# A Multifaceted Radiomics Model to Predict Cervical Lymph Node Metastasis for Involved Nodal Radiation Therapy

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2022 · $410,620

## Abstract

A Multifaceted Radiomics Model to Predict Cervical Lymph Node Metastasis for Involved
Nodal Radiation Therapy
PROJECT SUMMARY
The majority of disease sites treated with radiation therapy (RT) no longer receive elective/prophylactic RT to
clinically-negative areas, including lung, pancreas, and lymphoma. These disease sites now employ involved
nodal radiotherapy (INRT), focusing on involved lymphadenopathy. However, in head and neck cancer (HNC),
we still target the same lymph node regions as conventional 2D radiotherapy, despite our ability to tailor the
radiotherapy volume and dose to specific areas using intensity modulated radiation therapy (IMRT). This
approach leads to excessive acute and long-term toxicities for HNC patients after RT. Therefore, INRT is highly
desirable for HNC. In INRT, one particular challenge during gross tumor volume (GTV) and clinical target volume
(CTV) delineation is the identification of malignant lymphadenopathy. While some lymph nodes (LNs) are
obviously malignant based on standard imaging modalities, there is often uncertainty about whether a LN is
malignant and requires targeting. Treating benign nodes as malignant may cause a significantly higher risk of
late complications, such as xerostomia and dysphagia. On the other hand, missing occult lymphadenopathy will
lead to regional recurrence. The goal of this project is to develop, optimize, and test a multifaceted predictive
model with both high sensitivity and specificity for LN metastasis classification to maximize the efficacy and
minimize the toxicity of INRT for HNC. The proposed multifaced model presents a flexible framework and
considers multiple aspects of a predictive model, including: 1) Evaluation criteria used in model training (multi-
objective); 2) Different sources of information (multi-modality); and 3) Classifiers used for model construction
(multi-classifier). By designing a multi-objective function, we will consider sensitivity and specificity
simultaneously during model training and optimization. Instead of blindly combining features extracted from
different modalities and empirically choosing one preferred classifier, the information extracted by modality-
specific classifiers will be combined optimally through a reliable classifier fusion (RCF) strategy. We will develop
a prospective registry database to train the multi-classifier, multi-objective and multi-modality (MCOM) model
through prospectively collecting clinical characteristics and images of HNC patients who will undergo surgery at
UTSW with pathology-confirmed LN metastasis status. The model will be validated on an independent UTSW
patient cohort and patients who underwent outside imaging but operated at UTSW. The specific aims of the
project are: 1) Develop and validate a multi-classifier, multi-objective and multi-modality (MCOM) LN metastasis
prediction model for HNC patients. 2) Conduct a randomized phase II clinical trial to evaluate the efficacy and
utility of INRT versus conv...

## Key facts

- **NIH application ID:** 10520397
- **Project number:** 1R01CA251792-01A1
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** David Sher
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $410,620
- **Award type:** 1
- **Project period:** 2022-07-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10520397

## Citation

> US National Institutes of Health, RePORTER application 10520397, A Multifaceted Radiomics Model to Predict Cervical Lymph Node Metastasis for Involved Nodal Radiation Therapy (1R01CA251792-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10520397. Licensed CC0.

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