Methods and Tools for Integrating Pathomics Data into Cancer Registries

Our goal is to incorporate enrich SEER registry data with high-quality population-based biospecimen data in the form of digital pathology and quantitative pathomics feature sets to advance the development of imaging-based biomarkers for precision medicine. Our strategy is to create a well-curated repository of high-quality digitized pathology images, for subjects whose data is being collected by the registries. These images will be processed to extracted imaging features and establish deep linkages with registry data, thus enabling the creation of population cohorts using imaging and clinical attributes. The underlying scientific premise stems from increasing evidence that information extracted from digitized pathology images (pathomic features), such as morphological information, texture information and other higher-order features, are a quantitative surrogate of what is described in a pathology report. The important distinction being that these features are quantitative and reproducible, unlike human observations that are highly qualitative and subject to a high degree of variability. Specific examples of Pathomic feature sets include two or three-dimensional tumor contours and necrosis, identification of cancer or stromal nuclei, and of infiltrating lymphocytes. The information also includes various types of derived data such as texture, which can extend beyond what can be observed by the human eye. As the diagnosis of cancer and its immune response to therapy is made through tissue studies, the integration of Pathology imaging is critical to precisely classify tumors and predict tumor response to therapies.