# Early Evaluation of Ovarian Cancer Prognosis by Fusing Radiographic and Histopathologic Imaging Information

> **NIH NIH P20** · UNIVERSITY OF OKLAHOMA · 2022 · $244,870

## Abstract

Project 3: Early Evaluation of Ovarian Cancer Prognosis by Fusing Radiographic and Histopathologic
 Imaging Information
ABSTRACT
 As the most aggressive malignancy in gynecologic oncology, ovarian cancer is highly heterogeneous and
the tumor response to a specific chemotherapy vary significantly among patients. However, due to the lack of
accurate clinical markers to stratify patients and predict who can and cannot benefit from certain types of
chemotherapy drugs or methods, efficacy of treating ovarian cancer patients using chemotherapy is low. In order
to address and help solve this clinical challenge, the overarching objective of this project is to develop and
validate a new strategy for early prediction of tumor response to chemotherapy using a novel image marker
generated by a machine learning model that is trained using quantitative image features computed from
computer tomography (CT) and digital histopathology images. Based on the concept of Radiomics, Pathomics
and our encouraging preliminary studies, we hypothesize that the state-of-the-art data analysis technology can
fuse the valuable prognostic information from both radiographic and pathological images to generate a new
image marker, which has a high degree of association with the chemotherapy response of ovarian cancer
patients. To validate this hypothesis, we propose 4 specific aims. Aim 1: Based on a diverse patient database
at the Stephenson Cancer Center, we will assemble one retrospective and one prospective dataset, containing
a total of 420 ovarian cancer patients who have undergone chemotherapies. The dataset will include CT images,
histopathological images of tumor samples and other related clinical information of each patient. Aim 2: We will
explore and identify tumor heterogeneity-related images features computed from both CT and pathology images
after applying a new hybrid image processing scheme to accurately segment tumor volume and cancer cells.
Aim 3: We will apply feature selection methods on the initial CT/pathology feature pools to identify two optimal
feature vectors. Then, a prediction model (i.e., Bayesian belief network) will be trained to fuse optimal feature
vectors and other clinical variables to predict tumor response to therapy at early stage. Aim 4: We will conduct
a pilot prospective study to evaluate performance and robustness of the prediction model. Several statistical
methods (i.e. Cox proportional hazards analysis, receiver operation characteristic curve, confusion matrix) will
be used to evaluate the performance improvement by fusing the CT and pathology image features. We will also
validate the added prognostic value provided by the new model in the context of the existing markers. In order
to accomplish the proposed aims and research tasks, an interdisciplinary team is assembled, which includes
experts in medical imaging, gynecologic oncology, radiology and pathology from the University of Oklahoma. If
successful, this project can produce the es...

## Key facts

- **NIH application ID:** 10334987
- **Project number:** 1P20GM135009-01A1
- **Recipient organization:** UNIVERSITY OF OKLAHOMA
- **Principal Investigator:** Yuchen Qiu
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $244,870
- **Award type:** 1
- **Project period:** 2022-02-15 → 2026-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10334987

## Citation

> US National Institutes of Health, RePORTER application 10334987, Early Evaluation of Ovarian Cancer Prognosis by Fusing Radiographic and Histopathologic Imaging Information (1P20GM135009-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10334987. Licensed CC0.

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