Computational pathology: Exploring the spatial dimension of tumor ecology.
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Tumors are evolving ecosystems where cancer subclones and the microenvironment interact. This is analogous to interaction dynamics between species in their natural habitats, which is a prime area of study in ecology. Spatial statistics are frequently used in ecological studies to infer complex relations including predator-prey, resource dependency and co-evolution. Recently, the emerging field of computational pathology has enabled high-throughput spatial analysis by using image processing to identify different cell types and their locations within histological tumor samples. We discuss how these data may be analyzed with spatial statistics used in ecology to reveal patterns and advance our understanding of ecological interactions occurring among cancer cells and their microenvironment.
Image Interpretation, Computer-Assisted
Predictive Value of Tests
Pattern Recognition, Automated
High-Throughput Screening Assays
Computational Pathology & Integrated Genomics
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Cancer letters, 2016, 380 (1), pp. 296 - 303