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.

Public Health Relevance

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.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Ossandon, Miguel
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Case Western Reserve University
Biomedical Engineering
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code
Corredor, Germán; Wang, Xiangxue; Zhou, Yu et al. (2018) Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer. Clin Cancer Res :
Beig, Niha; Khorrami, Mohammadhadi; Alilou, Mehdi et al. (2018) Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology :180910
Orooji, Mahdi; Alilou, Mehdi; Rakshit, Sagar et al. (2018) Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. J Med Imaging (Bellingham) 5:024501
Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay et al. (2018) High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection. PLoS One 13:e0196828
Shiradkar, Rakesh; Ghose, Soumya; Jambor, Ivan et al. (2018) Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings. J Magn Reson Imaging 48:1626-1636
Nirschl, Jeffrey J; Janowczyk, Andrew; Peyster, Eliot G et al. (2018) A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. PLoS One 13:e0192726
Peyster, Eliot G; Madabhushi, Anant; Margulies, Kenneth B (2018) Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection. Transplantation 102:1230-1239
Penzias, Gregory; Singanamalli, Asha; Elliott, Robin et al. (2018) Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings. PLoS One 13:e0200730
Whitney, Jon; Corredor, German; Janowczyk, Andrew et al. (2018) Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer. BMC Cancer 18:610
Abdul-Wahid, Aws; Cydzik, Marzena; Fischer, Nicholas W et al. (2018) Serum-derived carcinoembryonic antigen (CEA) activates fibroblasts to induce a local re-modeling of the extracellular matrix that favors the engraftment of CEA-expressing tumor cells. Int J Cancer 143:1963-1977

Showing the most recent 10 out of 61 publications