# Noninvasive imaging and blood biomarkers for personalized lung cancer immunotherapy

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $627,705

## Abstract

ABSTRACT
Lung cancer is the leading cause of cancer-related deaths in the United States and worldwide. Around ~80%
of lung cancer is non-small cell lung cancer (NSCLC), and most patients are diagnosed at an advanced stage.
Immunotherapy, specifically immune checkpoint inhibitors (ICIs), has dramatically improved survival outcomes
and is now the standard of care for treatment of advanced NSCLC without targetable oncogene mutations.
However, only ~20% patients respond to ICIs radiologically and ~35% will experience durable clinical benefit.
Given the potential toxicity and financial burden of these treatments, it is critical to identify which patients will
benefit from ICIs as early as possible. Unfortunately, existing tissue-based biomarkers do not accurately
predict ICI response for an individual patient. Moreover, they suffer from fundamental and practical limitations,
including intra-tumor heterogeneity, insufficient sample quality and quantity. There is a critical need for reliable
biomarkers of immunotherapy response and clinical benefit in NSCLC.
To address this unmet need, we will develop noninvasive imaging and blood-based biomarkers and integrate
these complementary approaches to improve prediction of immunotherapy response and outcome in NSCLC.
Specifically, we will: (1) develop knowledge-guided radiomics and biology-informed deep learning models to
enhance generalizability and interpretability; (2) propose novel methods to extract therapy-induced information
from longitudinal images; and (3) integrate imaging and blood-based biomarkers to further improve prediction
of response and outcomes. We will leverage a large institutional dataset for model training and establish the
clinical validity through rigorous prospective validation. Successful completion of the project will afford a
noninvasive approach to accurate prediction of immunotherapy response and clinical benefit in advanced lung
cancer. This may lead to response-driven personalized treatment strategies by distinguishing patients who will
respond to immunotherapy and for whom current standard treatment is sufficient; versus patients who will not
respond and may benefit from novel combination treatment strategies. Additionally, early response evaluation
using on-treatment imaging and blood information could be used to guide decisions of subsequent treatments.
Given the routine use of CT scans and blood samples in lung cancer care, the proposed biomarkers can be
readily integrated to current clinical workflow and may have a positive impact on broad patient populations.

## Key facts

- **NIH application ID:** 10899401
- **Project number:** 1R01CA290715-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Maximilian Diehn
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $627,705
- **Award type:** 1
- **Project period:** 2024-05-15 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10899401

## Citation

> US National Institutes of Health, RePORTER application 10899401, Noninvasive imaging and blood biomarkers for personalized lung cancer immunotherapy (1R01CA290715-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10899401. Licensed CC0.

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