Noncontrast CT-Based Deep Learning for Predicting Hematoma Expansion Risk in Patients with Spontaneous Intracerebral Hemorrhage
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 2,000
- 试验地点
- 1
- 主要终点
- Prediction of Hematoma Expansion
研究概览
简要总结
Hematoma expansion is an independent predictor of poor prognosis and early neurological deterioration in patients with spontaneous intracerebral hemorrhage. Early identification of high-risk patients and timely targeted medical interventions may provide a crucial opportunity to limit hematoma growth and improve neurological outcomes. This study aims to develop an end-to-end deep learning model based on noncontrast computed tomography images to predict the risk of hematoma expansion in patients with spontaneous intracerebral hemorrhage. This model could serve as a valuable risk stratification tool for patients with hematoma expansion, facilitating targeted treatment and providing clinicians with streamlined decision-making support in emergency situations.
详细描述
This project is planned to be implemented in four steps:
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Data Collection
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Selection of Study Subjects: Clinical and imaging data of patients with spontaneous intracerebral hemorrhage were retrospectively collected from multiple centers, including 500 cases in the hematoma expansion group and 1500 cases in the non-expansion group, totaling 2000 cases. Hematoma expansion (rHE) was defined as an absolute increase in ICH volume of ≥6 mL or a relative increase of ≥33%.
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Collection of Clinical Data: Includes patient age, gender, history of coronary heart disease, smoking, alcohol, hypertension, admission systolic and diastolic blood pressures, among others.
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CT Image Acquisition: Admission and follow-up CT images were obtained using spiral CT scanning with a slice thickness and interslice spacing of 5 mm.
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Segmentation of Hematoma Based on Non-contrast CT Images Two radiologists independently segmented the volume of interest of the entire brain hematoma lesion using ITK-SNAP software, manually outlining the lesion on each CT slice while avoiding the surrounding edema and normal brain tissue.
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Establishment of Automatic Hematoma Segmentation Model
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Primary, spontaneous (non-traumatic) intracerebral hemorrhage (ICH).
- •Age ≥ 18 years.
- •Baseline CT performed within 24 hours of ICH symptom onset or last seen well (LSW).
- •Follow-up CT within 72 hours.
排除标准
- •Secondary ICH caused by trauma, vascular anomalies (e.g., aneurysm, cavernous angioma, arteriovenous malformation), brain tumor, or hemorrhagic transformation in brain infarction.
- •Primary intraventricular hemorrhage (IVH).
- •Surgical treatment with external ventricular drain placement or craniotomy.
- •Obvious artifacts observed in CT images.
结局指标
主要结局
Prediction of Hematoma Expansion
时间窗: From the onset of ICH symptoms to 72 hours after baseline CT
Proportion of patients with hematoma expansion
次要结局
未报告次要终点
研究者
Qiang Yu
Sponsor-Investigator
First Affiliated Hospital of Chongqing Medical University