# Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CT

> **NIH NIH R01** · CEDARS-SINAI MEDICAL CENTER · 2021 · $777,637

## Abstract

PROJECT SUMMARY
Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CT
Coronary artery disease remains a major public health problem worldwide. It causes approximately 1 of every 6
deaths in the United States. Imaging of myocardial perfusion (delivery of blood to the heart muscle) by myocardial
perfusion single photon emission tomography (MPS) allows physicians to detect disease before heart attacks
occur and is currently used to predict risk in millions of patients annually.
Under the current grant, we have established a unique collaborative multicenter registry including over 23,000
imaging datasets (REFINE SPECT) with both prognostic (major adverse cardiovascular events) and diagnostic
(invasive catheterization) outcomes. Using this registry, we have demonstrated that a combination of MPS image
analysis and artificial intelligence (AI) tools achieved superior predictive performance compared to visual
assessment by experienced readers or current state-of-the-art quantitative techniques. In the renewal, we plan
to expand REFINE SPECT with now-available enhanced datasets (adding CT and myocardial blood flow
information) and leverage latest AI advances to provide a personalized decision support tool for patient-specific
cardiovascular risk assessment and estimation of benefit from revascularization following MPS.
The overall aim is to optimize the clinical capabilities of MPS in risk prediction and treatment guidance by
integrating all available imaging and clinical data with state-of-the-art AI methods. For this work, we propose the
following 3 specific aims: (1) To expand and enhance our REFINE SPECT registry including CT and MPS flow
data, (2) To develop fully automated techniques for all MPS and CT image analysis, (3) To apply explainable
deep learning time-to-event AI models for optimal prediction of MACE and benefit from revascularization from
all image and clinical data.
This work will result in an immediately deployable clinical tool, which will optimally predict risk of adverse events
and establish the relative benefits from specific therapies, beyond what is possible by subjective visual analysis
and mental integration of all imaging (MPS, CT, flow), and clinical data by physicians. Such quantitative
integrative methods are not yet available, leaving the current practice for assessing risk and recommending
therapy highly subjective. The precise quantitative results will be presented to clinicians in easy to understand
terms (e.g., % risk per year, or relative risk of one therapy vs. the alternative) for a specific patient. Additionally,
our methods to make AI conclusions more tangible will improve adoption of this technology. All results will be
derived fully automatically thus eliminating any variability. Our approach will fit into current MPS practice and will
be immediately translatable to clinics worldwide. Most importantly, this research will allow patients to benefit
from increased precision and accuracy ...

## Key facts

- **NIH application ID:** 10110023
- **Project number:** 5R01HL089765-11
- **Recipient organization:** CEDARS-SINAI MEDICAL CENTER
- **Principal Investigator:** Piotr J Slomka
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $777,637
- **Award type:** 5
- **Project period:** 2007-07-18 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10110023

## Citation

> US National Institutes of Health, RePORTER application 10110023, Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CT (5R01HL089765-11). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10110023. Licensed CC0.

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