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

Official title

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

Overview

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.

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

Interventions

• 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

Primary outcome measures

• Assessing the feasibility of histological segmentation of the superficial femoral artery on preoperative microscanner using artificial intelligence [Time frame: 1 hour]

Eligibility criteria

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)

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation

N/A

Model

Single group

Masking

Open label

Primary purpose

Other

Study locations

France · 1 center

• Hôpitaux Universitaire de Strasbourg — Strasbourg

Identifiers

NCT: NCT06260488 · 8925

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗