# Development of an Open-Source Preclinical Imaging Informatics Platform for Cancer Research

> **NIH NIH U24** · WASHINGTON UNIVERSITY · 2021 · $748,114

## Abstract

ABSTRACT
Preclinical imaging is widely used in cancer research to devise novel tumor detection strategies, assess tumor
burden and physiology/biology, as well as to validate novel therapeutic strategies and predictive and biomarkers
of response to therapy. More recently, the use of patient-derived tumor xenografts (PDX) and genetically
engineered mouse models (GEMMs) has ushered an era of co-clinical trials where preclinical studies can inform
clinical trials, thus potentially bridging the translational gap in cancer research. However, differences in the
deployment of the instruments, differences in imaging formats and protocols, differences in animal models, and
variability in analytic pipelines among other factors result in non-tractable data and poor reproducibility.
Importantly, current databases are not compatible with complexity and growing demands in preclinical cancer
imaging which include big data needs and collection of metadata/annotation to support NCI’s precision medicine
initiative. Thus, there is an unmet need to develop a unifying imaging informatics and workflow management
platform to support cancer research, which will ultimately support the premise of translational precision medicine.
We propose to develop an open-source preclinical imaging informatics platform—Preclinical Imaging XNAT-
enabled Informatics (PIXI)—to manage the workflow of preclinical imaging laboratories, harmonize imaging
databases, and enable deployment of analytic and computational pipelines in preclinical imaging. PIXI will be
based on XNAT as the underlying informatics architecture. XNAT is used by over 200 academic institutions and
industry entities as the backbone for data management across a wide range of imaging applications in clinical
research, and thus offers a robust platform for the development and deployment of PIXI. Through this effort, we
will 1) develop the PIXI database and server to capture preclinical imaging associated data, metadata, and
preclinical imaging workflow and experiments; 2) develop the PIXI “point-of-service” interface, notebook
capabilities, and software development kit (SDK); and 3) develop the PIXI container-based application (“App”)
environment to implement portable analytic pipelines. Overall, the development of a preclinical imaging
informatics platform is expected to have a profound impact on the management of preclinical imaging in cancer
research which will ultimately support translational precision medicine.

## Key facts

- **NIH application ID:** 10249337
- **Project number:** 5U24CA253531-02
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Daniel Scott Marcus
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $748,114
- **Award type:** 5
- **Project period:** 2020-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10249337

## Citation

> US National Institutes of Health, RePORTER application 10249337, Development of an Open-Source Preclinical Imaging Informatics Platform for Cancer Research (5U24CA253531-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10249337. Licensed CC0.

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