摘要：Abstract Ultrasound imaging is routinely used to guide prostate biopsies, yet delineation of tumors within the prostate gland is extremely challenging, even with microbubble (MB) contrast. A more effective ultrasound protocol is needed that can effectively localize malignancies for targeted biopsy or aid in patient selection and treatment planning for organ-sparing focal therapy. This study focused on evaluating the application of a novel nanobubble ultrasound contrast agent targeted to the prostate specific membrane antigen (PSMA-targeted NBs) in ultrasound imaging of prostate cancer (PCa) in vivo using a clinically relevant orthotopic tumor model in nude mice. Our results demonstrated that PSMA-targeted NBs had increased extravasation and retention in PSMA-expressing orthotopic mouse tumors. These processes are reflected in significantly different time intensity curve (TIC) and several kinetic parameters for targeted versus non-targeted NBs or LUMASON MBs. These, may in turn, lead to improved image-based detection and diagnosis of PCa in the future.
其他摘要：Abstract Ultrasound imaging is routinely used to guide prostate biopsies, yet delineation of tumors within the prostate gland is extremely challenging, even with microbubble (MB) contrast. A more effective ultrasound protocol is needed that can effectively localize malignancies for targeted biopsy or aid in patient selection and treatment planning for organ-sparing focal therapy. This study focused on evaluating the application of a novel nanobubble ultrasound contrast agent targeted to the prostate specific membrane antigen (PSMA-targeted NBs) in ultrasound imaging of prostate cancer (PCa) in vivo using a clinically relevant orthotopic tumor model in nude mice. Our results demonstrated that PSMA-targeted NBs had increased extravasation and retention in PSMA-expressing orthotopic mouse tumors. These processes are reflected in significantly different time intensity curve (TIC) and several kinetic parameters for targeted versus non-targeted NBs or LUMASON MBs. These, may in turn, lead to improved image-based detection and diagnosis of PCa in the future.
