Digital Modeling of Thoracic CT and Pulmonary Fibrosis

Active, not recruiting

• Conditions: Inspiration Expiration Thoracic TDM Sequences of Patients With Diffuse Interstitial Lung Disease (DIP)

• Registration Number: NCT06618924
• Lead Sponsor: Assistance Publique - Hôpitaux de Paris

Brief Summary

Currently, to our knowledge, there is little data on the combination of tools based on a similar concept to understand and evaluate ILDs. It is expected that this portfolio of multi-tool software implemented in radiology departments, applied to routine thoracic TDM, will provide additional qualitative and quantitative information in real time that will be of great help for diagnosis, prognosis prediction, and treatment decision-making in ILDs.

Detailed Description

Thoracic CT scanning has revolutionized the definition of interstitial lung diseases (ILDs), some of which inexorably progress to pulmonary fibrosis (e.g., progressive pulmonary fibrosis or PPF), leading to early death or lung transplantation. Over the past decade, various treatments have shown effectiveness in slowing this fibrotic progression, but it is still not possible to define which patients might personally benefit from these treatments and when to prescribe them. Two major questions remain:

Why do some patients develop fibrosis despite seemingly appropriate treatment? What are the mechanisms driving this fibrotic progression? Hence, there is a great need to define biomarkers to answer these questions, particularly in the early phase. For more than 5 years, within a consortium including Avicenne Hospital APHP 93000 Bobigny, INSERM Unit 1272 Sorbonne Paris North University, and two partner laboratories (Mines Telecom and Ecole Polytechnique-INRIA, both belonging to the Institut Polytechnique), we have been developing the applications of artificial intelligence (AI) to lung imaging, extracting static and dynamic data from thoracic CT scans to aid in the diagnosis and follow-up of patients without additional examinations beyond standard care. Our project\'s objective is to identify patients at risk of progressive and irreversible fibrosis and those who could respond to antifibrotic treatments, by developing the identification of qualitative and quantitative biomarkers from the numerical modeling of routine thoracic CT scans.

Our program, which has just been funded in 2023 by the National Research Agency (ANR 2023 MLQ-CT), aims to:

Develop a portfolio of software tools, whose use should be facilitated in the hospital sector based on research prototypes already built and tested in our consortium for several years.

Apply them to a set of interstitial lung diseases (ILDs) known to be at risk of fibrotic progression.

Transfer these tools to the radiology department of Avicenne Hospital APHP. Conduct real-time experimentation between two pulmonology departments, one at Avicenne Hospital APHP and the other at Caen University Hospital, and the radiology department of Avicenne Hospital APHP, to validate the feasibility of using such biomarkers.

Study Timeline

• Study Start: 2024-07-01
• Status Verified: 2024-09
• Study First Submitted: 2024-09-16
• Study First Submitted QC: 2024-09-27
• Last Update Submitted: 2024-09-27
• Study First Posted: 2024-09-30
• Last Update Posted: 2024-09-30
• Primary Completion: 2029-07-01
• Study Completion: 2030-07-01

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 300

Inclusion Criteria

Not provided

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Exclusion Criteria

Not provided

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Qualitative and quantitative parameters obtained by in silico modeling of interstitial pattern, intra-pulmonary vascular remodeling and airway remodeling.	At 12 months after initial CT scan	Identify patients at risk of progressive and irreversible fibrosis and those who could respond to antifibrotic treatments, by developing the identification of qualitative and quantitative biomarkers from the numerical modeling of routine thoracic CT scans.
Specific mechanical parameters obtained by dynamic poromechanical modeling obtained by dynamic poromechanical modeling.	At 12 months after initial CT scan	Identify patients at risk of progressive and irreversible fibrosis and those who could respond to antifibrotic treatments, by developing the identification of qualitative and quantitative biomarkers from the numerical modeling of routine thoracic CT scans.

Secondary Outcome Measures

Name	Time	Method
indexes of interstitial pattern score	At 12 months after initial CT scan	* Bring the various software products in this portfolio up to Technology Readiness Level (TRL) 6 in order to create a prototype based on this portfolio; that can be implanted in a hospital environment; * test the feasibility of inter-connecting the radiology and pneumology departments of two university hospitals to this prototype, implanted in the radiology department of the Hôpital Avicenne APHP.
intra-pulmonary vascular remodeling and airway remodeling score	At 12 months after initial CT scan	* Bring the various software products in this portfolio up to Technology Readiness Level (TRL) 6 in order to create a prototype based on this portfolio; that can be implanted in a hospital environment; * test the feasibility of inter-connecting the radiology and pneumology departments of two university hospitals to this prototype, implanted in the radiology department of the Hôpital Avicenne APHP.
poromechanical stiffness score	At 12 months after initial CT scan	* Bring the various software products in this portfolio up to Technology Readiness Level (TRL) 6 in order to create a prototype based on this portfolio; that can be implanted in a hospital environment; * test the feasibility of inter-connecting the radiology and pneumology departments of two university hospitals to this prototype, implanted in the radiology department of the Hôpital Avicenne APHP.

Trial Locations

Locations (1)

Hôpital Avicenne APHP; 🇫🇷 Bobigny, France