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 (λ) 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 λ (in Hounsfield units per kiloelectron-volt) was the best single parameter for the detection of metastatic SLNs. The accuracy of the venous phase λ 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 λ 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. © RSNA, 2018 Online supplemental material is available for this article.

2 J. Magn. Reson. Imaging 2017;45:855-862.

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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.

A major challenge in stroke treatment is the restoration of neural circuit in which neuron function plays a central role. Although transplantation of exogenous neural stem cells (NSCs) is admittedly a promising therapeutical means, the treatment outcome is greatly affected due to the poor NSCs differentiation into neurons caused by myelin associated inhibitory factors binding to Nogo-66 receptor (NgR). Herein, a nanoscale polymersome is developed to codeliver superparamagnetic iron oxide nanoparticles and siRNA targeting NgR gene (siNgR) into NSCs. This multifunctional nanomedicine directs neuronal differentiation of NSCs through silencing the NgR gene and meanwhile allows a noninvasive monitoring of NSC migration with magnetic resonance imaging. An improved recovery of neural function is achieved in rat ischemic stroke model. The results demonstrate the great potential of the multifunctional siRNA nanomedicine in stroke treatment based on stem cell transplantation.

Objective Shear-wave elastography (SWE) can improve the diagnostic specificity of the B-model ultrasonography (US) in breast cancer. However, whether deep learning-based radiomics signatures based on the B-mode US (B-US-RS) or SWE (SWE-RS) could further improve the diagnostic performance remains to be investigated. We aimed to develop the B-US-RS and SWE-RS and determine their performances in classifying breast masses. Materials and Methods This retrospective study included 291 women (mean age ± standard deviation, 40.9 ± 12.3 years) from two centers who had US-visible solid breast masses and underwent biopsy and/or surgical resection between June 2015 and July 2017. B-mode US and SWE images of the 198 masses in 198 patients (training cohort) from center 1 were segmented, respectively, to construct B-US-RS and SWE-RS using the least absolute shrinkage and selection operator regression and tested in an independent validation cohort of 65 masses in 65 patients from center 1 and in an external validation cohort of 28 masses in 28 patients from center 2. The performances of B-US-RS and SWE-RS were assessed using receiver operating characteristic (ROC) analysis and compared with that of radiologist assessment [Breast Imaging Reporting and Data System (BI-RADS)] and quantitative SWE parameters [maximum elasticity ( E max ), mean elasticity ( E mean ), elasticity ratio ( E ratio ), and elastic modulus standard deviation ( E SD )] by using the McNemar test. Results The single best-performing quantitative SWE parameter, E max , had a higher specificity than BI-RADS assessment in the training and independent validation cohorts ( P < 0.001 for both). The areas under the ROC curves (AUCs) of B-US-RS and SWE-RS both were 0.99 (95% CI = 0.99–1.00) in the training cohort, 1.00 (95% CI = 1.00–1.00) in the independent validation cohort, and 1.00 (95% CI = 1.00–1.00) in the external validation cohort. The specificities of B-US-RS and SWE-RS were higher than that of E max in the training ( P < 0.001 for both) and independent validation cohorts ( P = 0.02 for both). Conclusion The B-US-RS and SWE-RS outperformed the quantitative SWE parameters and BI-RADS assessment for classifying breast masses. The integration of the deep learning-based radiomics approach would help improve the classification ability of B-mode US and SWE for breast masses.

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• qDCE-MRI parameters are useful for discriminating between malignant and benign breast lesions. • K , K and 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.