# 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 · 2024 · $779,609

## 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 ductal adenocarcinoma (PDAC), a malignancy with a 5-year relative survival rate of
only 12%. The goal of our parent grant is to fulfill the unmet need to discover a noninvasive biomarker and
imaging approach that has greater accuracy in predicting IPMN pathology than conventional radiologic and
clinical features, thereby enhancing clinical decision-making and promoting more good than harm for patients at-
risk to harbor or develop early PDAC. Our central hypothesis is that radiomic features extracted from
preoperative CT scans will more accurately predict IPMN pathology than conventional radiologic features, both
individually and in combination with a plasma-based miRNA genomic classifier (MGC). We further hypothesize
that the most promising radiomic features may serve as noninvasive surrogates for underlying biological
processes (which are miRNA-mediated and/or linked to mucin expression) that drive IPMN development and
progression to invasion. In the two-year extension period, we plan to continue to address this goal and
hypothesis by applying new artificial intelligence (AI)-based approaches and incorporating additional classes of
biomarkers (blood-based and behavioral). We aim to: evaluate the value of artificial intelligence (AI)-driven CT
deep learning radiomic features in predicting malignant versus benign IPMN pathology in retrospective and
prospective cohorts (Aim 1), evaluate telomere length and telomerase activity in the blood as candidate
molecular markers of high-grade IPMNs or early-stage PDAC (Aim 2), and use behavioral AI to predict malignant
transformation among patients with a high risk to develop PDAC (Aim 3). 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 PDAC as a major health problem, a goal
in line with the parent award and with NCI’s mission to lead, conduct, and support cancer research across the
nation to advance scientific knowledge and help all people live longer, healthier lives.

## Key facts

- **NIH application ID:** 10798763
- **Project number:** 4R37CA229810-06
- **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:** 2024
- **Award amount:** $779,609
- **Award type:** 4N
- **Project period:** 2019-07-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10798763

## Citation

> US National Institutes of Health, RePORTER application 10798763, Using Radiogenomics to Noninvasively Predict the Malignant Potential of Intraductal Papillary Mucinous Neoplasms of the Pancreas and Uncover Hidden Biology (4R37CA229810-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10798763. Licensed CC0.

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