle were calculated based on the Tofts model (TM), extended Tofts model (ETM), and shutter speed model (SSM). The reproducibility of each PK parameter over three measurements was evaluated with the intraclass correlation coefficient (ICC) and compared before and after DP4-based error correction. RESULTS: The contrast-agent concentration in the DP4 phantom was linearly increased over 10 min (0.17 mM/min, measurement accuracy: 96%) after injecting gadoteridol (100 mM) at a constant rate (0.24 ml/s, 4 ml). The repeatability of the CEC within the phantom was 0.997 when assessed by the ICC. The reproducibility of the volume transfer constant, Ktrans , was the highest of the PK parameters regardless of the PK models. The ICCs of Ktrans in the TM, ETM, and SSM before DP4-based error correction were 0.34, 0.39, and 0.72, respectively, while those increased to 0.93, 0.98, and 0.86, respectively, after correction. CONCLUSIONS: The DP4 phantom is reliable, portable, and capable of significantly improving the reproducibility of qDCE-MRI measurements.

Mechanical strain induces phenotypic changes in breast cancer cells and promotes immunosuppression in the tumor microenvironment.

Breast cancer (BCa) proliferates within a complex, three-dimensional microenvironment amid heterogeneous biochemical and biophysical cues. Understanding how mechanical forces within the tumor microenvironment (TME) regulate BCa phenotype is of great interest. We demonstrate that mechanical strain enhanced the proliferation and migration of both estrogen receptor+ and triple-negative (TNBC) human and mouse BCa cells. Furthermore, a critical role for exosomes derived from cells subjected to mechanical strain in these pro-tumorigenic effects was identified. Exosome production by TNBC cells increased upon exposure to oscillatory strain (OS), which correlated with elevated cell proliferation. Using a syngeneic, orthotopic mouse model of TNBC, we identified that preconditioning BCa cells with OS significantly increased tumor growth and myeloid-derived suppressor cells (MDSCs) and M2 macrophages in the TME. This pro-tumorigenic myeloid cell enrichment also correlated with a decrease in CD8+ T cells. An increase in PD-L1+ exosome release from BCa cells following OS supported additive T cell inhibitory functions in the TME. The role of exosomes in MDSC and M2 macrophage was confirmed in vivo by cytotracking fluorescent exosomes, derived from labeled 4T1.2 cells, preconditioned with OS. In addition, in vivo internalization and intratumoral localization of tumor-cell derived exosomes was observed within MDSCs, M2 macrophages, and CD45-negative cell populations following direct injection of fluorescently-labeled exosomes. Our data demonstrate that exposure to mechanical strain promotes invasive and pro-tumorigenic phenotypes in BCa cells, indicating that mechanical strain can impact the growth and proliferation of cancer cell, alter exosome production by BCa, and induce immunosuppression in the TME by dampening anti-tumor immunity.

Assessment of Surgical Effects on Patients with Obstructive Sleep Apnea Syndrome Using Computational Fluid Dynamics Simulations.

Obstructive sleep apnea syndrome is one of the most common sleep disorders. To treat patients with this health problem, it is important to detect the severity of this syndrome and occlusion sites in each patient. The goal of this study is to test the hypothesis that the cure of obstructive sleep apnea syndrome by maxillomandibular advancement surgery can be predicted by analyzing the effect of anatomical airway changes on the pressure effort required for normal breathing using a high-fidelity, 3-D numerical model. The employed numerical model consists of: 1) 3-D upper airway geometry construction from patient-specific computed tomographic scans using an image segmentation technique, 2) mixed-element mesh generation of the numerically constructed airway geometry for discretizing the domain of interest, and 3) computational fluid dynamics simulations for predicting the flow field within the airway and the degree of severity of breathing obstruction. In the present study, both laminar and turbulent flow simulations were performed to predict the flow field in the upper airway of the selected patients before and after maxillomandibular advancement surgery. Patients of different body mass indices were also studied to assess their effects. The numerical results were analyzed to evaluate the pressure gradient along the upper airway. The magnitude of the pressure gradient is regarded as the pressure effort required for breathing, and the extent of reduction of the pressure effort is taken to measure the success of the surgery. The description of the employed numerical model, numerical results from simulations of various patients, and suggestion for future work are detailed in this paper.

Patient-Specific Geometry Modeling and Mesh Generation for Simulating Obstructive Sleep Apnea Syndrome Cases by Maxillomandibular Advancement.

The objective of this paper is the reconstruction of upper airway geometric models as hybrid meshes from clinically used Computed Tomography (CT) data sets in order to understand the dynamics and behaviors of the pre- and postoperative upper airway systems of Obstructive Sleep Apnea Syndrome (OSAS) patients by viscous Computational Fluid Dynamics (CFD) simulations. The selection criteria for OSAS cases studied are discussed because two reasonable pre- and postoperative upper airway models for CFD simulations may not be created for every case without a special protocol for CT scanning. The geometry extraction and manipulation methods are presented with technical barriers that must be overcome so that they can be used along with computational simulation software as a daily clinical evaluation tool. Eight cases are presented in this paper, and each case consists of pre- and postoperative configurations. The results of computational simulations of two cases are included in this paper as demonstration.