The invasion and metastasis of tumor cells are not only related to the tumor cells themselves, but also closely intertwined with other components of the tumor microenvironment. The connective cells, including fibroblasts and myofibroblasts, play a crucial role in determining the remodeling of collagen fibers in the tumor microenvironment during the process of tumor invasion, which determinant of tumor migration. In this study, we used the multi-photon imaging system to identify two different prognostic tumor-associated collagen signatures (TACS4 and TACS6). Then, the multi-photon images were used to co-locate with HE images, and open-source convolutional neural network Hover-Net was used to segment, classify and quantifythe nuclear feature. Our results showed that the connective cells are opposed to tumor cells and do not mix with tumor cells in TACS4, while in TACS6, they are scattered promiscuously with tumor cells. The spatial distribution of connected cells in TACS4 is denser than that in TACS6. These differences in spatial distribution of connected cells may potentially have different prognostic information.
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