# Multimodal computational models to stratify ovarian cancer patients

> **NIH NIH F30** · WEILL MEDICAL COLL OF CORNELL UNIV · 2021 · $51,036

## Abstract

Project Summary/Abstract
High-grade serous ovarian cancer (HGSC) is the most lethal gynecologic malignancy, with a five-year survival
rate of less than 30% for metastatic disease. Our lab has identified mutational processes as predictors of survival
and response to therapy, along with a working model to predict homologous recombination deficiency from
hematoxylin and eosin (H&E) whole-slide images. Our collaborators in diagnostic radiology have discovered
robust associations between BRCA mutational status and qualitative features on contrast-enhanced computed
tomography (CE-CT). These two imaging modalities, however, have yet to be combined with genomic
information to improve stratification of HGSC patients.
Based on these preliminary data, I will test the hypothesis that combined mesoscopic information in CE-CT and
microscopic information in H&E can be used to infer known mutational subtypes and also to identify novel patient
strata. I have curated a cohort of 118 HGSC patients with matched targeted panel-based genome sequencing,
scanned H&E whole-slide images, and segmented pre-treatment CE-CT images for this purpose. In Specific
Aim 1, I will develop a machine learning model to integrate CE-CT and H&E imaging to predict mutational
subtype from these ubiquitous imaging modalities. In Specific Aim 2, I will develop an end-to-end deep learning
model to integrate the complementary information from CE-CT, H&E, and genome sequencing for survival
analysis using a Cox Proportional Hazards model. I anticipate that this work will (1) identify refined stratification
of HGSC patients using this multimodal prognostic signature and (2) develop a general-purpose machine
learning model to integrate CE-CT, H&E, and genomic sequencing for cancer patient survival analysis.
This research will be conducted at Memorial Sloan Kettering Cancer Center under the mentorship of Dr. Sohrab
Shah. The training plan that Dr. Shah and I have developed will prepare me well for a future as a physician-
scientist conducting machine learning research for cancer patient prognosis.

## Key facts

- **NIH application ID:** 10146152
- **Project number:** 1F30CA257414-01
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Kevin Michael Boehm
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $51,036
- **Award type:** 1
- **Project period:** 2021-02-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10146152

## Citation

> US National Institutes of Health, RePORTER application 10146152, Multimodal computational models to stratify ovarian cancer patients (1F30CA257414-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10146152. Licensed CC0.

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