Therapeutic drug monitoring (TDM) in cancer, while imperative, has been challenging due to inter-patient variability in drug pharmacokinetics. Additionally, most pharmacokinetic monitoring is done by assessments of the drugs in plasma, which is not an accurate gauge for drug concentrations in target tumor tissue. There exists a critical need for therapy monitoring tools that can provide real-time feedback on drug efficacy at target site to enable alteration in treatment regimens early during cancer therapy. Here, we report on theranostic optical imaging probes based on shortwave infrared (SWIR)-emitting rare earth-doped nanoparticles encapsulated with human serum albumin (abbreviated as ReANCs) that have demonstrated superior surveillance capability for detecting micro-lesions at depths of 1 cm in a mouse model of breast cancer metastasis. Most notably, ReANCs previously deployed for detection of multi-organ metastases resolved bone lesions earlier than contrast-enhanced magnetic resonance imaging (MRI). We engineered tumor-targeted ReANCs carrying a therapeutic payload as a potential theranostic for evaluating drug efficacy at the tumor site.
In vitro
results demonstrated efficacy of ReANCs carrying doxorubicin (Dox), providing sustained release of Dox while maintaining cytotoxic effects comparable to free Dox. Significantly, in a murine model of breast cancer lung metastasis, we demonstrated the ability for therapy monitoring based on measurements of SWIR fluorescence from tumor-targeted ReANCs. These findings correlated with a reduction in lung metastatic burden as quantified via MRI-based volumetric analysis over the course of four weeks. Future studies will address the potential of this novel class of theranostics as a preclinical pharmacological screening tool.
Imaging has become an invaluable tool in preclinical research for its capability to non-invasively detect and monitor disease and assess treatment response. With the increased use of preclinical imaging, large volumes of image data are being generated requiring critical data management tools. Due to proprietary issues and continuous technology development, preclinical images, unlike DICOM-based images, are often stored in an unstructured data file in company-specific proprietary formats. This limits the available DICOM-based image management database to be effectively used for preclinical applications. A centralized image registry and management tool is essential for advances in preclinical imaging research. Specifically, such tools may have a high impact in generating large image datasets for the evolving artificial intelligence applications and performing retrospective analyses of previously acquired images. In this study, a web-based server application is developed to address some of these issues. The application is designed to reflect the actual experimentation workflow maintaining detailed records of both individual images and experimental data relevant to specific studies and/or projects. The application also includes a web-based 3D/4D image viewer to easily and quickly view and evaluate images. This paper briefly describes the initial implementation of the web-based application.
Delineation of the prostate and nearby organs at risk (OARs) is a fundamental step in prostate cancer radiation therapy planning. Such contouring is often done manually, which can be a time-consuming and highly variable process. To alleviate these issues, we propose a fully automated two-step deep learning approach to segment the prostate, bladder, rectum, seminal vesicles, and femoral heads from CT images. The first step localizes the organs of interest using a modified 3D UNet architecture that contains an axial cross-attention module. Final segmentations are then computed for each organ individually using organ-specifically optimized UNet-based models. A total of 275 CT images were used for model training and validation. When evaluated on a hold-out set of 15 image sets, the full pipeline achieved mean dice similarity coefficients (DSC) and 95% Hausdorff distances (95HD, in mm) of 0.8660.034 and 4.461.02 (prostate), 0.9570.014 and 2.910.289 (bladder), 0.8530.044 and 5.101.87 (rectum), 0.7400.117 and 6.729.46 (seminal vesicles), 0.9420.016 and 2.851.04 (left femoral head), 0.9420.018 and 3.041.37 (right femoral head).
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