For most cancers, metastasis is the point at which disease is no longer curable. Earlier detection of metastasis, when it is undetectable by current clinical methods, may enable better outcomes. We have developed a biomaterial implant that recruits metastatic cancer cells in mouse models of breast cancer. Here, we investigate spectral ultrasound imaging (SUSI) as a non-invasive strategy for detecting metastasis to the implanted biomaterial scaffolds. Our results show that SUSI, which detects parameters related to tissue composition and structure, identified changes at an early time point when tumor cells were recruited to scaffolds in orthotopic breast cancer mouse models. These changes were not associated with acellular components in the scaffolds but were reflected in the cellular composition in the scaffold microenvironment, including an increase in CD31 + CD45-endothelial cell number in tumor bearing mice. In addition, we built a classification model based on changes in SUSI parameters from scaffold measurements to stratify tumor free and tumor bearing status. Combination of a linear discriminant analysis and bagged decision trees model resulted in an area under the curve of 0.92 for receiver operating characteristics analysis. With the potential for early non-invasive detection, SUSI could facilitate clinical translation of the scaffolds for monitoring metastatic disease.
Keywords: Cancer; Cancer diagnostics; Metastasis detection; Pre-metastatic niche; Spectral ultrasound imaging.