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The Rutgers Cancer Institute of New Jersey (RCINJ) is one of the first Institutions to put forth an oncology-based Clinical Data Warehouse. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers and scientists at RCINJ have designed, developed and implemented the methods, protocols and workflows to facilitate the extraction, standardization and ongoing population of  the Clinical Data Warehouse with information originating from a full range of data sources including the EMR containing encounter, laboratory results, computerized physician order entry and data originating from radiology reports, pathology reports, clinical history, genomic sequencing studies. The co-localization of such a broad number of correlated data elements representing the full spectrum of clinical information, imaging studies and genomic information coupled with our experience and expertise in advanced pattern recognition, high-performance computing and data mining has positioned our team with unique opportunities to optimize personalizing treatment, refine best practices and provide objective, reproducible insight as to the underlying mechanisms of disease onset and progression.

This effort has recently been extended to house an enterprise-wide Clinical & Research Data Warehouse (CRDW) project that extended out of the realm of oncology to reach all areas of clinical medicine. A close collaboration with Rutgers Office of Information Technology and the Office of Advanced Research Computing (OARC) ensures the construction of an environment  to support state-of-the-art AI & Machine-Learning pipelines with ready access to high-performance and cloud computing. The platform facilitates secure integration and analysis of sensitive data. Using these assets, our team hopes to accelerate the pace of developing these methods and algorithms to support a wide range of clinical applications and translational research projects.

Some examples of successful projects reliant upon CRDW –

BioSpecimens Repository Inventory Dashboard


Electronic Medical Systems Research Feasibility Dashboard


Feature map representation of TIL and tumor analysis of digitized TCGA BRCA specimen based on VGG16 and ResNet neural networks.


A dynamic pathology quiz bank is used to training medical students and residents at University of Botswana and RWJBH

Related publications:

  1. Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology. Foran, DJ et al. Cancer Informatics. 2017. PMCID: PMC5392017
  2. An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools & Advanced Mining Capabilities. Foran DJ et al
  3. An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support based on Machine Learning and Joint Kernel-based Supervised Hashing. Foran, DJ et al. Journal on Cancer Informatics. In press
  4. Ren J, Karagoz K, Gatza ML, Singer EA, Sadimin E, Foran DJ, Qi X. Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks. J Med Imaging. 2018. PMID: 30840742; PMCID: PMC6237203.
  5. Ren J, Singer EA, Sadimin E, Foran DJ, Qi X. Statistical Analysis of Survival Models using Feature Quantification on Prostate Cancer Histopathology Images. Journal of Pathology Informatics. 2019. PMCID: PMC6788183
  6. Qi X, Brown L, Foran DJ, Nosher J, Hacihaliloglu I. Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural networks. International Journal of Computer Assisted Radiology and Surgery. 2021 January; doi: 10.1007/s11548-020-02305-w.