Chinese PE Multimodality Imaging Artificial Intelligence Study

NCT ID: NCT06526468

Last Updated: 2024-08-28

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 1500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2010-09-01
• Study Completion Date: 2028-09-01

Brief Summary

The CHinese pulmOnary Embolism Multimodality Imaging-artifiCial intelligencE Study (CHOICE) is a prospective observational multi-center study that will collect imaging text data and raw data of patients with pulmonary embolism (PE) in China. By combining artificial intelligence technology, it aims to identify imaging markers to assist in early diagnosis, differential diagnosis, risk stratification, and prognosis assessment of PE.

Detailed Description

Pulmonary embolism (PE) represents a significant public health issue. Timely diagnosis and treatment during the acute phase, as well as appropriate long-term follow-up strategies, are crucial for the management of PE. PE is classified into three stages based on disease course: acute pulmonary embolism (APE), chronic thromboembolic pulmonary disease (CTEPD), and chronic thromboembolic pulmonary hypertension (CTEPH). APE can cause acute right ventricular failure and death if not diagnosed and treated early. CTEPD has the potential to significantly impair patients' quality of life. CTEPH is a rare and potentially life-threatening long-term sequelae of PE, characterized by persistent obstruction of pulmonary arteries by organized clots, leading to redistribution of blood flow and secondary remodeling of the pulmonary microvasculature. Early identification of PE and implementation of targeted treatment plans will significantly improve survival rates and prognosis.

Multimodal imaging tests play a crucial role in the management of PE (including computed tomography pulmonary angiography (CTPA), magnetic resonance imaging (MRI), echocardiography, and lung ventilation/perfusion (V/Q) scan). The guidelines have identified the right ventricle to left ventricle (RV:LV) ratio >1.0 on CTPA or right heart dysfunction signs from echocardiography as important indicators for risk stratification of APE. Patients stratified as high risk require closer monitoring in an inpatient setting. Whereas, those stratified as low risk are suitable for early discharge.

Therefore, exploring novel imaging markers and integrating these markers into radiology reports may have potential clinical significance. If no quantifiable evidence of right ventricular dysfunction is provided to clinicians to make treatment decisions, patients with high-risk APE may be considered "low-risk" and discharged home. In addition, patients with low-risk APE may require longer hospital stays and may not need to be hospitalized, which undoubtedly increases healthcare costs. For patients with CTEPD or CTEPH, treatment options are diverse, including multimodal therapies such as pulmonary endarterectomy, balloon pulmonary angioplasty and targeted medical therapy. Therefore, multimodal imaging evaluation is meaningful for clinical treatment decision-making and efficacy monitoring. Combined with artificial intelligence (AI) technology, it can provide a variety of metrics to assist in evaluating clots morphology, pulmonary ventilation-perfusion function, cardiac function, hemodynamics, and more. AI can not only assist in finding more clinically significant imaging biomarkers but also customize standardized radiology reports, which are expected to address the current challenges.

This study is a multi-center real-world study aimed at exploring novel imaging markers in combination with AI technology and integrating them into a software for clinical application to provide quantitative parameters, using imaging reports and raw data from Chinese patients with PE. It is hypothesized that AI technology can improve early diagnosis, differential diagnosis, risk stratification, and management of PE by increasing the ability to accurately evaluate PE in a real-world clinical setting. The researchers also hypothesized that the integration of AI technologies would be cost-effective and acceptable to radiologists and clinicians.

Conditions

Pulmonary Embolism; Chronic Thromboembolic Pulmonary Hypertension; Chronic Thromboembolic Pulmonary Disease

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Acute pulmonary embolism cohort

1. Patients objectively confirmed acute symptomatic PE or PE with deep vein thrombosis (DVT)

2. PE was confirmed by CTPA, lung V/Q scan or pulmonary angiography.

Artificial Intelligence

Intervention Type: DEVICE

AI technology will provide novel imaging markers and generate a radiology report with relevant key slice imaging and evaluation results

Chronic thromboembolic pulmonary disease without pulmonary hypertension cohort

1. Patients with functional impairment despite 3 months of adequate anticoagulation therapy after APE.

2. CTPA/ pulmonary angiography or V/Q scan showed unresolved thrombi in the pulmonary vessels.

3. Without pulmonary hypertension at rest(mean pulmonary arterial pressure (mPAP) <20 mmHg), as measured by right heart catheterization.

Artificial Intelligence

Intervention Type: DEVICE

AI technology will provide novel imaging markers and generate a radiology report with relevant key slice imaging and evaluation results

Chronic thromboembolic pulmonary hypertension cohort

1. Patients with functional impairment despite 3 months of adequate anticoagulation therapy

2. CTPA/ pulmonary angiography or V/Q scan showed unresolved thrombi in the pulmonary vessels.

3. With pulmonary hypertension at rest (mean pulmonary arterial pressure (mPAP) >20 mmHg), as measured by right heart catheterization.

Artificial Intelligence

Intervention Type: DEVICE

AI technology will provide novel imaging markers and generate a radiology report with relevant key slice imaging and evaluation results

Other pulmonary vascular disease cohort

Patients diagnosed with other pulmonary vascular disease including Takayasu arteritis, pulmonary artery sarcoma, and fibrosing mediastinitis.

Artificial Intelligence

Intervention Type: DEVICE

AI technology will provide novel imaging markers and generate a radiology report with relevant key slice imaging and evaluation results

Interventions

Artificial Intelligence

AI technology will provide novel imaging markers and generate a radiology report with relevant key slice imaging and evaluation results

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• 14 Years and older
• Patients suspected of PE

Exclusion Criteria

• Pregnant women
• Refuse to follow up
• Incomplete or discontinued imaging scans
• Insufficient quality of image data to allow for analysis

• Minimum Eligible Age: 14 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

China-Japan Friendship Hospital

OTHER

Sponsor Role: lead

Responsible Party

Zhenguo Zhai,MD,PhD

Clinical Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Zhenguo Zhai, PhD

Role: STUDY_DIRECTOR

China-Japan Friendship Hospital

Locations

China-Japan Frendship hospital

Beijing, , China

Site Status: RECRUITING

Countries

China

Central Contacts

Min Liu, PhD

Role: CONTACT

mikie0763@126.com

+86-10-84205056

Facility Contacts

Min Liu, PhD

Role: primary

mikie0763@126.com

+86-10-84205056

Zhenguo Zhai, PhD

Role: backup

zhaizhenguo2011@126.com

+86-10-84206269

References

Xi L, Wang J, Liu A, Ni Y, Du J, Huang Q, Li Y, Wen J, Wang H, Zhang S, Zhang Y, Zhang Z, Wang D, Xie W, Gao Q, Cheng Y, Zhai Z, Liu M. Development of a lung perfusion automated quantitative model based on dual-energy CT pulmonary angiography in patients with chronic pulmonary thromboembolism. Insights Imaging. 2025 Aug 18;16(1):182. doi: 10.1186/s13244-025-02067-6.

Reference Type: DERIVED

PMID: 40825922 (View on PubMed)

Other Identifiers

CHOICE

Identifier Type: -

Identifier Source: org_study_id