# Cyst-X: Interpretable Deep Learning Based Risk Stratification of Pancreatic Cystic Tumors

> **NIH NIH R01** · UNIVERSITY OF CENTRAL FLORIDA · 2020 · $510,210

## Abstract

Project Summary
The overall goal of this project is to develop a new diagnostic tool, called Cyst-X, for accurate detection and
characterization of pre-cancerous pancreatic cysts and improve patient outcome through precise decisions
(surgical resection or surveillance). Pancreatic cancer is the most fatal cancer among all cancers due to its poor
prognosis and lack of early detection methods. Unlike other common cancers where precursor lesions are well
known (colon polyps-colon cancer, ductal carcinoma in situ (DCIS)-breast cancer), pancreas cancer precursors
(cysts) are poorly understood. Diagnosing pancreatic cancer at earlier stages may decrease mortality and
morbidity rates of this lethal disease. One major approach for diagnosing pancreatic cancer at earlier stages is
to target pancreatic precancerous pancreatic neoplasms (cysts) before they turn into invasive cancer. Once cysts
are detected with radiology imaging such as magnetic resonance imaging (MRI), they should be characterized
with respect to their malignant potential. Low-risk cysts remain harmless; hence, patients should remain under
surveillance program. On the other hand, high-risk cysts can progress into an aggressive cancer, therefore,
patients should undergo surgical resection if possible. Despite this, international guidelines for risk stratification
of pancreatic cysts are woefully deficient (55-76% accuracy for determining characteristics of low-risk vs high
risk cystic tumors, while only 40-50% accuracy detecting cysts with MRI). Combined, these critical barriers
indicate that there is an urgent need for improving characterization of pancreatic cystic tumors. Based on our
preliminary results, which support the development of an image-based diagnostic decision tool, we hypothesize
that our proposed Cyst-X will produce higher diagnostic accuracy for characterizing pancreatic cysts and provide
better patient management compared to the current guidelines. Towards this overarching hypothesis, we will
first use powerful deep learning methods (specifically deep capsule networks) for automatically detecting and
segmenting the pancreas and pancreatic cysts from multi-sequence MRI scans (Aim 1). Next, we will create an
interpretable image-based diagnosis model for characterizing pancreatic cysts (Aim 2). Accurate
characterization is necessary for such a diagnostic model; however, emphasis will also be placed on
interpretability of the machine generated diagnostic model. Visual explanation of the discriminative features will
help radiologists obtain higher decision rates in patient management. In Aim 3, we will validate the proposed
Cyst-X framework in a multi-center study. A total of 1200 multi-sequence MRI scans will be collected from three
participating clinical centers (Mayo Clinic, Columbia University Medical Center, Erasmus Medical Center).
Comprehensive evaluations will be made to test the validity and generalizability of Cyst-X. All evaluations will be
made with respect to the ...

## Key facts

- **NIH application ID:** 9866770
- **Project number:** 1R01CA246704-01
- **Recipient organization:** UNIVERSITY OF CENTRAL FLORIDA
- **Principal Investigator:** Ulas Bagci
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $510,210
- **Award type:** 1
- **Project period:** 2020-03-01 → 2021-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9866770

## Citation

> US National Institutes of Health, RePORTER application 9866770, Cyst-X: Interpretable Deep Learning Based Risk Stratification of Pancreatic Cystic Tumors (1R01CA246704-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9866770. Licensed CC0.

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