# Imaging and circulating DNA markers to assess early response and predict treatment failure patterns in lung cancer

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $537,646

## Abstract

ABSTRACT
Non-small cell lung cancer (NSCLC) is a major disease burden in the United States and worldwide.
Most patients are diagnosed at an advanced stage. For unresectable locally advanced NSCLC, the
standard of care is definitive concurrent chemoradiotherapy. Unfortunately, the majority of patients
will develop local-regional or distant failure with standard treatment. High-dose radiotherapy or
consolidation chemotherapy may reduce local or distant recurrence, but are also associated with
significant toxicity leading to morbidity and even mortality. Several randomized phase III trials failed to
show a survival benefit with intensified treatment given to unselected, locally advanced NSCLC
populations, highlighting the limitations of current `one-size-fits-all' treatment. A biomarker-driven
approach would allow rational treatment selection based on individualized assessment of risks of
local-regional versus distant failure. However, current imaging and genomic markers lack sufficient
accuracy in predicting relevant outcomes. The goal of this project is to develop and validate
quantitative imaging biomarkers to evaluate early response and integrate with circulating tumor DNA
analysis to predict patterns of treatment failure in locally advanced NSCLC. Previously, we developed
a novel tumor partitioning method based on FDG-PET and CT images, which revealed spatially
distinct tumor subregions with predictive significance in NSCLC. In this project, we will further improve
our tumor partitioning method to identify robust subregions, and propose novel image features to
characterize intratumoral spatial heterogeneity via spatially explicit analysis. A rigorous qualification
procedure will be employed to identify repeatable and reproducible image features for biomarker
discovery. We will develop a predictive imaging biomarker by incorporating pre and mid-treatment
scans in a retrospective patient cohort, and independently test it in two prospectively collected
cohorts including a national randomized phase II trial. Finally, we will combine imaging with circulating
tumor DNA analysis in a unifying model to further improve predictive accuracy. We anticipate that the
integrated biomarker will allow reliable, early prediction of local-regional vs distant failure, which has
important implications for deciding treatment between high-dose RT vs intensive systemic therapy. If
successful, the proposed biomarkers will afford a rational approach to individualized therapy and
ultimately improve outcomes in locally advanced NSCLC.

## Key facts

- **NIH application ID:** 10330010
- **Project number:** 5R01CA233578-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Maximilian Diehn
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $537,646
- **Award type:** 5
- **Project period:** 2019-02-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10330010

## Citation

> US National Institutes of Health, RePORTER application 10330010, Imaging and circulating DNA markers to assess early response and predict treatment failure patterns in lung cancer (5R01CA233578-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10330010. Licensed CC0.

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