# An imaging AI-driven predictive chemoradiation response tool for Veterans with oropharyngeal cancer

> **NIH VA I01** · MICHAEL E DEBAKEY VA MEDICAL CENTER · 2024 · —

## Abstract

Oropharyngeal cancer (OPC) poses a complex therapeutic dilemma for patients and oncologists alike, made
worse by the epidemic increase in new cases associated with oncogenic human papillomavirus (HPV) infection.
OPC incidence in the Veteran population is increasing at a rate 3 times greater than the general US population
and survival lags the US population by >20% due to a disproportionate burden of aggressive, treatment-resistant
disease. These factors combine to make rapid development of precision oncology approaches to OPC a
categorical imperative for the VHA. In order to safely match treatment intensity to OPC biology, biomarkers of
treatment response must be developed and tailored to US Veterans with OPC. Using artificial intelligence (AI)
and machine learning (ML) approaches over the last 5 years we have shown the ability to discriminate risk of
recurrence following treatment in OPC, with a sensitivity and specificity which surpasses that of conventional
stratification approaches. We hypothesize that ML models based on multidimensional data (pathomic and
radiomic) and standard clinical-pathologic features can be integrated to generate a robust risk
stratification algorithm for Veterans with OPC that can be rapidly deployed across the VHA for
optimization of treatment algorithms.
 We will develop a predictive algorithm of chemo-radiation response in Veterans with OPC (Aim 1).
We will use a 1,000 OPC patient cohort from 6 tertiary VHA institutions and the VA Hub for Computer Vision and
Machine Learning in Precision Oncology (CoMPL) to curate, categorize, and integrate multidimensional data
inputs of Veterans with OPC suitable for machine learning to develop the Artificial Intelligence for Risk
Stratification of Oropharyngeal Carcinoma (AIROC) algorithm. AIROC will be designed to predict response to
conventional chemo-radiation with a sensitivity of >95% and a specificity of >95%. We will further refine AIROC
to maximize sensitivity and specificity on a secondary 400 OPC patient cohort generated from completed
cooperative group trials. We will then validate the predictive potential of AIROC in a prospective cohort of
Veterans with OPC (Aim 2). AIROC will be utilized to make a priori predictions of chemo-radiation response in
a blinded fashion for Veterans with OPC slated for curative intent chemo-radiation using locoregional recurrence
as the primary outcome measure. AIROC will be considered accurate if it correctly predicts response in 99% of
low-risk patients and 90% of high-risk patients.
Impact and Criteria for Success. AIROC will represent a unique chemo-radiation response algorithm built using
clinically generated data readily available across the VHA that can be easily deployed across VHA facilities. In
addition, the multimodal dataset will become the gold standard database for OPC in the VHA and the US broadly
and will serve as a valuable hub for future discovery and validation within the cooperative group clinical trial
network.

## Key facts

- **NIH application ID:** 10862187
- **Project number:** 1I01CX002776-01
- **Recipient organization:** MICHAEL E DEBAKEY VA MEDICAL CENTER
- **Principal Investigator:** Anant Madabhushi
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2024-04-01 → 2028-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10862187

## Citation

> US National Institutes of Health, RePORTER application 10862187, An imaging AI-driven predictive chemoradiation response tool for Veterans with oropharyngeal cancer (1I01CX002776-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10862187. Licensed CC0.

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