# Large scale clinical and economic impact analysis of potentially malignant incidental findings in radiology reports

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2021 · $674,025

## Abstract

Abstract
Unexpected findings, or incidentalomas, are increasing dramatically with the growth in the use of imaging
technology within healthcare organizations. Incidentalomas may indicate significant health problems, such as
malignancy in the medium or long term. However, they also may lead to overinvestigation, unnecessary
radiation exposure, overtreatment, substantial downstream expenditures, and patient anxiety. Several
systematic reviews have explored the prevalence and outcomes of incidentalomas. These studies used
inconsistent and often inappropriate synthesis methods, commonly only focusing on one imaging scan or
organ in a very limited number of patients. As a result, there is need for large-scale study of incidentalomas
that can inform their follow up and guide efforts to optimize health outcomes. To address this need, we
propose to build natural language processing (NLP) approaches to identify cancer-related incidentalomas
reported in radiology reports (Aim 1) and to create the first large-scale incidentaloma database covering over
half-a-million patients (Aim 2). Our research dataset will contain radiology reports, clinical notes containing
imaging orders, as well as structured data such as demographic information (e.g., age) and diagnoses codes
of patients who received radiologic imaging tests in University of Washington Medical Center (UWMC),
Harborview Medical Center (HMC), Seattle Cancer Care Alliance (SCCA), and Northwest Hospital and Medical
Center (NWMC) between 2007-2019. Our patient population will be linked to Hutchinson Institute for Cancer
Outcomes Research (HICOR) data repository for detailed cancer outcomes and claims data. The created
database will be used for clinical and economic analysis of incidentalomas (Aim 3). We will (1) evaluate the
concordance between radiologists' documentation of incidentaloma follow-up and established clinical
guidelines for thyroid, lung, adrenal, kidney, liver, and pancreas incidentalomas, (2) determine risk of
subsequent cancer diagnosis and median survival for each category of incidentaloma, and (3) determine the
incremental cost associated with follow-up imaging in patients with incidentalomas. All models and their
implementations produced during the execution of this project will be shared with the community as open
source. Additionally, the de-identified incidentaloma database will be made available to the research
community under a data use agreement. By identifying risk factors for cancer diagnosis and death for common
incidental findings, we will be able to provide critical information for future clinical practice guideline
development and appropriate use criteria. We assembled a highly interdisciplinary team of experts in NLP,
medical informatics, radiology, oncology, health outcomes, and health economics to ensure the successful
completion of the proposed project.

## Key facts

- **NIH application ID:** 10116614
- **Project number:** 1R01CA248422-01A1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Martin Gunn
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $674,025
- **Award type:** 1
- **Project period:** 2021-03-03 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10116614

## Citation

> US National Institutes of Health, RePORTER application 10116614, Large scale clinical and economic impact analysis of potentially malignant incidental findings in radiology reports (1R01CA248422-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10116614. Licensed CC0.

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