# SCH: Personalized Rescheduling of Adaptive Radiation Therapy for Head & Neck Cancer

> **NIH NIH R01** · RICE UNIVERSITY · 2022 · $247,241

## Abstract

Head and neck cancers (HNCs) account for nearly 3% of all cancers in the U.S. and most
commonly affect aging individuals. While chemo-radiotherapy is the standard treatment
approach for HNC, the method is known to cause substantial side-effects. In particular,
anatomical changes occurring during the treatment may result in under-coverage of the clinical
target volume or over-dosage of organs at risk.
The project will develop novel optimization models for Adaptive Radiation Therapy (ART) – a
customized treatment planning approach for individual patients designed by evaluating the
systematic and random variations in tumor response. The models will use imaging data of tumor
volume and normal tissue complication probabilities to determine the optimal number and timing
of treatment replans. The models will provide personalized optimization through sequential
decision-making based on response to treatment, as well as optimization and evaluation of
simple threshold replanning policies often used by doctors. Optimal behavior in the face of
conflicting payer/provider perspectives for emerging technologies will also be analyzed using
mechanism design techniques. ART requires information about patient-state as well as transition
probabilities describing the tumor's evolution over time. Since the process inherently calls for
sequential decision-making under uncertainty, the proposed optimization models use a Markov
Decision Process (MDP). The resulting optimal policies may be difficult to implement in practice,
especially in centers lacking state-of-the-art equipment. Therefore, the proposed work will further
evaluate simple threshold replanning policies using a bilevel programming framework. In
particular, the bilevel program will find threshold values for various patient classes by minimizing
the deviation from the MDP-prescribed policy.
The proposed framework offers multiple avenues for methodological contributions. The novel
MDP design framework is an incredibly powerful tool that can be used to model many
interesting questions. We will explore ways of discretizing the continuous state spaces and
estimating transition probabilities based on patient imaging data. We will explore algorithms for
solving bilevel programs, especially utilizing the structure and properties of the lower-level MDP
models. Finally, we will study applications of the principal-agent framework from economics in
modeling payer/provider interactions for emerging clinical therapies.
RELEVANCE (See instructions):
This study uses novel models to develop and validate an integrated approach to reduce cancer
radiotherapy side effects while maintaining or improving cancer control. It will maximize efficiency for
patients and providers. Its findings will inform decisions about individual radiation planning, optimize risk-
stratified treatment, and healthcare policy implementation of effective new technology interventions.

## Key facts

- **NIH application ID:** 10397692
- **Project number:** 5R01CA257814-02
- **Recipient organization:** RICE UNIVERSITY
- **Principal Investigator:** Clifton David Fuller
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $247,241
- **Award type:** 5
- **Project period:** 2021-05-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10397692

## Citation

> US National Institutes of Health, RePORTER application 10397692, SCH: Personalized Rescheduling of Adaptive Radiation Therapy for Head & Neck Cancer (5R01CA257814-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10397692. Licensed CC0.

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