# Classification and Prognostication in Pulmonary Thromboembolism Using Computed Tomography Image Analytics

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2022 · $768,679

## Abstract

Project Summary
Pulmonary thromboembolism remains a significant cause of morbidity and mortality in the western world. While
many of the initial symptoms in acute pulmonary embolism (PE) resolves with appropriate treatment, there is
increasing awareness of chronic impact of the disease ranging from development of chronic thromboembolic
pulmonary hypertension (CTEPH) to persisting dyspnea and exercise impairment. Many patients initially
diagnosed with PE may already have chronic disease and inappropriate treatment for acute disease in these
cases may be harmful and delay referral to specialized centers with experience in treating chronic disease. On
the other hand many patients with acute PE go on to develop chronic disease despite current treatment
options and follow-up to insure resolution remains a challenge particularly without the ability to predict who will
develop chronic disease. Furthermore, prognostication and selection of treatments can be difficult, particularly
in submassive acute PE and CTEPH, particularly with newly emerging treatment choices. Quantitative
methods are needed to help define disease trajectories early in presentation, help guide prognostication and
treatment and improve our understanding of the pathophysiology of this condition.
Computed Tomography (CT) imaging is the cornerstone of evaluation of pulmonary thromboembolism. In
acute PE, it is the often the first imaging modality available for assessing treatment options. As the patient
recovers, it is used to detect chronic or reoccurring clot guide interventions in chronic disease. Advances in CT
imaging quality, image processing (including application of deep learning), coupled with increasing
computation power make possible the extraction of a large number of novel features from CT imaging. In this
proposal we seek to combine our team’s experience in CT image quantification with multi-center longitudinal
data to develop CT imaging features that can identify and predict disease chronicity, its impact on the
pulmonary circulation and its response to treatment. In Aim 1 we utilize longitudinal data from three academic
hospitals (Brigham and Women’s Hospital, Massachusetts General Hospital, Northwestern University) to
assess CT features at presentation that predict the presence or development of chronic disease. In Aim 2, we
study both the presentation and follow-up image to build quantitative models of the impact of acute and chronic
disease on the pulmonary circulation in order to help with prognostication and improve non-invasive methods
of predicting the relevance of persistent disease to the clinical state of patients. In aim 3 we use a combination
of longitudinal imaging in CTEPH patients having undergone surgery and patients with pulmonary arterial
hypertension to identify patients that would have the most optimal surgical outcomes. We believe that the
combination of the features and models developed in these complementary aims will advance our ability to use
cli...

## Key facts

- **NIH application ID:** 10502716
- **Project number:** 1R01HL164717-01
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Farbod Nicholas Rahaghi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $768,679
- **Award type:** 1
- **Project period:** 2022-09-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10502716

## Citation

> US National Institutes of Health, RePORTER application 10502716, Classification and Prognostication in Pulmonary Thromboembolism Using Computed Tomography Image Analytics (1R01HL164717-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10502716. Licensed CC0.

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