Abstract 045: A Radiomics Based Pipeline for Paired Risk‐Stratification of Intracranial Atherosclerotic Plaques
Introduction High resolution vessel wall imaging (HR‐VWI) enables accurate visualization of intracranial atherosclerotic plaques. Radiomics can be utilized as an objective quantification method of plaque appearance and shape. We aimed to analyze the radiomics features (RFs) obtained from 7T‐HR‐VWI t...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-11-01
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| Series: | Stroke: Vascular and Interventional Neurology |
| Online Access: | https://www.ahajournals.org/doi/10.1161/SVIN.03.suppl_2.045 |
| _version_ | 1797221232626630656 |
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| author | Elena S. Sagues Sese Sebastian Sanchez Sricharan Veeturi Tatsat Patel Vicent M. Tutino Diego J. Ojeda Jacob M. Miller Arshaq Saleem Andres Gudino Bincheng Wang Edgar A. Samaniego |
| author_facet | Elena S. Sagues Sese Sebastian Sanchez Sricharan Veeturi Tatsat Patel Vicent M. Tutino Diego J. Ojeda Jacob M. Miller Arshaq Saleem Andres Gudino Bincheng Wang Edgar A. Samaniego |
| author_sort | Elena S. Sagues Sese |
| collection | DOAJ |
| description | Introduction High resolution vessel wall imaging (HR‐VWI) enables accurate visualization of intracranial atherosclerotic plaques. Radiomics can be utilized as an objective quantification method of plaque appearance and shape. We aimed to analyze the radiomics features (RFs) obtained from 7T‐HR‐VWI to differentiate between culprit and non‐culprit plaques in patients with intracranial atherosclerotic disease (ICAD). Methods Patients with ICAD as stroke etiology undergoing HR‐VWI were included in the study. Culprit plaques in the vascular territory of the stroke were identified. The degree of stenosis, area degree of stenosis and plaque burden were calculated. Three‐dimensional segmentation of the plaque was performed, and RFs were extracted. We then evaluated multiple machine learning models to predict and identify culprit plaques using significantly different RFs. The dataset was then randomly divided into training and testing datasets, and the trained model was evaluated on the independent testing dataset. Results The study included 33 patients with ICAD as the cause of stroke. Univariate analysis revealed 38 significantly different RFs between culprit and non‐culprit plaques in pre‐contrast MRI, 39 in post‐contrast MRI and 25 RFs that were different between pre and post contrast MRIs. Additionally, seven shape‐based RFs exhibited significant distinctions between the two plaque types. The random forest model achieved an accuracy rate of 81% (88% sensitivity and 75% specificity) in identifying culprit plaques in the independent testing dataset. This model successfully identified the culprit plaque in 7 out of 8 cases during the testing phase. Conclusion In this study symptomatic culprit plaques had a different signature RFs compared to other plaque within the same subject. A machine learning model built with RFs successfully identified the symptomatic atherosclerotic plaques in most cases. Radiomics is a promising tool for stratification of plaques in patients with ICAD. |
| first_indexed | 2024-03-07T15:38:56Z |
| format | Article |
| id | doaj.art-b67ef0230d994cc7b13d72ca88b17614 |
| institution | Directory Open Access Journal |
| issn | 2694-5746 |
| language | English |
| last_indexed | 2024-04-24T13:02:10Z |
| publishDate | 2023-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Stroke: Vascular and Interventional Neurology |
| spelling | doaj.art-b67ef0230d994cc7b13d72ca88b176142024-04-05T10:51:56ZengWileyStroke: Vascular and Interventional Neurology2694-57462023-11-013S210.1161/SVIN.03.suppl_2.045Abstract 045: A Radiomics Based Pipeline for Paired Risk‐Stratification of Intracranial Atherosclerotic PlaquesElena S. Sagues Sese0Sebastian Sanchez1Sricharan Veeturi2Tatsat Patel3Vicent M. Tutino4Diego J. Ojeda5Jacob M. Miller6Arshaq Saleem7Andres Gudino8Bincheng Wang9Edgar A. Samaniego10University of Iowa Hospitals and Clinics Iowa United StatesUniversity of Iowa Hospitals and Clinics Iowa United StatesUniversity at Buffalo New York United StatesUniversity at Buffalo New York United StateUniversity at Buffalo New York United StatesUniversity of Iowa Hospitals and Clinics Iowa United StatesUniversity of Iowa Iowa United StatesUniversity of Iowa Carver College of Medicine Iowa United StatesUniversity of Iowa Hospitals and Clinics Iowa United StatesUniversity of Iowa Hospitals and Clinics Iowa United StatesUniversity of Iowa Hospitals and Clinics Iowa United StatesIntroduction High resolution vessel wall imaging (HR‐VWI) enables accurate visualization of intracranial atherosclerotic plaques. Radiomics can be utilized as an objective quantification method of plaque appearance and shape. We aimed to analyze the radiomics features (RFs) obtained from 7T‐HR‐VWI to differentiate between culprit and non‐culprit plaques in patients with intracranial atherosclerotic disease (ICAD). Methods Patients with ICAD as stroke etiology undergoing HR‐VWI were included in the study. Culprit plaques in the vascular territory of the stroke were identified. The degree of stenosis, area degree of stenosis and plaque burden were calculated. Three‐dimensional segmentation of the plaque was performed, and RFs were extracted. We then evaluated multiple machine learning models to predict and identify culprit plaques using significantly different RFs. The dataset was then randomly divided into training and testing datasets, and the trained model was evaluated on the independent testing dataset. Results The study included 33 patients with ICAD as the cause of stroke. Univariate analysis revealed 38 significantly different RFs between culprit and non‐culprit plaques in pre‐contrast MRI, 39 in post‐contrast MRI and 25 RFs that were different between pre and post contrast MRIs. Additionally, seven shape‐based RFs exhibited significant distinctions between the two plaque types. The random forest model achieved an accuracy rate of 81% (88% sensitivity and 75% specificity) in identifying culprit plaques in the independent testing dataset. This model successfully identified the culprit plaque in 7 out of 8 cases during the testing phase. Conclusion In this study symptomatic culprit plaques had a different signature RFs compared to other plaque within the same subject. A machine learning model built with RFs successfully identified the symptomatic atherosclerotic plaques in most cases. Radiomics is a promising tool for stratification of plaques in patients with ICAD.https://www.ahajournals.org/doi/10.1161/SVIN.03.suppl_2.045 |
| spellingShingle | Elena S. Sagues Sese Sebastian Sanchez Sricharan Veeturi Tatsat Patel Vicent M. Tutino Diego J. Ojeda Jacob M. Miller Arshaq Saleem Andres Gudino Bincheng Wang Edgar A. Samaniego Abstract 045: A Radiomics Based Pipeline for Paired Risk‐Stratification of Intracranial Atherosclerotic Plaques Stroke: Vascular and Interventional Neurology |
| title | Abstract 045: A Radiomics Based Pipeline for Paired Risk‐Stratification of Intracranial Atherosclerotic Plaques |
| title_full | Abstract 045: A Radiomics Based Pipeline for Paired Risk‐Stratification of Intracranial Atherosclerotic Plaques |
| title_fullStr | Abstract 045: A Radiomics Based Pipeline for Paired Risk‐Stratification of Intracranial Atherosclerotic Plaques |
| title_full_unstemmed | Abstract 045: A Radiomics Based Pipeline for Paired Risk‐Stratification of Intracranial Atherosclerotic Plaques |
| title_short | Abstract 045: A Radiomics Based Pipeline for Paired Risk‐Stratification of Intracranial Atherosclerotic Plaques |
| title_sort | abstract 045 a radiomics based pipeline for paired risk stratification of intracranial atherosclerotic plaques |
| url | https://www.ahajournals.org/doi/10.1161/SVIN.03.suppl_2.045 |
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