# A fully automated PET radiomics framework

> **NIH NIH R56** · WASHINGTON UNIVERSITY · 2021 · $492,943

## Abstract

Summary
The overall goal of this proposal is to develop a fully automated PET radiomics framework and evaluate the
efficacy of PET radiomic features (RFs) derived from this framework in predicting therapy response in patients
with stage III non-small cell lung cancer (NSCLC). Radiomics is showing exciting promise in deriving biomarkers
for several diseases. The potential to measure and evaluate the efficacy of radiomic features derived from PET
for early prediction of therapy response is highly impactful since PET probes the functional characteristics of the
tumor, where changes are manifested sooner in comparison to anatomical changes. However, PET images have
high noise and limited resolution, which leads to inaccurate and imprecise RF measurements that then have
limited clinical value. Previously we have developed techniques to optimize quantitative imaging methods and
shown that these can help estimate more reliable quantitative metrics leading to better predictive ability with
these metrics. Building on these past studies and by combining concepts from imaging physics, statistical
inference theory, deep learning, we propose to develop methods that accurately and precisely estimate RFs
from PET. These methods will include a fully automated PET segmentation method that will enable reliable
delineation of tumor boundaries using a practical approach. Next, a no-gold-standard (NGS) evaluation
technique will be developed to optimize RF quantification protocols. This technique will provide a mechanism for
precise measurement of RFs from PET images without access to the ground truth RF value. The methods will
be rigorously validated in the context of measuring radiomics features in patients with NSCLC using a
combination of realistic simulations, physical phantom studies and existing patient data. Select RFs will then be
retrospectively evaluated on predicting therapy response using existing data the ACRIN 6697 longitudinal clinical
trial in patients with stage III NSCLC. A strong multidisciplinary team has been assembled for this project,
consisting of an imaging scientist, clinical nuclear-medicine radiologists, medical oncologist with expertise in
biomarker development for thoracic malignancies and biology of NSCLC, biostatistician, and a medical physicist.
The proposed methods are poised to have a strong impact on PET radiomics by enabling measurement of
precise and accurate RFs, and by facilitating the clinical translation of PET radiomics. The impact is strengthened
as we investigate the predictive ability of the PET RFs in patients with stage III NSCLC, a leading cause of death
with low overall survival, and with an important and timely need for improved personalized therapy regimens.
Further, the methods developed in this project are general and potentially impact precision-medicine approaches
for other cancers as well as other diseases where PET imaging has a clinical role.

## Key facts

- **NIH application ID:** 10458241
- **Project number:** 1R56EB028287-01A1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Abhinav K Jha
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $492,943
- **Award type:** 1
- **Project period:** 2021-09-06 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10458241

## Citation

> US National Institutes of Health, RePORTER application 10458241, A fully automated PET radiomics framework (1R56EB028287-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10458241. Licensed CC0.

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