# Precision imaging for risk stratification and personalized therapy of oropharyngeal cancer

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $633,059

## Abstract

ABSTRACT
During the past few decades, there has been a rapid increase in the incidence of oropharyngeal
cancer (OPC), which is attributable to the epidemic of oral human papillomavirus (HPV) infection.
Patients with HPV-positive OPC respond well to concurrent chemoradiotherapy and have a more
favorable prognosis than HPV-negative patients. However, standard treatment is associated with
significant toxicity and likely represents over-treatment for many patients with HPV-positive disease.
Several randomized clinical trials have tested novel deintensification strategies with the goal to
reduce toxicity and improve patients’ quality of life while preserving the high cure rate. These trials
enroll patients based on cancer stage and smoking history. However, current clinical prognostic
factors are rather crude and do not accurately predict disease progression on an individual level.
Reliable prognostic models are critically needed for personalized risk-adaptive therapy of OPC. To
address this unmet need, we propose quantitative CT features to characterize intratumoral spatial
heterogeneity and disease invasion/spread, which are known drivers of treatment resistance and
disease progression. In addition to knowledge-based image features, we will develop complementary
data-driven deep learning models to predict disease progression by using a retrospective multi-
institutional dataset of 1771 patients. Further, we will integrate imaging with clinical data to improve
prediction and establish their validity by rigorous prospective validation in 1780 patients enrolled in 3
randomized clinical trials. Finally, we will employ a radiogenomic approach to elucidate biological
basis of the imaging signatures. If successful, the proposed models will allow more accurate
prediction of prognosis and improve risk stratification of OPC. This has significant therapeutic
implications by optimizing the selection of patients for treatment deintensification, which will increase
the likelihood of success of future clinical trials and pave the way for precision medicine in OPC.
Because the information is derived from standard CT scans, this would be readily integrated into
current clinical workflow, widely applicable to underserved populations in low-resource settings, and
therefore would help reduce health disparity in the US.

## Key facts

- **NIH application ID:** 10445148
- **Project number:** 1R01DE030894-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Quynh-Thu Xuan Le
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $633,059
- **Award type:** 1
- **Project period:** 2022-07-05 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10445148

## Citation

> US National Institutes of Health, RePORTER application 10445148, Precision imaging for risk stratification and personalized therapy of oropharyngeal cancer (1R01DE030894-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10445148. Licensed CC0.

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