# Longitudinal Spatial-Nonspatial Decision Support for Competing Outcomes in Head and Neck Cancer Therapy

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2021 · $589,506

## Abstract

Cancers that depend on the spatial location of the disease affect all ethnicities and age groups,
accounting for significant mortality and therapy-related side effects. In one instance, over 50,000
new cases of head and neck squamous carcinomas are diagnosed each year in the United
States, leading to large, rich repositories of patient data. For each of these cases, oncologists
need to anticipate survival, oncologic, and toxicity outcomes associated with treatment
strategies in order to select a treatment which balances efficacy and toxicity. However, despite
the wealth of data available, in the clinic decision support for cancer treatment is rudimentary
and incorporates only a handful of patient characteristics, largely due to a lack of computational
methodology and tools.
We propose to construct a novel statistical and computational methodology for longitudinal and
personalized treatment decisions over time, with specific application to head and neck cancer
therapy planning. Simultaneous incorporation of complex factors---such as radiation dose
location with respect to radiosensitive organs or patient reported side effects affecting quality of
life---into treatment decisions over the course of cancer therapy requires the development of
novel methodology. This methodology is revolutionary in that it is the first in the field to include
both imaging and nonimaging data, while taking into account large-scale biological and clinical
correlates. The approach is innovative through its leverage of big data repositories and through
its unique blend of computational modeling principles from bioengineering and computer
science. These methods allow us to incorporate diverse data types and model competing
outcomes.
From a clinical perspective, this integrative approach is novel in the field of cancer therapy. The
resulting clinical decision support methodology will mark a significant advance in biomedical
computing because it will be able to identify, for the first time, actionable timepoints for therapy
and toxicity modification, based on a patient’s characteristics and quality of life indicators. The
empirically-derived treatment decision support methodology developed in this project has the
potential to directly improve the standard of care and the quality of life of surviving patients with
a grave, often fatal and debilitating illness.

## Key facts

- **NIH application ID:** 10185481
- **Project number:** 1R01CA258827-01
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** GUADALUPE CANAHUATE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $589,506
- **Award type:** 1
- **Project period:** 2021-03-01 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10185481

## Citation

> US National Institutes of Health, RePORTER application 10185481, Longitudinal Spatial-Nonspatial Decision Support for Competing Outcomes in Head and Neck Cancer Therapy (1R01CA258827-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10185481. Licensed CC0.

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