# Combined Imaging and RNA Analyses to Develop Cervical Cancer Biomarkers

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2024 · $661,302

## Abstract

TITLE: Combined Imaging and RNA Analyses to Develop Cervical Cancer Biomarkers
ABSTRACT
Despite significant advances in disease prevention and screening, cervical cancer continues to be an important
worldwide public health problem. Treating cervical cancer patients with personalized strategies can potentially
improve the chance of survival. Predicting early in treatment whether a tumor is likely to be responsive is one of
the most challenging yet important tasks for stratifying cervical cancer patients and supporting personalized
treatment strategies to improve cancer patient care.
Various unimodal data, including ribonucleic acids (RNAs), radiologic and histologic imaging, and
clinicopathologic data, have been employed for predicting cervical cancer treatment response and patient
outcome. Each type of unimodal data analyzes tumor phenotypes from a different point of view and provides
valuable while limited prognostic information. We and others have shown that RNAs are promising biomarkers
and play critical regulatory roles in cervical cancer. Radiologic imaging biomarkers have shown promise in
stratifying patients with favorable and unfavorable prognosis for multiple tumor sites. Their non-invasive
characteristics also allow for convenient and longitudinal monitoring of tumor progression and heterogeneous
response during the treatment course. Moreover, histologic images provide key information about microscopic
structure of cells and tissues of organisms. Recent reports and our preliminary studies have shown that
histologic imaging biomarkers, can aid in clinical decision-making by identifying metastases, subtyping and
grading tumors, and predicting clinical outcomes. Clinicopathologic biomarkers show prognostic value
through retrospective studies. Still, many cervical cancer patients have tumor recurrence despite favorable
prognosis by these biomarkers individually.
The major goal of this study is to develop a comprehensive and robust computational model for prediction of
cervical cancer treatment response and outcomes. We will integrate our recently developed advanced learning-
based techniques to build prognostic models using about 600 cervical patient cases collected from two
institutions. The prognostic model will form a solid basis for individualized care of cervical cancer patients.
Moreover, our work is expected to discover the correlations among multimodal data, leading to dynamic patient
stratification to support adaptive treatment strategies in the future.

## Key facts

- **NIH application ID:** 10998883
- **Project number:** 1R01CA287778-01A1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Hua Li
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $661,302
- **Award type:** 1
- **Project period:** 2024-06-10 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10998883

## Citation

> US National Institutes of Health, RePORTER application 10998883, Combined Imaging and RNA Analyses to Develop Cervical Cancer Biomarkers (1R01CA287778-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10998883. Licensed CC0.

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