Abstract
189 Objectives: As one of the elegant examples of DNA nanostructures, DNA tetrahedron nanoparticle (DTN) has been used for biosensing, imaging, and treatment of cancer. To facilitate potential biomedical application of DTN, fundamental understanding of the biological properties of DTN in animals becomes increasingly important. In this study, we aim to investigate the biodistribution and pharmacokinetics of DTN in animals and apply DTN for kidney function evaluation in healthy mice and mice with unilateral ureteral obstruction (UUO). Methods: DTN with 4 side-arms was assembled through a one-step annealing process and characterized using gel electrophoresis. After hybridization with Dylight755-ssDNA or 64Cu-NOTA-ssDNA, Dylight755-DTN or 64Cu-DTN was then applied for fluorescent imaging and dynamic PET imaging of healthy mice. A murine model of UUO was prepared by surgically ligating the left ureter. Dynamic PET imaging of UUO mice using 64Cu-DTN was performed to not only validate but also quantify the renal clearance and biodistribution of DTN in vivo. Results: Dylight755-DTN and 64Cu-DTN could be well prepared and characterized. Fluorescent imaging revealed the efficient renal clearance of DTN in healthy mice, and dynamic PET imaging confirmed that a majority of DTN undertook urine excretion and accumulated in the bladder. Dynamic PET imaging of UUO mice presented distinct uptake patterns and transportation kinetics of DTN in the obstructed and contralateral kidneys. After analyzing the time-activity curve of kidneys based on PET imaging, we were able to systematically compare the renal perfusion and excretion speed of kidneys in healthy mice and mice with UUO in a non-invasive manner. Conclusion: With Dylight755-DTN for fluorescent imaging and 64Cu-DTN for PET imaging, real-time and quantitative biological understanding of DTN in animals was established. Efficient renal clearance of DTN further enabled kidney function evaluation of mice with unilateral ureteral obstruction, allowing for the correlate renal dysfunction with the changes of the renal time-activity curve in vivo.
Highlights
A first proof-of-concept study dedicated to the prediction of tumor outcomes using PET radiomics-based multivariable models built via machine learning was published in 2009 [5]
In the context of precision oncology, the radiomics workflow for the construction of predictive or prognostic models consists of 3 major steps (Fig. 1A): medical image acquisition, computation of radiomics features, and statistical analysis and machine learning
Other major issues include the limited number of patients available for radiomics research, the high false-positive rates, and the reporting of overly optimistic results, all of which affect the generalizability of the conclusions reached in current studies
Summary
The development of machine-learning techniques and the rise of computational power allow for the exploitation of a large number of quantitative features [4] This ability has led to a new incarnation of computer-aided diagnosis, “radiomics,” which refers to the characterization of tumor phenotypes via the extraction of highdimensional mineable data—for example, morphologic, intensitybased, fractal-based, and textural features—from medical images and whose subsequent analysis aims at supporting clinical decision making. Medical imaging journals are currently overwhelmed by a large volume of radiomics-related articles of variable quality and associated clinical value The aim of this editorial is to present guidelines that we think can improve the reporting quality and the reproducibility of radiomics studies, as well as the statistical quality of radiomics analyses. These guidelines can serve the authors of such studies and the reviewers who assess their appropriateness for publication
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