# Precision Management of Cystic Precursors to Pancreatic Cancer

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $427,682

## Abstract

PROJECT SUMMARY/ABSTRACT
Our goal is to find the best ways to prevent pancreatic cancer deaths in patients with pancreatic cysts. Recent
advances in imaging have led to the detection of innumerable pancreatic cysts that could never be seen
before, now visible in >10% of patients who have an MRI and in >2% who have a CT scan for an unrelated
reason. When such cysts are unexpected and asymptomatic, they are considered “incidental.” The majority
represent intraductal papillary mucinous neoplasms (IPMNs), which are indolent precursors to Pancreatic
Ductal AdenoCarcinoma (PDAC), the most common type of pancreatic cancer. Given the poor prognosis and
survival of patients with PDAC, pancreatic cysts have become a primary target of early PDAC detection.
Imaging surveillance is advised for most patients who are diagnosed with an incidental pancreatic cyst, but key
factors that define surveillance – e.g., the frequency, modality, and duration of imaging, and when to pursue
biopsy or surgery – are highly controversial. Critics of intensive surveillance raise concerns about overtesting
and overtreatment, particularly given that many patients with such cysts are older and have comorbidities.
Advocates emphasize the singular opportunity for early PDAC detection that arises from close monitoring.
To address this problem, we will build a computer-based simulation model that replicates the natural history of
incidental pancreatic cysts, and use it to formulate a precision management approach. Our research plan will
draw from our team’s existing simulation model of PDAC, which is calibrated to data from the National Cancer
Institute’s Surveillance, Epidemiology, and End Results (SEER) Program and published studies. First, we will
extend this model to replicate the natural history of incidentally detected pancreatic cysts (Aim 1). We will then
use the model to identify effective (Aim 2) and cost-effective (Aim 3) management strategies that are tailored to
both cyst features (size, complexity) and patient characteristics (age, comorbidity status). Finally, we will
evaluate the potential for emerging blood and cyst-fluid biomarkers to further improve management (Aim 4).
The proposed research is innovative because it applies an advanced modeling approach to a controversial
problem that will be difficult to solve with observational studies or clinical trials alone. The research team is
well-suited, with an established track record in pancreatic cancer care and incidental pancreatic cysts, and with
substantial experience in developing mathematical models that have been used to inform health policy at
national levels. The results will be threefold: 1) a detailed natural history model of incidental pancreatic cysts;
2) a tailored approach to their management, based on cyst features and patient characteristics; and 3) a
roadmap for advancing future research in cystic precursors to pancreatic cancer in the coming years.

## Key facts

- **NIH application ID:** 9928396
- **Project number:** 5R01CA237133-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Pari Vijay Pandharipande
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $427,682
- **Award type:** 5
- **Project period:** 2019-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9928396

## Citation

> US National Institutes of Health, RePORTER application 9928396, Precision Management of Cystic Precursors to Pancreatic Cancer (5R01CA237133-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9928396. Licensed CC0.

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