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This article reports a new approach for the synthesis of ultrasmall iron oxide nanoparticles (NPs) conjugated with Arg-Gly-Asp (RGD)-modified dendrimers (G5.NHAc-RGD-Fe3O4 NPs) as a platform for targeted magnetic resonance (MR) imaging of C6 glioma cells. Ultrasmall Fe3O4 NPs synthesized via a solvothermal route were conjugated with RGD peptide-modified generation-5 poly(amidoamine) dendrimers (G5.NH2-RGD). The final G5.NHAc-RGD-Fe3O4 NPs were formed following the acetylation of the remaining dendrimer terminal amines. The as-prepared multifunctional Fe3O4 NPs were characterized using various techniques. The results of a cell viability assay, cell morphological observation, and hemolysis assay indicated that the G5.NHAc-RGD-Fe3O4 NPs exhibit excellent cytocompatibility and hemocompatibility over the studied concentration range. In addition, RGD conjugated onto the Fe3O4 NPs allows for the efficient targeting of the particles to C6 cells that overexpress αvβ3 receptors, which was confirmed via in vitro cell MR imaging and cellular uptake. Finally, the G5.NHAc-RGD-Fe3O4 NPs were used in the targeted MR imaging of C6 glioma cells in mice. The results obtained from the current study indicate that the developed G5.NHAc-RGD-Fe3O4 NPs offer significant potential for use as contrast agents in the targeted MR imaging of different types of tumors.

ObjectiveTo establish a radiomics signature and a nomogram model based on enhanced CT images to predict the Ki-67 index of lung cancer.MethodsFrom January 2014 to December 2018, 282 patients with lung cancer who had undergone enhanced CT scans and Ki-67 examination within 2 weeks were retrospectively enrolled and analyzed. The clinical data of the patients were collected, such as age, sex, smoking history, maximum tumor diameter and serum tumor markers. Our primary cohort was randomly divided into a training group (n=197) and a validation group (n=85) at a 7:3 ratio. A Ki-67 index ≤ 40% indicated low expression, and a Ki-67 index > 40% indicated high expression. In total, 396 radiomics features were extracted using AK software. Feature reduction and selection were performed using the lasso regression model. Logistic regression analysis was used to establish a multivariate predictive model to identify high and low Ki-67 expression in lung cancer. A nomogram integrating the radiomics score was established based on multiple logistic regression analysis. Area under the curve (AUC) was used to evaluate the prediction efficiency of the radiomics signature and nomogram.ResultsThe AUC,sensitivity, specificity and accuracy of the radiomics signature in the training and validation groups were 0.88 (95% CI: 0.82~0.93),79.2%,84.3%,81.2% and 0.86 (95% CI: 0.78~0.94),74.6%,88.1%,79.8%, respectively. A nomogram combining radiomics features and clinical risk factors (smoking history and NSE) was developed. The AUC, sensitivity, specificity and accuracy were 0.87 (95% CI: 0.80~0.95), 75.0%, 90.2% and 83.5% in the validation group, respectively.ConclusionThe radiomics signature and nomogram based on enhanced CT images provide a way to predict the Ki-67 expression level in lung cancer.

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