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The liver is a common site for metastases for multiple cancer types – with adenocarcinoma being the most common subtype. Standard pathology assessments fail to identify a site of origin for three to five percent of these patients, who are then treated in an empirical manner.1 Computational pathology is an ideal technology to improve diagnostic accuracy for metastatic cancers with a significant reduction in costs and increase in tissue preservation for future testing. Recently, Deep Learning (DL)-based algorithms have been developed to automatically identify the origination of metastases using only H&E-stained whole slide images as inputs.2 However, immunohistochemical based analysis is time- and resource-intensive and tissue destructive, while the “black box” nature of the DL-based approach makes it difficult if not impossible to explain the biology behind the classifier’s decision. This pilot study demonstrates that an analysis of computer extracted quantitative cellular features from tumor epithelial nuclei, including a tumor nuclei morphological feature and graph feature as well as cytoplasm texture from H&E stained histology slides, can be used to identify the origination of liver metastasis. Since the primary source of liver metastasis is typically the colon, breast, pancreas, or esophagus,3 a data set consisting of 120 patients with primary adenocarcinoma and matched liver metastases from the breast, colon, esophagus, and pancreas was constructed. A combination of supervised classification and unsupervised clustering was applied in order to identify the tumors’ origination using the above mentioned histomorphometric features from the data set. We evaluated the utility of histomorphometric features to identify the origination of the metastatic tumors in the liver using both supervised machine classifiers and unsupervised clustering. A Random Forest classifier achieved AUCs of 0.83, 0.63, 0.82, and 0.64 in classifying the primary or metastatic tumor from colon, esophagus, breast, and pancreas. The top performing features included local cellular diversity as well as cytoplasmic texture feature families. We further verified selected features using unsupervised methods such as UMAP, hierarchical clustering, and content-based image retrieving. Based off similarity measures calculated between image tiles in the primary and metastatic tissue, a heatmap corresponding to the WSIs for the primary tumor was constructed to highlight the sites representing potential points of origination of the metastases. Our preliminary results suggest that the site of origination for metastases in the context of primary breast tumors appears to be within the stroma.
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