# Assessment of a Radiomics-Based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules

> **NIH NIH K08** · UNIVERSITY OF PENNSYLVANIA · 2024 · $249,555

## Abstract

PROJECT SUMMARY/ABSTRACT
This proposal describes a comprehensive 5-year mentored career development plan with the goal of training
the candidate to become a leading independent physician-scientist focused on improving the diagnostic
evaluation and management of pulmonary nodules (PNs) to optimize early detection of thoracic cancer and
minimize unnecessary harms to patients. The candidate is currently a Post-Doctoral Research Fellow and
Attending Pulmonologist at the University of Pennsylvania (Penn). The proposal builds upon Dr. Kim’s previous
research training in epidemiology and biostatistics and clinical experience in thoracic oncology. PNs are
commonly detected by computed tomography (CT). Lung biopsy, a highly invasive procedure, is required for a
definitive diagnosis but carries significant risks and costs. Thus, clinicians face the diagnostic challenge of PN
malignancy risk estimation when deciding which patients should undergo a biopsy, and which should be
surveilled with repeat imaging. The overall goal of this project is to address the current inadequacy of
estimating malignancy risk within the diagnostic process of PN evaluation by assessing the clinical utility and
effectiveness of a radiomics-based computer-aided diagnosis (CAD) tool. This novel technology synthesizes
quantitative features from raw CT imaging data invisible to the human eye and has been previously
demonstrated by the candidate’s team to improve clinicians’ PN diagnostic accuracy. This project’s goal will be
accomplished via three complementary specific aims. In Aim 1, a retrospective cohort study will be performed
to determine the clinical utility of a CAD-based risk stratification strategy using net reclassification indices,
decision curve analysis, and relative utility curves. In Aim 2, a pilot, single-center pragmatic randomized clinical
trial will be conducted to compare the clinical effectiveness of a CAD-based risk stratification strategy to usual
care for appropriate management of PNs, defined as biopsy or empiric treatment for malignant PNs and
surveillance for benign PNs. Finally, in Aim 3, the cost-effectiveness of a CAD-based risk stratification strategy
will be evaluated using decision analytic models for a simulated cohort of individuals with newly detected PNs.
Dr. Kim has outlined a rigorous training plan of coursework, skills acquisition (with a focus on clinical utility
analysis, clinical trial design, decision analytic modeling, and cost-effectiveness analysis), and professional
career development. To realize this vision, he has assembled a distinguished, multidisciplinary mentorship and
advisory team, led by his primary mentor, Dr. Anil Vachani, the Director of Clinical Research in the Section of
Interventional Pulmonology and Thoracic Oncology at Penn, and co-mentor, Dr. Katharine Rendle, Deputy
Director for Research at the Penn Center for Cancer Care Innovation. Penn provides an outstanding
intellectual, collaborative, and supportive environment ...

## Key facts

- **NIH application ID:** 10890087
- **Project number:** 5K08CA279881-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Roger Yeon-Kyu Kim
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $249,555
- **Award type:** 5
- **Project period:** 2023-08-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10890087

## Citation

> US National Institutes of Health, RePORTER application 10890087, Assessment of a Radiomics-Based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules (5K08CA279881-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10890087. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*