Related Experiment Video
Updated: Sep 1, 2025
Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images
Published on: November 20, 2015
Identification of high-risk intracranial plaques with 3D high-resolution magnetic resonance imaging-based radiomics
Hongxia Li1, Jia Liu1, Zheng Dong2
1Department of Medical Imaging, The First School of Clinical Medicine, Jinling Hospital, Southern Medical University, Nanjing, 210002, Jiangsu, China.
Insights
A new radiomics model using 3D high-resolution magnetic resonance imaging (HRMRI) accurately identifies high-risk intracranial plaques. This advanced model significantly outperforms conventional methods in predicting stroke risk.
Area of Science:
- Medical Imaging
- Radiology
- Artificial Intelligence in Medicine
Background:
- Identifying high-risk intracranial plaques is crucial for stroke prevention and treatment.
- Symptomatic intracranial artery stenosis poses a significant stroke risk.
Purpose of the Study:
- To develop a high-risk intracranial plaque model using radiomics features from 3D high-resolution magnetic resonance imaging (HRMRI).
- To evaluate the performance of machine learning models in differentiating symptomatic from asymptomatic plaques.
Main Methods:
- 136 patients with symptomatic intracranial artery stenosis underwent HRMRI.
- Radiomics features were extracted from T1-weighted and contrast-enhanced T1-weighted images.
- A linear support vector classification (SVC) model was trained using radiomics features, and compared to a conventional model based on radiological characteristics.
Main Results:
- The radiomics model achieved an AUC of 0.923 (training) and 0.906 (test).
- The radiomics model significantly outperformed the conventional model (AUC 0.853 training, 0.837 test).
- A combined model incorporating radiological and radiomics features showed comparable performance to the radiomics model.
Conclusions:
- The radiomics model based on 3D HRMRI accurately differentiates symptomatic from asymptomatic intracranial arterial plaques.
- This radiomics approach offers a significant improvement over conventional methods for high-risk plaque identification.
Background:
Identifying high-risk intracranial plaques is significant for the treatment and prevention of stroke.
Objective:
To develop a high-risk plaque model using three-dimensional (3D) high-resolution magnetic resonance imaging (HRMRI) based radiomics features and machine learning.
Methods:
136 patients with documented symptomatic intracranial artery stenosis and available HRMRI data were included. Among these patients, 136 and 92 plaques were identified as symptomatic and asymptomatic plaques, respectively. A conventional model was developed by recording and quantifying the radiological plaque characteristics. Radiomics features from T1-weighted images (T1WI) and contrast-enhanced T1WI (CE-T1WI) were used to construct a high-risk plaque model with linear support vector classification (linear SVC). The radiological and radiomics features were combined to build a combined model. Receiver operating characteristic (ROC) curves were used to evaluate these models.
Results:
Plaque length, burden, and enhancement were independently associated with clinical symptoms and were included in the conventional model, which had an AUC of 0.853 vs. 0.837 in the training and test sets. While the radiomics and the combined model showed an improved AUC: 0.923 vs. 0.925 for the training sets and 0.906 vs. 0.903 in the test sets. Both the radiomics model (p = 0.024, p = 0.018) and combined model (p = 0.042, p = 0.049) outperformed the conventional model in the two sets, whereas the performance of the combined model was not significantly different from that of the radiomics model in the two sets (p = 0.583 and p = 0.606).
Conclusion:
The radiomics model based on 3D HRMRI can accurately differentiate symptomatic from asymptomatic intracranial arterial plaques and significantly outperforms the conventional model.
More Related Videos
12:50Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
Published on: April 14, 2014
09:06Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
Published on: June 9, 2018