# Diversity Supplement: Radiation-specific Automated Dental Dose Distributions via Machine-learning based Mapping for Accurate Predictions of (Peri)odontal Problems (RADMAP)

> **NIH NIH R21** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2022 · $78,700

## Abstract

PROJECT SUMMARY
Oral cavity and oropharyngeal (OC/OPC) cancers afflict more than 53,000 individuals in the United States
annually. Despite advancements in oncologic therapies, the majority of patients will experience significant
toxicity burden during and after therapy, including moderate-severe xerostomia, reduced mouth opening (i.e.
trismus), periodontal disease, and osteoradionecrosis. To date, acute and chronic orodental complications are
largely managed by clinicians and dentists based on empirical knowledge, with wide inter-provider
management variability influenced by provider experience and available clinical information which is often
incomplete, incorrect, or nonexistent. To further complicate long-term care of OC/OPC survivors, there is no
standardized method for communicating with dentists the extent and intensity of radiation doses delivered to
tooth bearing areas which is vital information for accurate assessment of risks related to dental procedures.
Therefore, development of a standardized radiotherapy dental information tool and data-driven, algorithmic
toxicity risk prediction models for enhanced communication and personalized medicine for OC/OPC
survivors remains an unmet public health need. In response to NIDCR’s NOT-DE-20-006, we herein propose
a rigorous and reproducible application of informatics and computational methods and approaches for the
development of machine learning “ML/AI based optimization of clinical procedures for precision dental care”,
“novel and robust data analysis algorithms to tackle causal mechanisms of action for onset and progression
of disease” related to posttherapy orodental complications, and “computational modeling for treatment
planning and assessment of treatment outcomes.” In Specific Aim 1, we will train and validate a deep
learning contouring (DLC) neural network for automatic delineation of tooth-bearing regions. Our collaborator,
Dr. van Dijk, has previous experience with DLC design and application for auto- delineation of non-dental
head and neck organs at risk (OAR). Her research, published in a peer- reviewed journal showing an equal or
significantly improved OAR automatic delineation using DLC over atlas-based contouring, will serve as a
reproducible model for our proposed project. Using DLC-based mandibular and dental OAR delineation (SA
1), we will develop a novel “radiation odontogram” which will generate automated and accurate summative
radiotherapy dose distribution mapping reports for effective data transmission and communication among
providers (SA 2). Accurate prognosis and management of high-morbidity high-prevalence post- therapy
orodental sequelae will be enabled through the development of a statistically robust machine-learning based
model of toxicity risk predictions that incorporates patient- and provide- generated data (Aim 3). In summary,
the RADMAP proposal fosters innovative informatics and computational modeling approaches to address
existing challenges in multidisci...

## Key facts

- **NIH application ID:** 10602003
- **Project number:** 3R21DE031082-02S1
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Amy Catherine Moreno
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $78,700
- **Award type:** 3
- **Project period:** 2022-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10602003

## Citation

> US National Institutes of Health, RePORTER application 10602003, Diversity Supplement: Radiation-specific Automated Dental Dose Distributions via Machine-learning based Mapping for Accurate Predictions of (Peri)odontal Problems (RADMAP) (3R21DE031082-02S1). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10602003. Licensed CC0.

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