With the advent of whole slide digital scanners, histopathology slides can be digitized into very high-resolution digital images, realizing a new big data stream that can potentially rival omics data in size and complexity. Just as with the analysis of high-throughput genetic and expression data, the application of sophisticated image analytic tools and data pipelines can render the often passive data of digital pathology (DP) archives into a powerful source for: (a) rich quantitative insights into cancer biology and (b) companion diagnostic decision support tools for precision medicine. Digital pathology enabled companion diagnostic tests could yield predictions of cancer risk and aggressiveness in a manner similar to molecular diagnostic tests. However, prior to widespread clinical adoption of DP, extensive evaluation of clinical interpretation of DP imaging (DPI) and accompanying decision support tools needs to be undertaken. Wider acceptance of DPI by the cancer community (clinical and research) is hampered by lack of a publicly available, open access image informatics platform for easily viewing, managing, and quantitatively analyzing DPIs. While some commercial platforms exist for viewing and analyzing DPI data, none of these platforms are freely available. Open source image viewing/management platforms that cater to the radiology (e.g. XNAT) and computational biology communities are typically not conducive to handling very large file sizes as encountered with DPI datasets. This multi-PI U24 proposal seeks to expand on an existing, freely available pathology image viewer (Sedeen Image Viewer) to create a pathology informatics platform (PIIP) for managing, annotating, sharing, and quantitatively analyzing DPI data. Sedeen was designed as a universal platform for DPI (by addressing several proprietary scanner formats and big data challenges), to provide (1) reliable and useful image annotation tools, and (2) for image registration and analysis of DPI data. Additionally, Sedeen has become an application for cropping large DPIs so that they can be input into programs such as Matlab or ImageJ. Sedeen has been freely available to the public for three years, with over 160 unique users from over 20 countries. Building on the initial successes of Sedeen and its existing user base, our intent is to massively increase dissemination of DPI and algorithms in the cancer research community and clinical trial efforts, as well as to contribute towards the adoption of a rational and standardized set of DP operational conventions. This unique project will allow end users with different needs and technical backgrounds to seamlessly (a) archive and manage, (b) share, and (c) visualize their DPI data, acquired from different sites, formats, and platforms. The PIIP will provide a unified user interface for third party algorithms (nuclear segmentation, color normalization, biomarker quantification, radiology-pathology fusion) and will allow for algorithmic evaluation upon data arising from a plurality of source sites. By partnering with professional societies, we envision that the PIIP user base will expand to include the oncology, pathology, radiology, and pharmaceutical communities.
This grant will result in the further development of advanced functionality of the already existing digital pathology image informatics platform (PIIP) with an established user-base for cancer research. Such an enhanced platform will provide the much-needed foundation for advancing (a) routine clinical adoption of digital pathology for primary diagnosis and (b) training and validation of companion diagnostic decision support systems based off histopathology. Thus, the project is aligned with the NCI's goal to foster innovative research strategies and their applications as a basis for ultimately protecting and improving human health.
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