Tumor cell invasion and metastasis is closely related to various components in tumor microenvironment, and the spatial distribution of tumor cells may be linked to the tumor progression. The directionally distributed collagen fibers would lead to one-way migration of tumor cells, and misarranged collagen fibers allow tumor cells to multi-directional migration in tumor boundary, and therefore tumor cells present in different spatial distributions in the microenvironment. In this study, the open source convolutional neural network Hover-Net was used for segmentation and classification. After accurately segmenting tumor cells, spatial features were extracted. Our results indicated that there were significant differences in the spatial distribution of tumor cells in the two modes. These were further demonstrated to be potential different prognostic patterns.
Adipocytes are considered to be a critical cell type in the tumor microenvironment of breast cancer. Many studies have confirmed that adipocytes are not only found adjacent to cancer cells, but they also play an active role in the entire process of cancer development, progression, metastasis, and treatment response in breast cancer. Adipose tissue invasion (ATI) is a way of tumor cell metastasis, which not only indicates the poor prognosis of patients but also indicates the decline of survival rate. Multiphoton microscopy (MPM) with subcellular resolution based on second harmonic generation (SHG) and two-photon excited fluorescence (TPEF) is very suitable for real-time detecting morphological and structural changes in biological tissues without tissue staining and exogenous probe molecule. In this study, MPM was applied to identify the adipose tissue invasion in breast cancer patients. The results indicated that it is feasible to detect adipose tissue invasion with multiphoton microscopy, and to provide a new auxiliary tool for pathologists to quickly and effectively diagnose adipose tissue invasion.
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