Integrating Radiomics and Genomics to Improve the Clinical Assessment of Pancreatic Cysts and Early Detection of Pancreatic Cancer

Abstract

With the rapid utilization and ongoing advancements in cross-sectional abdominal imaging, the detection of pancreatic cysts has become increasingly frequent. It is reported pancreatic cysts are identified in 1.2-2.6 percent of abdominal computed tomography (CT) scans. Many of these cysts, including serous cystadenomas (SCA) and pseudocysts, are benign and can be monitored clinically. In contrast, mucinous cysts, which include intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs), have the potential to progress to pancreatic adenocarcinoma (PDAC). Currently, a multidisciplinary approach is recommended in the assessment of pancreatic cysts. This includes clinical and radiographic evaluation, endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA), cytology, cyst fluid analysis (e.g., viscosity) and tumor markers (e.g., carcinoembryonic antigen (CEA)). Despite a combination of methodologies, the distinction between mucinous cysts from other pancreatic cysts can be difficult. Moreover, the detection of PDAC within a pancreatic cyst can be even more challenging. Thus, there is a dire need for biomarker assays that can accurately differentiate mucinous from non mucinous pancreatic cysts and the presence versus absence of advanced neoplasia within a pancreatic cyst. This proposal represents the formation of a multi-institutional team of investigators to evaluate promising radiomic and genomic biomarkers to improve the initial evaluation and follow-up for patients with pancreatic cysts.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2022
Accession Number
AD1191052

Entities

People

  • Randall E Brand

Organizations

  • University of Pittsburgh

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Adenocarcinoma
  • Biological Markers
  • Biomedical Research
  • Cancer
  • Computer Vision
  • Data Analysis
  • Deep Learning
  • Department Of Defense
  • Detection
  • Electronic Mail
  • Exudates And Transudates
  • Genomics
  • Identification
  • Maryland
  • Medical Personnel
  • Neoplasms
  • Test And Evaluation
  • Tomography
  • United States
  • Universities
  • West Virginia
  • X-Ray Computed Tomography

Fields of Study

  • Medicine

Readers

  • Oncology
  • Oncology and Biomarker-Based Cancer Detection.