Stroke, IschemicNCT06066593
Prediction Model for Chronic Intracranial Arterial Occlusion With Radiomic Features
An observational study of Stroke, Ischemic and Stroke Hemorrhagic, run by Beijing Hospital at 2 sites in China.
Trial summary
Chronic intracranial arterial occlusion is associated with a "bidirectional stroke risk," with a significantly increased risk of both ischemic stroke and cerebral hemorrhage. Currently, Western CTAP products, in combination with clinical expertise, offer some predictive value for assessing the risk of ischemic events… Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Stroke, Ischemic, Stroke Hemorrhagic
- How many people
- 1,000 participants
- Where
- 2 sitesChina
- Study dates
- 1 Aug 2023to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Training Cohort Selection: 600 patients with chronic intracranial arterial occlusion who had been confirmed to have experienced new-onset intracranial stroke through cranial MRI, with 200 experienced ischemic strokes, 200 experienced hemorrhagic strokes, and 200 asymptomatic patients. Test Cohort Selection: 400 patients with chronic intracranial arterial occlusion who presented with either no symptoms or isolated TIAs.
Who may be able to join
- Diagnosed with chronic intracranial arterial occlusion.
- Asymptomatic or with a history of only transient ischemic attacks (TIAs).
- The age of the patient falls within the range of 18 to 80 years old.
- Willing to undergo both perfusion CT scans and magnetic resonance imaging (MRI).
Who cannot join
- Acute intracranial arterial occlusive disease.
- History of either ischemic or hemorrhagic stroke.
- Allergies to contrast agents or conditions such as claustrophobia that prevent completion of imaging data acquisition.
Trial locations and contacts
2 sites in 1 country.
China2 sites, 2 recruiting
- Recruiting
Beijing Tiantan Hospital
Beijing, Beijing Municipality, 100070
Yuanren Zhai, Doctor, 18610638233, zhaiyr3998@aliyun.com
- Recruiting
Beijing Hospital
Beijing, Beijing Municipality, 100730
Shaosen Zhang, Doctor, +8618611284839, longyoubafang@hotmail.com
Dong Zhang, Doctor
About this study
Official title: Developing and Validating a Prediction Model for Bidirectional Stroke Risk in Chronic Intracranial Arterial Occlusion Based on Multimodal CT Radiomic Features of Key Brain Regions.
Chronic intracranial arterial occlusion is associated with a "bidirectional stroke risk," with a significantly increased risk of both ischemic stroke and cerebral hemorrhage. Currently, Western CTAP products, in combination with clinical expertise, offer some predictive value for assessing the risk of ischemic events by evaluating compensatory pathways and overall perfusion in chronic intracranial arterial occlusion. However, there is limited support for assessing the risk of hemorrhagic events.
Our proposed project aims to address a significant scientific challenge: the precise assessment of long-term stroke risk in asymptomatic patients with chronic intracranial arterial occlusion using a machine learning-based approach. The rapidly advancing field of machine learning provides a rich set of solutions for tackling this problem. In this project, we intend to develop a deep learning-based segmentation model for key brain regions using multimodal CT scans. Subsequently, we will automate the extraction of radiomic features and CT perfusion parameters, followed by the application of machine learning techniques to construct a stroke risk prediction model tailored for patients with chronic intracranial arterial occlusion.
Read the detailed description
- Establishment of a Brain Segmentation Model for Key Brain Regions Based on Multimodal CT The establishment of a brain segmentation model for key brain regions based on multimodal CT is one of the core components of this research. The project plans to use advanced algorithms such as U-Net and Transformer as the foundation to develop a model for automatically segmenting key brain regions in CT images. These key brain regions include the basal ganglia, frontal lobe, temporal lobe, parietal lobe, and others. The development of the automatic segmentation model consists of two main parts: training and validation. Retrospective cohort data from this research are used to constitute the training set. Specific brain regions are manually annotated to serve as training materials for the model. Parameter tuning and internal validation are conducted to achieve satisfactory training results. Prospective cohort data from this research form an independent validation set used for external validation of the model. The performance evaluation criteria for the model include the DICE coefficient and loss value.
- Exploration of Core Neuroimaging Features Related to Stroke in Patients with Chronic Intracranial Arterial Occlusion Using masks generated by the automatic segmentation model, this study intends to extract radiomic information and CT perfusion parameters of specific brain regions through a self-designed automated workflow. These features will be used to construct a machine learning predictive model for stroke risk in patients with chronic intracranial arterial occlusion. To select the most effective features for stroke prediction, a feature selection and dimensionality reduction process is required. This study plans to use L1 regularization to reduce the dimensionality of the standardized data, aiming to optimize the ROC area under the curve (AUC). Thus, it is possible to explore and refine core neuroimaging features related to stroke in patients with intracranial arterial occlusion, analyze their correlation with stroke, and use them for subsequent model construction.
- Construction and Validation of a Stroke Prediction Model Based on Core Neuroimaging Features of Key Brain Regions
The construction and validation of the prediction model are divided into training and validation phases. The training data are collected from the training cohort and internally validated using cross-validation. The prospective cohort remains an independent validation set for external validation of the model. Model construction begins with testing basic machine learning models' performance. By analyzing the performance of these basic models, the architecture of an ensemble learning model is designed and constructed. Finally, the ensemble learning model is validated. The primary performance metric for the model is the area under the ROC curve (AUC), and additional evaluation metrics such as accuracy, recall, precision, and F1 score are used for supplementary assessment of performance.
- Conditions
- Stroke, Ischemic, Stroke Hemorrhagic
- Keywords
- Chronic intracranial arterial occlusion, Multimodal CT, Radiomics, Machine Learning, Deep Learning
- Medical subject headings
- Ischemic Stroke, Hemorrhagic Stroke
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Ischemic Stroke | MRI suggests a new cerebral infarction. | |
| Hemorrhagic Stroke | CT suggests a new cerebral hemorrhage. | |
| Asymptomatic Group | Without new infarction or hemorrhage |
What is being measured
The primary outcome is the main question the study is designed to answer.
Stroke
The patient cohort requires the collection of both CT and MRI imaging data, including various modalities:
CT Imaging: CT Plain Scan, CT Angiography, CT Perfusion MRI Imaging: T1-Weighted Imaging, T2-Weighted Imaging, DWI, FLAIR
Follow-up evaluations should be conducted annually to record the occurrence of either hemorrhagic or ischemic stroke events. This involves monitoring and documenting whether any stroke events (hemorrhagic or ischemic) have occurred in the patient cohort during the follow-up period.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Oct 2023
- Study start1 Aug 2023
- Primary completion1 Aug 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Stroke, IschemicStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Beijing Hospital
- Sponsor type
- Other government
- Collaborators
- Beijing Tiantan Hospital, Chinese Academy of Sciences
- Responsible party
- Sponsor
- Investigators
- Dong Zhang, Doctor, Study chair, Beijing Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06066593
- Sponsor's protocol ID
- 2023BJYYEC-239-01
- Status last verified
- Sep 2023
- First submitted
- 21 Sep 2023
- Last updated
- 4 Oct 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: The data that support the findings of this study are available from the corresponding author upon reasonable request.
Study at a glance
From the registry record, last updated by the sponsor on 4 Oct 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Developing and Validating a Prediction Model for Bidirectional Stroke Risk in Chronic Intracranial Arterial Occlusion Based on Multimodal CT Radiomic Features of Key Brain Regions.
- ClinicalTrials.gov ID
- NCT06066593
- Acronym / sponsor ID
- 2023BJYYEC-239-01
- Conditions
- Stroke, Ischemic, Stroke Hemorrhagic
- Keywords
- Chronic intracranial arterial occlusion, Multimodal CT, Radiomics, Machine Learning, Deep Learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Beijing HospitalOther government
- Collaborators
- Beijing Tiantan Hospital, Chinese Academy of Sciences
- Study start
- 1 Aug 2023 (actual)
- Primary completion
- 1 Aug 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 4 Oct 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Chronic intracranial arterial occlusion is associated with a "bidirectional stroke risk," with a significantly increased risk of both ischemic stroke and cerebral hemorrhage. Currently, Western CTAP products, in combination with clinical expertise, offer some predictive value for assessing the risk of ischemic events by evaluating compensatory pathways and overall perfusion in chronic intracranial arterial occlusion. However, there is limited support for assessing the risk of hemorrhagic events.
Our proposed project aims to address a significant scientific challenge: the precise assessment of long-term stroke risk in asymptomatic patients with chronic intracranial arterial occlusion using a machine learning-based approach. The rapidly advancing field of machine learning provides a rich set of solutions for tackling this problem. In this project, we intend to develop a deep learning-based segmentation model for key brain regions using multimodal CT scans. Subsequently, we will automate the extraction of radiomic features and CT perfusion parameters, followed by the application of machine learning techniques to construct a stroke risk prediction model tailored for patients with chronic intracranial arterial occlusion.
Detailed description
- Establishment of a Brain Segmentation Model for Key Brain Regions Based on Multimodal CT The establishment of a brain segmentation model for key brain regions based on multimodal CT is one of the core components of this research. The project plans to use advanced algorithms such as U-Net and Transformer as the foundation to develop a model for automatically segmenting key brain regions in CT images. These key brain regions include the basal ganglia, frontal lobe, temporal lobe, parietal lobe, and others. The development of the automatic segmentation model consists of two main parts: training and validation. Retrospective cohort data from this research are used to constitute the training set. Specific brain regions are manually annotated to serve as training materials for the model. Parameter tuning and internal validation are conducted to achieve satisfactory training results. Prospective cohort data from this research form an independent validation set used for external validation of the model. The performance evaluation criteria for the model include the DICE coefficient and loss value.
- Exploration of Core Neuroimaging Features Related to Stroke in Patients with Chronic Intracranial Arterial Occlusion Using masks generated by the automatic segmentation model, this study intends to extract radiomic information and CT perfusion parameters of specific brain regions through a self-designed automated workflow. These features will be used to construct a machine learning predictive model for stroke risk in patients with chronic intracranial arterial occlusion. To select the most effective features for stroke prediction, a feature selection and dimensionality reduction process is required. This study plans to use L1 regularization to reduce the dimensionality of the standardized data, aiming to optimize the ROC area under the curve (AUC). Thus, it is possible to explore and refine core neuroimaging features related to stroke in patients with intracranial arterial occlusion, analyze their correlation with stroke, and use them for subsequent model construction.
- Construction and Validation of a Stroke Prediction Model Based on Core Neuroimaging Features of Key Brain Regions
The construction and validation of the prediction model are divided into training and validation phases. The training data are collected from the training cohort and internally validated using cross-validation. The prospective cohort remains an independent validation set for external validation of the model. Model construction begins with testing basic machine learning models' performance. By analyzing the performance of these basic models, the architecture of an ensemble learning model is designed and constructed. Finally, the ensemble learning model is validated. The primary performance metric for the model is the area under the ROC curve (AUC), and additional evaluation metrics such as accuracy, recall, precision, and F1 score are used for supplementary assessment of performance.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Ischemic Stroke | – | –MRI suggests a new cerebral infarction. |
| Hemorrhagic Stroke | – | –CT suggests a new cerebral hemorrhage. |
| Asymptomatic Group | – | –Without new infarction or hemorrhage |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Training Cohort Selection: 600 patients with chronic intracranial arterial occlusion who had been confirmed to have experienced new-onset intracranial stroke through cranial MRI, with 200 experienced ischemic strokes, 200 experienced hemorrhagic strokes, and 200 asymptomatic patients. Test Cohort Selection: 400 patients with chronic intracranial arterial occlusion who presented with either no symptoms or isolated TIAs.
Key inclusion criteria (4 listed)
- Diagnosed with chronic intracranial arterial occlusion.
- Asymptomatic or with a history of only transient ischemic attacks (TIAs).
- The age of the patient falls within the range of 18 to 80 years old.
- Willing to undergo both perfusion CT scans and magnetic resonance imaging (MRI).
Key exclusion criteria (3 listed)
- Acute intracranial arterial occlusive disease.
- History of either ischemic or hemorrhagic stroke.
- Allergies to contrast agents or conditions such as claustrophobia that prevent completion of imaging data acquisition.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Stroke
Time frame: 1 year
The patient cohort requires the collection of both CT and MRI imaging data, including various modalities: CT Imaging: CT Plain Scan, CT Angiography, CT Perfusion MRI Imaging: T1-Weighted Imaging, T2-Weighted Imaging, DWI, FLAIR Follow-up evaluations should be conducted annually to record the occurrence of either hemorrhagic or ischemic stroke events. This involves monitoring and documenting whether any stroke events (hemorrhagic or ischemic) have occurred in the patient cohort during the follow-up period.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Oct 2023.
- Individual participant dataSharing planned: Undecided. The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Dong Zhang, Doctor study chair
Beijing Hospital
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Stroke, Ischemic and Stroke Hemorrhagic, run by Beijing Hospital at 2 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 4 Oct 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Shaosen Zhang, Doctor (18611284839, longyoubafang@hotmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Stroke (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Oct 2023.