The majority of high-grade serous ovarian cancers originate in the fallopian tubes, however, the corresponding structural changes in the extracellular matrix (ECM) have not been well-characterized. This information could provide new insight into the carcinogenesis and provide the basis for new diagnostic tools. We have previously used the collagen-specific Second Harmonic Generation (SHG) microscopy to probe collagen fiber alterations in high-grade serous ovarian cancer and in other ovarian tumors, and showed they could be uniquely identified by machine learning approaches. Here we couple SHG imaging of serous tubal intra-epithelial carcinomas (STICs), high-grade cancers, and normal regions of the fallopian tubes, using three distinct image analysis approaches to form a classification scheme based on the respective collagen fiber morphology. Using a linear discriminant analysis, we achieved near 100% classification accuracy between high-grade disease and the other tissues, where the STICs and normal regions were differentiated with ~75% accuracy. Importantly, the collagen in high-grade disease in both the fallopian tube and the ovary itself have a similar collagen morphology, further substantiating the metastasis between these sites. This analysis provides a new method of classification, but also quantifies the structural changes in the disease, which may provide new insight into metastasis.
Keywords: STIC; collagen fibers; high grade; image analysis; second harmonic generation.