# Deep learning-based target volume delineation capturing observer variability in head and neck cancer

> **NIH NIH R01** · YALE UNIVERSITY · 2024 · $564,599

## Abstract

We propose to develop and evaluate robust deep learning (DL)-based approaches capable of
accurately delineating target volumes and predicting recurrence in head and neck cancer (HNC)
patients. Radiation therapy (RT) is one of the most common treatments for HNC patients. Advanced
RT techniques enable highly conformal dose delivery to target volumes. However, a major challenge
in the RT planning for HNC is delineating target tumor volumes. Despite the availability of consensus
guidelines, delineating the gross target volume (GTV) and the clinical target volume (CTV) for HNC is
time-consuming and requires extensive clinical expertise. It demands a comprehensive understanding
of the region's intricate anatomy, tumor histology, and spread patterns. Precise delineation of target
volumes, especially the CTV, is essential to avoid marginal misses and excess doses to organs at risk
(OARs). Also, existing DL target volume delineation algorithms have predominantly focused on the
GTV delineation of oropharyngeal cancer, neglecting other commonly encountered tumor subsites,
such as laryngeal and nasopharyngeal cancers, which represent ~30% of HNC. Another challenge in
HNC management is the high recurrence rate. There is a daunting 30% five-year recurrence rate, with
at least 50% occurring in-field, despite comprehensive treatment strategies encompassing surgery,
chemotherapy, and RT. 18F-FDG-PET/CT has become part of the standard of care for HNC thanks to
its ability to improve the accuracy of GTV delineation, reveal previously undiagnosed regional nodal
disease, and contribute to a decrease in inter- and intra- observer variability (IOV) in GTV delineation.
However, most DL algorithms have used contours derived from a single physician as the gold standard
(label), failing to capture IOV in tumor delineation, an essential component of robust DL strategies. We
will develop robust delineation algorithms capable of accurately delineating both GTV and CTV in
various HNC locations in both primary and recurrent settings. We propose diffusion-based DL
algorithms to delineate GTV and CTV from 18F-FDG-PET and contrast-enhanced CT, while capturing
observer variability. We will also develop a DL method that incorporates imaging and clinical
information to predict recurrence and whether recurrence will occur in-field.

## Key facts

- **NIH application ID:** 10901288
- **Project number:** 1R01CA290745-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Georges El Fakhri
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $564,599
- **Award type:** 1
- **Project period:** 2024-05-01 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10901288

## Citation

> US National Institutes of Health, RePORTER application 10901288, Deep learning-based target volume delineation capturing observer variability in head and neck cancer (1R01CA290745-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10901288. Licensed CC0.

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