Evaluation of cancer cell and immune cell distribution in tumor microenvironment (TME) is one of the most important factors for guiding cancer immunotherapy and assessing therapeutic response. Multiplexed immunohistochemistry (mIHC) is often used to obtain the different types of cellular biomarker expression and distribution information in TME, but mIHC is limited by time-consuming and cost-intensive, and pathologists’ objectives etc. In this work, we proposed a deep learning-based modified U-Net (m-Unet), by replacing the original convolution sub-module with a modified block to predict the distribution of several typical cellular biomarkers’ expression and distribution information in TME. We have demonstrated that our model can be trained in both fully supervised and semi-supervised manners. The model can extract segmentation information from Hematoxylin and Eosin (H&E) images, and predict the cellular biomarker distributions including panCK for colon cancer cells, CD3 and CD20 for tumor infiltrating lymphocytes (TILs) and DAPI for nucleus. We have demonstrated that our model can be trained in both fully supervised and semi-supervised manners and. the performance of the m-Unet is better than the U-Net in this work. The optimal prediction accuracy of m-Unet is 88.3% on the test dataset. In general, this model possesses the potential to assist the clinical TME analysis.
In this work, we propose a novel GCN based Residual connected (GCN-RC) network to improve the quality of Fluorescence molecular tomography (FMT) morphological reconstruction. Instead of using a simplified linear model of photon propagation for FMT reconstruction, the method can directly construct a nonlinear mapping relationship between the surface photon density and internal fluorescent source. In order to validate the reconstruction performance of GCN-RC, we performed numerical simulation experiments and in vivo experiments based on tumor-bearing mice. Both numerical simulated and in vivo experimental results demonstrated that GCN-RC achieved improved reconstruction in terms of both source localization and morphology recovery.
The tumor microenvironment (TME) is the internal environment in which tumors develop and consist of tumor cells, various immune cells, and interstitial cells. Understanding different cell distribution in TME can help predict clinical response of immunotherapy and offer guidance for therapeutic optimization. Current pathological practice utilizes multiplexed immunohistochemistry (mIHC) to make assessment of different types of cell distribution in TME. However, these staining methods are time- and cost consuming and staining results often require professional pathologists to interpret, which can be possibly influenced by subjectivity. In this work, we propose a computational prediction method for cell distribution in TME using a modified U-Net structure, which can learn useful features from the hematoxylin and eosin (H&E) images and predict PanCK positive colon cancer cells and tumor infiltrating lymphocytes (TILs) at cellular-level. Our created datasets contain H&E images and cellular-level segmentation labels annotated by board-certified pathologists according to the corresponding registered mIHC images. We optimize the structure of the traditional U-Net network, and extract multi-scale features by integrating the Inception block, so as to create a modified U-Net structure which can predict PanCK positive colon cancer cells and TILs distribution in TME with the accuracy of 84.6% on test set. Hence, this method shows the potential to predict different types of cell distribution in TME more objectively and efficiently.
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