Identification of Neoatherosclerosis in ISR Patients Based on Artifical Intelligence

Study Overview
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Brief Summary
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Based on the large population of patients, in-stent restenosis (ISR) is still an important problem in the field of cardiovascular disease. How to reduce the incidence of ISR and the treatment of ISR has become the focus and hot spot. The 2018 ESC Guidelines for Cardiovascular Intervention recommends treatment of ISR under the guidance of intravascular ultrasound (IVUS), or optical coherent tomography (OCT). Circulation published a new Waksman ISR classification based on mechanisms and components of the restenosis tissue, which provides guidance for treatment strategy. Because of its good resolution, OCT makes it more accurate to distinguish the components of vascular tissue, thus providing a decision-making basis for interventional therapy. OCT examination can obtain the characteristics of the ISR more precisely. Neoatherosclerosis (NA), is one of the ISR types and accounts for more stent failure and target lesion failure than other types. Identification NA is important for decision-making of interventional therapy. However, the acquisition and analysis of OCT images not only need the digital angiography machine (DSA) equipped with the majority of hospitals, but also need professional OCT imaging equipment and technicians. Patients with severely CKD cannot bear OCT examination because of the large amount of contrast agent. OCT catheter is more than ten times the price of the CAG catheter. Therefore, identification of NA by the use of artificial intelligence (AI) is of significance to set therapeutic strategy for ISR patients, especially in patients with CKD. Our study retrospectively analyzed CAG images and OCT images of ISR patients obtained from Jan 1st,2015 to Oct 31st,2020. Identify NA by analyzing OCT images, build up U-net and V-net to analyze the CAG and OCT images, and finally build up an identification system of NA based on CAG images by AI. This study has been approved by Ethics Committee of Chinese PLA General Hospital (S2018-033-01)

Detailed Description
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Drug Eluting Stents (DES) reduce the rate of in-stent restenosis (ISR) to 3.6-10%. Based on the large population of patients, ISR is still an important problem in the field of cardiovascular disease. How to reduce the incidence of ISR and the treatment of ISR has become the focus and hot spot. The 2018 ESC Guidelines for Cardiovascular Intervention recommends treatment of ISR under the guidance of intravascular ultrasound (IVUS), or optical coherent tomography (OCT). The European Expert Consensus on Intravascular Imaging, published in 2018, recommends finding the underlying mechanisms of ISR through intravascular imaging guidance (IVUS or OCT), and determining therapeutic strategies based on the mechanisms. Circulation published a new Waksman ISR classification based on mechanisms and components of the restenosis tissue, which provides guidance of treatment strategy. The use of intravascular imaging to identify and classify the types and mechanisms is very important for ISR treatment strategy. Because of its good resolution, OCT makes it more accurate to distinguish the components of vascular tissue, thus providing a decision-making basis for interventional therapy. OCT examination can obtain the characteristics of ISR more precisely. Neoatherosclerosis (NA), is one of the ISR types and accounts for more stent failure and target lesion failure than other types. Identification of NA is important for decision-making of interventional therapy. However, the acquisition and analysis of OCT images not only need the digital angiography machine (DSA) equipped with the majority of hospitals, but also need professional OCT imaging equipment and technicians. Patients with severely CKD cannot bear OCT examination because of the large amount of contrast agent. OCT catheter is more than ten times the price of the CAG catheter. Therefore, identification of NA by the use of artificial intelligence (AI) is of significance to set therapeutic strategy for ISR patients, especially in patients with CKD. Our study retrospectively analyzed CAG images and OCT images of ISR patients obtained from Jan 1st,2015 to Jan 31st,2020. Offline OCT analysis was performed using dedicated software (Light Lab Imaging Inc, Westford, MA). All images were analyzed at every frame in the stents by 2 independent investigators, who were blinded to the angiographic and clinical findings. Identify NA by analyzing OCT images, build up U-net and V-net to analyze the CAG and OCT images, and finally build up an identification system of NA based on CAG images by AI. This study has been approved by Ethics Committee of Chinese PLA General Hospital (S2018-033-01)

Official Title
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Identification of Neoatherosclerosis in In-stent Restenosis Patients Based on Artifical Intelligence

Conditions
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In-stent Restenosis, Neo-atherosclerosis, Retrospective

Intervention / Treatment
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* Other: no interventin


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: - all gender 18ys old to 80ys old diagnosed of in-stent restenosis based on CAG both CAG images and OCT images were obtained in the same patient on the same day Exclusion Criteria: CAG images and OCT images were not obtained on the same day in the same patient low quality in CAG images low qualitiy in OCT images

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| CAG and OCT group<br>Images of CAG and OCT patients obtained from ISR patients were retrospectively collected and analyzed. | Other: no interventin<br>* Our stusy analysed the images obtained from ISR patients, no extra intervention was given based on this study.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The identification of NA | a neointima containing a diffuse border and a signal-poor region, with the struts underneath invisible because of the marked signal attenuation | through the study completion, an average of 3 years | 
| neovascularizaion | diameter 50-300um, cavity in the stent area, not connected with the vasular | through the study completion, an average of 3 years | 
| ISR segment in the CAG images | the segement in the stent area or within 5mm beside the stent,diameter stenosis rate>50% | through the study completion, an average of 3 years | 
| lipid-core arc | To quantify the circumferential extent of NA, the lipid-core arc was measured at a 0.2-mm interval throughout the segments showing NA. | through the study completion, an average of 3 years | 
| Thin-cap fibroatheroma-like neointima | defined as a neointima characterized by a fibrous cap thickness at the thinnest part of <65 μm and an angle of lipid-laden neointima of >180 degrees | through the study completion, an average of 3 years | 
| macrophage arc | measured at 0.2-mm intervals and divided into 5 groups: grade 0, no macrophages; grade 1, localized macrophage accumulation, <30 degrees; grade 2, clustered accumulation, ≥30 and <90 degrees; grade 3, clustered accumulation, ≥90 and <270 degrees; and grade 4, clustered accumulation, ≥270 degrees. | through the study completion, an average of 3 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Neoatherosclerosis, in-stent restenosis, Optical Coherence Tomography, angiography, artificial intelligence