# A System for Xerostomia Risk Classification after Head & Neck Cancer Radiotherapy

> **NIH NIH R43** · ONCOSPACE, INC. · 2021 · $399,916

## Abstract

Summary
Radiotherapy (RT) is a major component in the treatment of most head and neck cancer (HNC) cases. During
irradiation, sensitive regions such as the salivary glands can sustain injury, resulting in xerostomia (dry mouth).
This side effect is common and can significantly reduce quality of life during and post-treatment. The focus of
this application is prediction during treatment planning of whether patients will suffer high-grade xerostomia
(NCI CTCAE Grade 2-3) at the time of their first post-treatment follow-up visit, typically 3-6 months after RT
(prevalence is approximately 40%). Predictions will enable clinicians to carry out treatment planning with
improved knowledge of the likelihood of high-grade xerostomia development and allow better-informed and more
timely anticipation of consequences such as eating difficulty.
In this Phase 1 project, Oncospace Inc. will develop a Classification and Regression Tree (CART) prediction
model using over 1200 complete HNC patient records. Associations between high-grade xerostomia and a wide
range of dosimetric, clinical and demographic features will be automatically discovered and the features with the
strongest associations will populate the nodes of a decision tree. The terminal leaf nodes will each contain the
probability of high-grade xerostomia for the subset of patients in that node. In addition, leaf nodes will be assigned
binary class labels designating a high- or low risk of high-grade xerostomia. This type of model provides
transparency and interpretability, which are beneficial for clinical acceptance and for demonstration of safety to
regulatory agencies. The software will be built using the Microsoft Azure cloud architecture and be deployed via
a Software as a Service (SaaS) model.
There are three distinct aims of this project:
 1. Populate Oncospace Inc.’s Microsoft Azure CosmosDB database with data licensed from Johns Hopkins
 University, including steps such as patient de-identification, data curation, and additional dataset feature
engineering
 2. Perform CART modeling and test model accuracy, using separate training and test datasets and a variety
 of performance metrics, including sensitivity, specificity, AUC, and F1-score.
 3. Design a clinically acceptable risk classification strategy and a user interface (UI) to communicate model
 results. Expert input from a team of UI consultants and three radiation oncologists will be an integral part
 of the development, testing, and evaluation processes.
The successful completion of these aims will demonstrate the clinical and commercial feasibility of a xerostomia
prediction model for HNC. Further development in Phase 2 will include deeper model personalization via
incorporation of advanced image features (radiomics), as well as validation of model generalizability and
commercial viability via the curation and use in model building of data from other institutions.
Oncospace, formed in 2018, is uniquely positioned to carry out this work...

## Key facts

- **NIH application ID:** 10255864
- **Project number:** 1R43CA254559-01A1
- **Recipient organization:** ONCOSPACE, INC.
- **Principal Investigator:** Pranav Lakshminarayanan
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $399,916
- **Award type:** 1
- **Project period:** 2021-04-01 → 2022-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10255864

## Citation

> US National Institutes of Health, RePORTER application 10255864, A System for Xerostomia Risk Classification after Head & Neck Cancer Radiotherapy (1R43CA254559-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10255864. Licensed CC0.

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