Histological Segmentation of the Superficial Femoral Artery From Microscan to CT Using Artificial Intelligence (CTPred)

Histological Segmentation of the Superficial Femoral Artery From Microscan to CT Using Artificial Intelligence: a Feasibility Study (CTPred)

The femoropopliteal artery segment (FPAS) is one of the longest arteries in the human body, undergoing torsion, compression, flexion and extension due to lower limb movements. Endovascular surgery is considered to be the treatment of choice for the peripheral arterial disease, the results of which depend on the physiological forces on the arterial wall, the anatomy of the vessels and the characteristics of the lesions being treated. The atheromatous disease includes, in a simple way, 3 categories of plaques: calcified, fibrous, and lipidic. The study of these plaques and their differentiation in imaging and histology in the FPAS has already been the subject of research. To treat them, there are angioplasty balloons and stents with different designs and components, with different mechanical properties and different impregnated molecules.

There is no non-invasive method (imaging) to accurately differentiate lesions along the FPAS. The analysis is performed from the preoperative CT scan, but there are high-resolution scanners that allow a quasi-histological analysis of the tissue.

This microscanner can be used ex vivo. In the framework of a project, the learning algorithm was be créated (Convolutional Neural Networks) to automatically segment microscanner slices: after taking FPAS from amputated limbs, we correlated ex-vivo microscanner images of the arteries with their histology. The correlation was then performed manually between the microscanner images, and the histological sections obtained. the algorithm well be trained on these slices and validated its performance. The validation of the CT and microscanner concordance was the subject of scientific publications.

Study Overview

• Status: Recruiting
• Conditions: Peripheral Artery Disease; Femoropopliteal Stenosis
• Intervention / Treatment: Procedure: Endovascular surgery

Detailed Description

The aim of this study is to evaluate the technical feasibility of histological segmentation by the FPAS algorithm from CT. The results of this study will provide initial data to evaluate the interest of a subsequent larger scale study to validate the diagnostic capabilities of automated segmentation

• Study Type: Interventional
• Enrollment (Estimated): 20
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Salomé KUNTZ, Doctor; Phone Number: +31 3 69 55 01 98; Email: salome.kuntz@chru-strasbourg.fr

Study Locations

• France
  • Bas-Rhin
    • Strasbourg, Bas-Rhin, France, 67 091
      • Recruiting
      • Hopitaux Universitaire de Strasbourg
      • Contact: Salomé KUNTZ, Doctor; Phone Number: +31 3 69 55 01 98; Email: salome.kuntz@chru-strasbourg.fr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion criteria:

• Male or female of legal age
• Subject with a planned transfemoral amputation in the vascular surgery department of the Hôpitaux Universitaires de Strasbourg as standard care
• Subject with a CT as part of standard care
• Subject who has given his/her non-opposition to participate in the study

Exclusion criteria:

- Impossible to give the subject informed information (subject in emergency situation, difficulties in understanding)

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Other: transfemoral amputation; Subject with a planned transfemoral amputation in the vascular surgery department of the Hôpitaux Universitaires de Strasbourg as standard care	Procedure: Endovascular surgery; routine endovascular surgery and FPAS harvesting from amputated limbs to evaluate the technical feasibility of histological segmentation by the FPAS algorithm from CT

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Assessing the feasibility of histological segmentation of the superficial femoral artery on preoperative microscanner using artificial intelligence	Rate of slices (in %) for which segmentation is considered sufficient. The quality of segmentation will be assessed by the clinician using a Likert scale. Segmentation is considered sufficient if the scale is ≥ 3 and insufficient if it is < 3	1 hour

Collaborators and Investigators

• Sponsor: University Hospital, Strasbourg, France

Study record dates

Study Major Dates

• Study Start (Actual): March 15, 2024
• Primary Completion (Estimated): June 15, 2025
• Study Completion (Estimated): August 15, 2025

Study Registration Dates

• First Submitted: July 19, 2023
• First Submitted That Met QC Criteria: February 13, 2024
• First Posted (Actual): February 15, 2024

Study Record Updates

• Last Update Posted (Actual): April 25, 2025
• Last Update Submitted That Met QC Criteria: April 22, 2025
• Last Verified: April 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Atherosclerosis; Arteriosclerosis; Arterial Occlusive Diseases; Peripheral Vascular Diseases; Peripheral Arterial Disease
• Other Study ID Numbers: 8925

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No