# Using Radiogenomics to Noninvasively Predict the Malignant Potential of Intraductal Papillary Mucinous Neoplasms of the Pancreas and Uncover Hidden Biology

> **NIH NIH R37** · H. LEE MOFFITT CANCER CTR & RES INST · 2023 · $653,811

## Abstract

PROJECT SUMMARY/ABSTRACT
Approximately 700,000 pancreatic cysts are incidentally detected by imaging each year. Up to 70% of these
radiologically-detected cysts are intraductal papillary mucinous neoplasms (IPMNs), bona fide precursor
lesions to pancreatic cancer, the only solid malignancy with a 5-year relative survival rate in the single digits.
Once detected, existing imaging modalities and molecular markers cannot reliably distinguish low/moderate
grade (benign) IPMNs that merit surveillance from high-grade/invasive (malignant) IPMNs that warrant surgical
resection, posing a great clinical challenge. Based on preliminary data generated by our group, we
hypothesize that unexplored categories of quantitative ‘radiomic’ features extracted from preoperative
computed tomography (CT) scans will have added diagnostic value in predicting malignant IPMN pathology,
compared to standard radiologic features. We further hypothesize that a liquid biopsy that measures
microRNAs circulating in blood plasma (a miRNA genomic classifier (MGC)) that we have developed may help
to further enhance diagnostic accuracy. The goals of this proposal are to 1) Evaluate the diagnostic
performance of novel radiomic CT features in predicting IPMN pathology, compared to standard radiologic
features, using data and specimens from a retrospective series (Aim 1a) and a prospective multi-institutional
series of IPMN cases (Aim 1b); 2) Generate prototype clinical decision-making models (nomograms) that take
into account radiomic data, the MGC, and other clinical characteristics (Aim 2); and 3) Evaluate the relationship
between radiomic features and biological processes that underlie IPMN tumor development and/or
progression. By leveraging interdisciplinary expertise and largely existing data unique to our institutions, our
long-term goal is to discover a combined quantitative imaging and biomarker approach that is noninvasive and
has added value in predicting IPMN pathology beyond that provided by standard radiologic characteristics.
This line of translational research has potential to foster clinically actionable information that could be used to
rapidly and cost-effectively personalize care for individuals with IPMNs and ultimately reduce the burden of
pancreatic cancer as a major health problem.

## Key facts

- **NIH application ID:** 10684262
- **Project number:** 5R37CA229810-05
- **Recipient organization:** H. LEE MOFFITT CANCER CTR & RES INST
- **Principal Investigator:** Daniel Jeong
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $653,811
- **Award type:** 5
- **Project period:** 2019-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10684262

## Citation

> US National Institutes of Health, RePORTER application 10684262, Using Radiogenomics to Noninvasively Predict the Malignant Potential of Intraductal Papillary Mucinous Neoplasms of the Pancreas and Uncover Hidden Biology (5R37CA229810-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10684262. Licensed CC0.

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