Implantable drug delivery systems (DDS) provide a platform for sustained release of therapeutic agents over a period of weeks to months and sometimes years. Such strategies are typically used clinically to increase patient compliance by replacing frequent administration of drugs such as contraceptives and hormones to maintain plasma concentration within the therapeutic window. Implantable or injectable systems have also been investigated as a means of local drug administration which favors high drug concentration at a site of interest, such as a tumor, while reducing systemic drug exposure to minimize unwanted side effects. Significant advances in the field of local DDS have led to increasingly sophisticated technology with new challenges including quantification of local and systemic pharmacokinetics and implant-body interactions. Because many of these sought-after parameters are highly dependent on the tissue properties at the implantation site, and rarely represented adequately with in vitro models, new nondestructive techniques that can be used to study implants in situ are highly desirable. Versatile imaging tools can meet this need and provide quantitative data on morphological and functional aspects of implantable systems. The focus of this review article is an overview of current biomedical imaging techniques, including magnetic resonance imaging (MRI), ultrasound imaging, optical imaging, X-ray and computed tomography (CT), and their application in evaluation of implantable DDS.

Significant advancements in biodegradable polymeric materials have been made for numerous biomedical applications including tissue engineering, regenerative medicine, and drug delivery. The functions of these polymers within each application often rely on controllable polymer degradation and erosion, yet the process has proven difficult to measure in vivo. Traditional methods for investigating polymer erosion and degradation are destructive, hampering accurate longitudinal measurement of the samples in the same subject. To overcome this limitation, we have utilized ultrasound elastography imaging as a tool to nondestructively measure strain of poly­(lactic-co-glycolic acid) (PLGA) phase sensitive in situ forming implants (ISFI), which changes with progressive loss of structural integrity resulting from polymer erosion. Using this tool, we investigated erosion kinetics of implants comprised of three different PLGA molecular weights (18, 34, and 52 kDa) in vitro and in vivo. The in vitro environment was created using a novel polyacrylamide based tissue mimicking phantom while the in vivo experiment was performed subcutaneously using a rat abdominal model. A strong linear relationship independent of polymer molecular weight was found between average strain values and erosion values in both the in vitro and in vivo environment. Results support the use of a mechanical stiffness-based predicative model for longitudinal monitoring of material erosion and highlight the use of ultrasound elastography as a nondestructive tool for measuring polymer erosion kinetics.

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e16556 Background: Following radical prostatectomy, around 30% of prostate cancer (PCa) patients experience biochemical recurrence (BCR). H&E highlights nuclear morphology and Feulgen reflects nuclear DNA content, a feature linked to PCa presence and aggressiveness. In this work we sought to explore whether computer extracted measurements of tumor morphology and tumor adjacent benign regions on H&E and Feulgen tissue images could predict BCR. Methods: We used 108 patients (59 BCR and 49 non-recurrence (NR)) and each patient had 242 QH features calculated from both the tumor and benign region of stained TMA core images. Feature selection was performed on a training set (30 BCR, 24 NR) to select the 10 most discriminating tumor and tumor adjacent benign features of each stain. A random forest classifier was trained with features so identified and validated on a test set (29 BCR, 25 NR) to predict BCR. Predictions were displayed using Kaplan-Meier analysis and area under the ROC curve (AUC). Results: The most discriminating feature from the tumor regions of the H&E stain was Fourier descriptors of nuclear shape and from the Feulgen stain was texture intensity while from the benign regions it was invariant moments of nuclear shape and texture contrast energy. Combining the significant features from tumor and tumor adjacent benign regions from H&E and Feulgen resulted in the highest accuracy and a statistically significant difference (p < 0.05) via a log-rank test (Table 1). Gleason score did not show statistically significant differences and had the lowest AUC. Conclusions: Combining nuclear morphology and DNA related features of the tumor and tumor adjacent benign regions enabled accurate prediction of BCR. With additional multi-site validation, the combined H&E + Feulgen classifier could allow better risk stratification and post-surgical patient management. [Table: see text]

preferences, 26/50 (54%) preferred the Rotterdam app while 24/50 (46%) preferred the Coral App.CONCLUSIONS: In our experience the Rotterdam App outperformed the Coral App for the prediction of prostate cancer or highgrade cancer diagnosis. Particularly, we confirmed, using the Rotterdam App, that only one out of ten patients with a low Rotterdam score will harbor high grade prostate cancer on biopsy.