# Deep learning in cervical cancer radiogenomics

> **NIH NIH R21** · WASHINGTON UNIVERSITY · 2022 · $220,894

## Abstract

PROJECT SUMMARY/ABSTRACT
The overall goal of this proposal is to optimize the use of radiomic and genomic data to develop biomarkers
which make clinical predictions that change cancer patient management. While the need for such predictive
biomarkers is evident across cancer types, we focus our proposal on the particularly prevalent and damaging
condition of recurrent, locally-advanced cervical cancer (LACC). Cervical cancer remains the third most
common cancer diagnosis of women, and treatment failure for locally-advanced disease is 30-50% following
chemoradiation therapy. There is a pressing need to identify patients at risk for treatment failure to allow for
personalized treatment including modified chemoradiation regimens, early escalation of therapy, and clinical
trial enrollment. To develop radiogenomic biomarkers for LACC recurrence, this proposal addresses three
outstanding methodological needs: limited availability of gene expression data for cancer subtypes, noisy and
redundant imaging feature data, and lack of disease-informed, interpretable -omics integration, each
addressed in its own specific aim. Aim 1 will use generative adversarial networks (GAN) to augment the small
gene expression datasets for all high-risk HPV subtypes. Aim 2 will optimize imaging feature selection using a
deep convolutional autoencoder (CAE). Aim 3 will integrate radiogenomic features through a structural
equation modeling (SEM) approach incorporating HPV-specific oncogenic mechanisms as latent variables.
Together, we expect fulfillment of these aims will create an optimized recurrence biomarker which will out-
perform other prediction modalities as well as standard-of-care follow-up imaging. Beyond the specific
application to HPV-driven malignancies, our proposal will generate novel tools and methods to integrate any
high-dimensional radiogenomic data with hypothesis-driven research findings to improve cancer prediction.

## Key facts

- **NIH application ID:** 10424854
- **Project number:** 1R21CA264343-01A1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Jin Zhang
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $220,894
- **Award type:** 1
- **Project period:** 2022-06-13 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10424854

## Citation

> US National Institutes of Health, RePORTER application 10424854, Deep learning in cervical cancer radiogenomics (1R21CA264343-01A1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10424854. Licensed CC0.

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