# Quantitative Imaging Core

> **NIH NIH P30** · H. LEE MOFFITT CANCER CTR & RES INST · 2023 · $1

## Abstract

PROJECT SUMMARY
QUANTITATIVE IMAGING CORE
The overarching goal of the Quantitative Imaging Core (QIC), formerly the Image Response Assessment Team
(IRAT), is to provide one-stop expertise in standardized medical imaging and reproducible image biomarker
development from the analysis of radiologic and digital pathology images collected in clinical and preclinical
studies. QIC provides expertise and tools for radiologic tumor response assessment measurement and reporting,
imaging data management, study coordination, and standard and advanced image processing operations,
including multispectral analysis, radiomics and pathomics. QIC also provides expertise in custom quantitative
image algorithm development and in design of radiologic imaging protocols for clinical studies. Spanning multiple
imaging scales and modalities, QIC offers quantitative analysis of image data acquired by live-cell microscopy,
digital pathology, small animal imaging, and clinical imaging. Data extracted from images are provided to
Members in formats suitable for downstream bioinformatics, biostatistics, and machine learning analyses. QIC
activities towards its goals are organized along three Specific Aims:
Aim 1: To provide high reliability and fast turnaround times for standard radiologic tumor response assessment
metrics.
Aim 2: To improve clinical and preclinical research studies at Moffitt, by providing turnkey imaging biomarker
services from quantitative imaging, radiomics and pathomics analyses.
Aim 3: To educate scientists and clinicians on experimental design elements required for the reproducible
acquisition and analysis of QI data in clinical and preclinical studies.
Customizable algorithms developed by QIC allow Members to pursue unique hypotheses characterizing and
quantifying cancer progression, evolution and response to therapy. QIC services enable investigators to unlock
information contained in radiologic and digital pathology images collected in clinical, translational and pre-clinical
studies. Since 2016, QIC usage has increased by 29% and has contributed to 87 publications (25 high impact),
and 600 clinical protocols, representing 4,668 unique patients. QIC increased staffing since 2016 and currently
operates at 68% usage capacity. In FY20, QIC supported 60 Members across all five Programs (CBE 14%, CE
5%, MM 66%, HOB 1%, IO 14%), with 64% of those Members holding peer review funding. This represented
81% of all QIC usage. Future priorities of the QIC are to leverage Moffitt’s enterprise-scale, cloud-based analytics
platforms and to implement newly developed digital pathology and multiplex immunohistochemistry image
processing modules to support analyses of tumor sections. Using commercial image processing tools, QIC will
also enhance training for Members, trainees, and staff.

## Key facts

- **NIH application ID:** 10558784
- **Project number:** 5P30CA076292-25
- **Recipient organization:** H. LEE MOFFITT CANCER CTR & RES INST
- **Principal Investigator:** Natarajan Raghunand
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1
- **Award type:** 5
- **Project period:** 1998-02-18 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10558784

## Citation

> US National Institutes of Health, RePORTER application 10558784, Quantitative Imaging Core (5P30CA076292-25). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10558784. Licensed CC0.

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