# Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

> **NIH NIH F31** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2021 · $43,436

## Abstract

Oropharyngeal cancer (OPC) is one of the few domestic cancers that is rising in incidence, primarily due to
increased human papillomavirus (HPV) infection rates. Radiographic images are crucial for assessment of
OPC and aid in disease detection and radiotherapy (RT) treatment. However, RT planning with conventional
imaging requires operator-dependent tumor segmentation, which is the primary source of treatment error
leading to unintended dose to normal tissues and subsequent debilitating oro-dental sequelae. Further, HPV+
OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant
differences between planned and delivered RT dose. Moreover, for HPV+ OPC patients with intra-treatment
resistant sub-volumes, the degree of normal tissue sparing is dependent on the location of residual active
disease. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous high-dimensional anatomical
and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision
support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. The
hypothesis of this F31 project is that mpMRI techniques and AI algorithms will facilitate segmentation, rapid
response prediction, and intra-treatment resistance classification of OPC. To test this hypothesis, I will first
develop an AI model using mpMRI to accurately segment primary tumors and metastatic cervical lymph nodes
and benchmark the model against human experts (Specific Aim 1). Next, I will investigate the differences in
mpMRI between primary tumors/nodes of rapid therapy responders and non-responders and subsequently use
AI to build a response prediction model (Specific Aim 2). Finally, I will characterize areas of primary tumor
treatment resistance at the regional and voxel level on mpMRI and subsequently use AI to build a resistance
classification model (Specific Aim 3). Through dedicated training proposed in this F31 award, I will gain
expertise in clinical decision support tool implementation and design (Training Goal 1), develop methodological
innovations for deep learning in medical imaging (Training Goal 2), gain expertise in statistical modeling and
clinical informatics approaches (Training Goal 3), and transition from graduate research to mentored post-graduate research and eventual independent principal investigator status (Training Goal 4). To successfully
complete my proposed specific aims and achieve my training goals, I have assembled a dedicated group of
mentors and collaborators that will provide me with excellent guidance throughout this project period.
Moreover, this project will take place at MD Anderson Cancer Center, an internationally renowned cancer
institution that is home to some of the largest imaging datasets of head and neck cancer patients in the world.
Therefore, I am uniquely positioned to conduct pioneering work in this research space through this F31 award.

## Key facts

- **NIH application ID:** 10387234
- **Project number:** 1F31DE031502-01
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Kareem Wahid
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $43,436
- **Award type:** 1
- **Project period:** 2022-01-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10387234

## Citation

> US National Institutes of Health, RePORTER application 10387234, Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance (1F31DE031502-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10387234. Licensed CC0.

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