# STAN-CT: Standardization and Normalization of CT images for Lung Cancer Patients

> **NIH NIH R21** · UNIVERSITY OF KENTUCKY · 2020 · $175,505

## Abstract

Project Summary/Abstract
Lung cancer is the leading cause of cancer death and one of the most common cancers among both men and
women in the United States. Recent advances in high-resolution imaging set the stage for radiomics to
become an active emerging field in cancer research. However, the promise of radiomics is limited by a lack of
image standardization tools, because computed tomography (CT) images are often acquired using scanners
from different vendors with customized acquisition parameters, posing a fundamental challenge to radiomic
studies across sites. To overcome this challenge, especially for large-scale, multi-site radiomic studies,
advanced algorithms are required to integrate, standardize, and normalize CT images from multiple sources.
We propose to develop STAN-CT, a deep learning software package that can automatically standardize and
normalize a large volume of diagnostic images to facilitate cross-site large-scale image feature extraction for
lung cancer characterization and stratification. By precisely mitigating the differences in advanced radiomic
features of CT images, STAN-CT will overcome research silos and promote medical image resource sharing,
ultimately improving the diagnosis and treatment of lung cancer. Our goal will be achieved through two Aims.
In Aim 1, we will develop a working prototype to standardize CT images. First, we will collect raw image data
from lung cancer patients and reconstruct CT images using multiple image reconstruction parameters, and we
will scan a multipurpose chest phantom along with five different nodule inserts. Then, we will develop and train
STAN-CT for CT image standardization. An alternative training architecture will be developed to achieve the
improved model training stability. In Aim 2. We will deploy and test STAN-CT for image standardization locally
and across three medical centers. First, we will make the STAN-CT software package available to the public by
providing a menu-driven web-interface so that that users can conveniently convert medical images that were
taken using non-standard protocols to one or multiple standards that they specify. Second, we will deploy
STAN-CT at the University of Kentucky for local performance validation. We will test the functionality, reliability,
and performance of STAN-CT using both patient chest CT image data collected at large-scale and the
phantom image data, both independent to training. Third, we will deploy and test STAN-CT at the University of
Kentucky as well as the University of Texas Southwestern Medical Center and Emory University for cross-
center performance validation. We will use the same multipurpose chest phantom and both standard and non-
standard protocols to validate STAN-CT at the three centers. We will test the generalizability of STAN-CT
using clinical CT images of human patients and will determine whether a model trained using the data from
one medical center are applicable for images collected at another place. Finall...

## Key facts

- **NIH application ID:** 9961508
- **Project number:** 5R21CA231911-02
- **Recipient organization:** UNIVERSITY OF KENTUCKY
- **Principal Investigator:** Jin Chen
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $175,505
- **Award type:** 5
- **Project period:** 2019-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9961508

## Citation

> US National Institutes of Health, RePORTER application 9961508, STAN-CT: Standardization and Normalization of CT images for Lung Cancer Patients (5R21CA231911-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9961508. Licensed CC0.

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