  The spatial distributions of different types of cells could reveal a cancer
cell growth pattern, its relationships with the tumor microenvironment and the
immune response of the body, all of which represent key hallmarks of cancer.
However, manually recognizing and localizing all the cells in pathology slides
are almost impossible. In this study, we developed an automated cell type
classification pipeline, ConvPath, which includes nuclei segmentation,
convolutional neural network-based tumor, stromal and lymphocytes
classification, and extraction of tumor microenvironment related features for
lung cancer pathology images. The overall classification accuracy is 92.9% and
90.1% in training and independent testing datasets, respectively. By
identifying cells and classifying cell types, this pipeline can convert a
pathology image into a spatial map of tumor, stromal and lymphocyte cells. From
this spatial map, we can extracted features that characterize the tumor
micro-environment. Based on these features, we developed an image feature-based
prognostic model and validated the model in two independent cohorts. The
predicted risk group serves as an independent prognostic factor, after
adjusting for clinical variables that include age, gender, smoking status, and
stage.
