Threshold Analysis and Biodistribution of Fluorescently Labeled Bevacizumab in Human Breast Cancer

38Citations
Citations of this article
56Readers
Mendeley users who have this article in their library.
Get full text

Abstract

In vivo tumor labeling with fluorescent agents may assist endoscopic and surgical guidance for cancer therapy as well as create opportunities to directly observe cancer biology in patients. However, malignant and nonmalignant tissues are usually distinguished on fluorescence images by applying empirically determined fluorescence intensity thresholds. Here, we report the development of fSTREAM, a set of analytic methods designed to streamline the analysis of surgically excised breast tissues by collecting and statistically processing hybrid multiscale fluorescence, color, and histology readouts toward precision fluorescence imaging. fSTREAM addresses core questions of how to relate fluorescence intensity to tumor tissue and how to quantitatively assign a normalized threshold that sufficiently differentiates tumor tissue from healthy tissue. Using fSTREAM we assessed human breast tumors stained in vivo with fluorescent bevacizumab at microdose levels. Showing that detection of such levels is achievable, we validated fSTREAM for high-resolution mapping of the spatial pattern of labeled antibody and its relation to the underlying cancer pathophysiology and tumor border on a per patient basis. We demonstrated a 98% sensitivity and 79% specificity when using labeled bevacizumab to outline the tumor mass. Overall, our results illustrate a quantitative approach to relate fluorescence signals to malignant tissues and improve the theranostic application of fluorescence molecular imaging.

Cite

CITATION STYLE

APA

Koch, M., De Jong, J. S., Glatz, J., Symvoulidis, P., Lamberts, L. E., Adams, A. L. L., … Ntziachristos, V. (2017). Threshold Analysis and Biodistribution of Fluorescently Labeled Bevacizumab in Human Breast Cancer. Cancer Research, 77(3), 623–631. https://doi.org/10.1158/0008-5472.CAN-16-1773

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free