Chushan Zheng

Superparamagnetic Iron Oxide Nanoparticles-Complexed Cationic Amylose for In Vivo Magnetic Resonance Imaging Tracking of Transplanted Stem Cells in Stroke

Cell-based therapy with mesenchymal stem cells (MSCs) is a promising strategy for acute ischemic stroke. In vivo tracking of therapeutic stem cells with magnetic resonance imaging (MRI) is imperative for better understanding cellular survival and migrational dynamics over time. In this study, we develop a novel biocompatible nanocomplex (ASP-SPIONs) based on cationic amylose, by introducing spermine and the image label, ultrasmall superparamagnetic iron oxide nanoparticles (SPIONs), to label MSCs. The capacity, efficiency, and cytotoxicity of the nanocomplex in transferring SPIONs into green fluorescence protein-modified MSCs were tested; and the performance of in vivo MRI tracking of the transplanted cells in acute ischemic stroke was determined. The results demonstrated that the new class of SPIONs-complexed nanoparticles based on biodegradable amylose can serve as a highly effective and safe carrier to transfer magnetic label into stem cells. A reliable tracking of transplanted stem cells in stroke was achieved by MRI up to 6 weeks, with the desirable therapeutic benefit of stem cells on stroke retained. With the advantages of a relatively low SPIONs concentration and a short labeling period, the biocompatible complex of cationic amylose with SPIONs is highly translatable for clinical application. It holds great promise in efficient, rapid, and safe labeling of stem cells for subsequent cellular MRI tracking in regenerative medicine.

DTI metrics can be used as biomarkers to determine the therapeutic effect of stem cells in acute peripheral nerve injury

To determine the role of diffusion tensor imaging (DTI) metrics as biomarkers for the therapeutic effects of mesenchymal stem cells (MSCs) in acute peripheral nerve injury.

Assessment of the synergic effect of immunomodulation on nerve repair using multiparametric magnetic resonance imaging

The immune system plays a pivotal role in nerve injury. The aim of this study was to determine the role of multiparametric magnetic resonance imaging (MRI) in evaluation of the synergic effect of immunomodulation on nerve regeneration in neurotmesis.

Monitoring of macrophage recruitment enhanced by Toll-like receptor 4 activation with MR imaging in nerve injury: MRI of Enhanced Macrophage Recruitment in Nerve Injury

Introduction: Macrophage recruitment is critical for nerve regeneration after an injury. The aim of this study was to investigate whether ultrasmall superparamagnetic iron oxide (USPIO) nanoparticle‐based MRI could be used to monitor the enhanced macrophage recruitment by Toll‐like receptor 4 (TLR4) activation in nerve injury. Methods: Rats received intraperitoneal injections of either lipopolysaccharide (LPS) or phosphate buffered saline (PBS) or no injection (controls) after a sciatic nerve crush injury. After intravenous injection of the USPIOs (LPS and PBS groups) or PBS (control group), MRI was performed and correlated with histological findings. Results: LPS group showed more remarkable hypointense signals on T2*‐weighted imaging and lower T2 values in the crushed nerves than PBS group. The hypointense signal areas were associated with an enhanced recruitment of iron‐loaded macrophages to the injured nerves. Discussion: USPIO‐enhanced MRI can be used to monitor the enhanced macrophage recruitment by means of TLR4 signal pathway activation in nerve injury. Muscle Nerve 58: 123–132, 2018

In vivo tracking of the tropism of mesenchymal stem cells to malignant gliomas using reporter gene‐based MR imaging

Mesenchymal stem cells (MSCs) have emerged as a promising cellular vehicle for gene therapy of malignant gliomas due to their property of tumor tropism. However, MSCs may show bidirectional and divergent effects on tumor growth. Therefore, a robust surveillance system with a capacity for noninvasive monitoring of the homing, distribution and fate of stem cells in vivo is highly desired for developing stem cell‐based gene therapies for tumors. In this study, we used ferritin gene‐based magnetic resonance imaging (MRI) to track the tumor tropism of MSCs in a rat orthotopic xenograft model of malignant glioma. MSCs were transduced with lentiviral vectors expressing ferritin heavy chain (FTH) and enhanced green fluorescent protein (eGFP). Intra‐arterial, intravenous and intertumoral injections of these FTH transgenic MSCs (FTH‐MSCs) were performed in rats bearing intracranial orthotopic C6 gliomas. The FTH‐MSCs were detected as hypointense signals on T2‐ and T2*‐weighted images on a 3.0 T clinical MRI. After intra‐arterial injection, 17% of FTH‐MSCs migrated toward the tumor and gradually diffused throughout the orthotopic glioma. This dynamic process could be tracked in vivo by MRI up to 10 days of follow‐up, as confirmed by histology. Moreover, the tumor tropism of MSCs showed no appreciable impact on the progression of the tumor. These results suggest that FTH reporter gene‐based MRI can be used to reliably track the tropism and fate of MSCs after their systemic transplantation in orthotopic gliomas. This real‐time in vivo tracking system will facilitate the future development of stem cell‐based therapies for malignant gliomas.

Discrimination between benign and malignant breast lesions using volumetric quantitative dynamic contrast-enhanced MR imaging

ObjectiveTo determine the diagnostic performance of volumetric quantitative dynamic contrast-enhanced MRI (qDCE-MRI) in differentiation between malignant and benign breast lesions.MethodsDCE-MRI was performed in 124 patients with 136 breast lesions. Quantitative pharmacokinetic parameters Ktrans, Kep, Ve, Vp and semi-quantitative parameters TTP, MaxCon, MaxSlope, AUC were obtained by using a two-compartment extended Tofts model and three-dimensional volume of interest. Morphologic features (lesion size, margin, internal enhancement pattern) and time-signal intensity curve (TIC) type were also assessed. Logistic regression analysis was used to determine predictors of malignancy, followed by receiver operating characteristics (ROC) analysis to evaluate the diagnostic performance.ResultsqDCE parameters (Ktrans, Kep, Vp, TTP, MaxCon, MaxSlope and AUC), morphological parameters and TIC type were significantly different between malignant and benign lesions (P≤0.001). Multivariate logistic regression analyses showed that Ktrans, Kep, MaxSlope, size, margin and TIC type were independent predictors of malignancy. The diagnostic accuracy of logistic models based on qDCE parameters alone, morphological features plus TIC type, and all parameters combined was 94.9%, 89.0%, and 95.6% respectively.ConclusionqDCE-MRI can be used to improve diagnostic differentiation between benign and malignant breast lesions in relation to morphology and kinetic analysis.Key Points• qDCE-MRI parameters are useful for discriminating between malignant and benign breast lesions.• Ktrans, Kepand MaxSlope were independent predictors of breast malignancy.• qDCE-MRI has a better diagnostic ability than morphology and kinetic analysis.• qDCE-MRI can be used to improve the diagnostic accuracy of breast malignancy.

Axillary Sentinel Lymph Nodes in Breast Cancer: Quantitative Evaluation at Dual-Energy CT

Purpose To evaluate the diagnostic performance of quantitative parameters derived from dual-energy CT for the preoperative diagnosis of metastatic sentinel lymph nodes (SLNs) in participants with breast cancer. Materials and Methods For this prospective study, dual-phase contrast agent-enhanced CT was performed in female participants with breast cancer from June 2015 to December 2017. Quantitative dual-energy CT parameters and morphologic parameters were compared between metastatic and nonmetastatic SLNs. The quantitative parameters were fitted to univariable and multivariable logistic regression models. The diagnostic role of morphologic and quantitative parameters was analyzed by receiver operating characteristic curves and compared by using the McNemar test. Results This study included 193 female participants (mean age, 47.6 years ± 10.1; age range, 22-79 years). Quantitative dual-energy CT parameters including slope of the spectral Hounsfield unit curve (λHu) measured at both arterial and venous phases, normalized iodine concentration at both arterial and venous phase, and normalized effective atomic number at the venous phase were higher in metastatic than in nonmetastatic SLNs (P value range, ≤.001 to .031). Univariable and multivariable logistic regression analyses showed that venous phase λHu (in Hounsfield units per kiloelectron-volt) was the best single parameter for the detection of metastatic SLNs. The accuracy of the venous phase λHu for detecting metastatic SLNs was 90.5% on a per-lymph node basis and 87.0% on a per-patient basis. The accuracy and specificity at venous phase λHu was higher than their counterparts in the morphologic parameters (P < .001). Conclusion Dual-energy CT is a complementary means for the preoperative identification of sentinel lymph nodes metastases in participants with breast cancer.

Assessment of Cardiac Lead Perforation: Comparison Among Chest Radiography, Transthoracic Echocardiography and Electrocardiography-gated Contrast-enhanced Cardiac CT

ObjectivesCardiac lead perforation is a rare but potentially life-threatening event. The purpose of this study was to investigate the diagnostic performances of chest radiography, transthoracic echocardiography (TTE) and electrocardiography (ECG)-gated contrast-enhanced cardiac CT in the assessment of cardiac lead perforation.MethodsThis retrospective study was approved by the ethics review board of Sun Yat-Sen Memorial Hospital at Sun Yat-Sen University (Guangzhou, China), and the need to obtain informed consent was waived. Between May 2010 and Oct 2017, 52 patients were clinically suspected to have a cardiac lead perforation and received chest radiography, TTE and ECG-gated contrast-enhanced cardiac CT. Among them, 13 patients were identified as having cardiac lead perforation. The diagnostic performances of these three modalities were evaluated by receiver-operating characteristic (ROC) curves using a composite reference standard of surgical and electrophysiological results and clinical follow-up. The areas under ROCs (AUROCs) were compared with the McNemar test.ResultsThe accuracies of chest radiography, TTE and ECG-gated contrast-enhanced cardiac CT imaging for the diagnosis of cardiac lead perforation were 73.1%, 82.7% and 98.1%, respectively. ECG-gated contrast-enhanced cardiac CT had a higher AUROC than chest radiography (p < 0.001) and TTE (p < 0.001).ConclusionsECG-gated contrast-enhanced cardiac CT is superior to both chest radiography and TTE imaging for the assessment of cardiac lead perforation.Key Points• ECG-gated contrast-enhanced cardiac CT has an accuracy of 98.1% in the diagnosis of cardiac lead perforation.• The AUROC of ECG-gated contrast-enhanced cardiac CT is higher than those of chest radiography and TTE imaging.• ECG-gated contrast-enhanced cardiac CT imaging has better diagnostic performance than both chest radiography and TTE imaging for the assessment of cardiac lead perforation.

Metastatic breast cancer: characterization of axillary sentinel lymph node (SLN) on the preoperative spectral CT.

Axillary lymph node (ALN) status is a prognostic factor for patients with breast cancer. Metastasis of sentinel lymph node (SLN) indicates ALN involvement. In this study, our purpose is to develop a quantitative approach in characterizing the metastasis of ALN on spectral CT using the largest SLN (LSLN) as the surrogate. With IRB approval, a data set of 185 patients with breast cancer was retrospectively collect at Sun Yat-Sen Memorial Hospital in Guangzhou, China. Each patient underwent a preoperative spectral CT scan. A chest and axillary dual-phasic contrast media enhanced scan were acquired with a GE Discovery CT750HD CT scanner while the patient was in supine position. The LSLN was manually identified by radiologists for quantitative image analysis. We used a total of 6 sets of dual-phasic scans including 40 keV monochromatic images, 70 keV monochromatic images, and gemstone spectral images obtained at arterial and venous phases. 82 patients were positive to biopsy-proven cancer metastasis and the remaining 103 were negative. A deep convolutional neural network (DCNN) was used to extract quantitative image features as the image representation of SLN. To assess the efficacy of quantitative image features in characterization of SLN, three machine learning classifiers including KNN, SVM, and random forest were compared. Ten-fold cross validation was used for model selection. Results indicated that the AUCs on the 6 CT images for classification of LSLN metastasis ranged from 0.71-0.78 in which the best classification were observed on 70 keV monochromatic images at arterial phase. The overall classifications in arterial phase were better than those in venous phase for low (40 keV) and mixture energy setting while the findings were reversed for high (70 keV) energy setting. Future work is underway to assess our quantitative measures in axillary staging.