# Quantitative framework to predict CTEPH surgical outcome from imaging

> **NIH NIH F30** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2022 · $39,660

## Abstract

Project Summary
The proposal “Quantitative framework to predict CTEPH surgical outcome from imaging” has a long term
objective of improving matching of Chronic Thromboembolic Pulmonary Hypertension (CTEPH) patients to
their optimum therapy. Currently, advancement of this goal is limited by the lack of quantitative tools and
metrics available to physicians to standardize evaluation of patient disease seen on imaging. In this proposal,
we aim to tackle two different aspects of this problem. First, we aim to develop metrics to comprehensively
quantify disease from imaging in a manner that informs disease severity. In this first aim, we are using dual-
energy CT images to capture, from a single study, both the amount and location of vascular obstruction,
perfusion deficit, and their relationship to one another. These metrics will be robustly designed to incorporate
all levels of the vasculature (proximal to distal), to capture a range of occlusion severities, and to use location
weightings based on surgical treatment accessibility. The utility of the metrics will be in their ability to inform
both pre and post operative invasive hemodynamics. Our second aim of the proposal is to utilize CT pulmonary
angiograms to predict the surgical accessibility of patient disease. We will train convolutional neural networks
to predict the vascular location (and therefore surgical accessibility) of CTEPH using the UCSD surgical
disease level classification. Neural networks will greatly aid in systematic prediction of disease location, since
they can analyze images without data loss, and can also incorporate both clinical and imaging data. Because
UCSD performs the highest volume of pulmonary thromboendarterectomy surgeries (a surgery to remove the
CTEPH vascular obstructions) in the world, we are the only institution that has the required number of pre-
operative images and gold standard (surgically confirmed) assessed surgical disease level classifications to
train and evaluate a neural network approach. In future work, these tools can be combined to rapidly,
systematically, and quantitatively evaluate CTEPH patients. With these metrics that standardize evaluation, we
will be able to quantify factors that contribute to CTEPH phenotypes and determine which of these imaging
phenotypes are most responsive to surgery.

## Key facts

- **NIH application ID:** 10389736
- **Project number:** 1F30HL158220-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Elizabeth M. Bird
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $39,660
- **Award type:** 1
- **Project period:** 2022-01-23 → 2024-01-22

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10389736

## Citation

> US National Institutes of Health, RePORTER application 10389736, Quantitative framework to predict CTEPH surgical outcome from imaging (1F30HL158220-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10389736. Licensed CC0.

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