Tissue Phenomics: Profiling Cancer Patients for Treatment Decisions

In the chapters 2 to 4 of this book different authors describe various approaches on how to convert the wealth of tissue slide pixel data into mineable information using knowledge-based and data-driven image analysis methods. Subsequently, the datafication of images and the bioinformatics part plays a crucial role in generating prognostic and predictive models for disease progression. The integration of other data sources such as genomics, radiomics and patient related information is also important and is described as well. As discussed in chapters 5 and 6, these models may classify patients in distinct groups such as those responding to a given therapy. Since Tissue Phenomics provides a huge set of potentially prognostic features (phenes), one focus of both chapters is robust feature selection methods by advanced Monte-Carlo cross validation algorithms. In chapter 7 we discuss multiple application examples of Tissue Phenomics in academic and commercial settings and its tremendous impact to advances in biomedical sciences. Building on the successes in research, chapters 8 and 9 discuss applications in clinical environments and provide a flavor to the future envision in chapter 10, where tissue datafication and subsequent patient profiling is part of every routine examination, with the goal to best match patients with the most successful therapy, as predicted by tissue phenes.