Non-Contrast CT-Based Radiomics Score for Predicting Hematoma Enlargement in Spontaneous Intracerebral Hemorrhage
Non-Contrast CT-Based Radiomics Score for Predicting Hematoma Enlargement in Spontaneous Intracerebral Hemorrhage
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DOI：
10.1007/s00062-021-01062-w
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发表时间：
2021-07-29
影响因子：
2.8
通讯作者：
Wang, Xiang
中科院分区：
文献类型：
--
作者：
Li, Hui;Xie, Yuanliang;Wang, Xiang
目的建立一种基于CT的放射组学评分方法，用于预测自发性脑出血患者早期血肿扩大的风险。方法收集258例急性自发性脑实质出血患者的临床资料。利用Radiomics软件对基线平扫CT图像上的血肿进行分割，并提取纹理特征。使用最小冗余和最大相关性（mRMR）和最小绝对收缩和选择算子（LASSO）来选择优化的特征子集，并计算放射组学评分。在训练队列中建立放射组学模型（基于放射组学评分）、放射组学列线图（放射组学评分结合基于临床因素）和临床模型（基于临床因素），并在测试队列中进行验证。对模型的鉴别、校准和临床实用性进行了评价。最后，进行亚组分析，以评估放射组学评分在特定出血部位的预测价值。结果放射组学评分由12个放射组学特征组成。放射组学模型和放射组学列线图均显示出良好的预测血肿扩大的性能（曲线下面积，AUC 0.83 [0.71-0.95]，AUC 0.82 [0.72，0.93]），并且均优于临床模型（AUC 0.66 [0.54-0.79]）。放射组学模型和放射组学诺模图显示出令人满意的校准和临床实用性检测血肿扩大。对于亚组分析，放射组学评分也显示了对不同部位血肿扩大的良好预测值（幕上、幕下、深部和脑叶的AUC分别为0.828、0.940、0.836和0.904）。结论基于CT平扫的放射组学评分可作为预测自发性脑出血患者血肿扩大的潜在生物标志物，对临床因素的预测具有较高的增量价值。
Purpose To develop a non-contrast computed tomography-(CT)-based radiomics score for predicting the risk of hematoma early enlargement in spontaneous intracerebral hemorrhage. Methods A total of 258 patients from a single-center database with acute spontaneous intracerebral parenchymal hemorrhage were collected. Radiomics software was explored to segment hematomas on baseline non-contrast CT images, and the texture features were extracted. Minimal Redundancy and Maximal Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO), were used to select optimized subset of features and radiomics score was calculated. The radiomics model (radiomics score-based), radiomics nomogram (radiomics score combined with clinical factors-based) and clinical model (clinical factors-based) were built in a training cohort and validated in a test cohort. The discrimination, calibration, and clinical usefulness of the models were evaluated. Finally, a subgroup analysis was performed to assess the predictive value of radiomics score in specific hemorrhage location. Results Radiomics score was composed of 12 radiomics features. The radiomics model and radiomics nomogram both showed good performance in predicting hematoma enlargement (area under the curve, AUC 0.83 [0.71-0.95], AUC 0.82 [0.72, 0.93]), and were both better than clinical model (AUC 0.66 [0.54-0.79]). The radiomics model and radiomics nomogram showed satisfactory calibration and clinical usefulness for detecting hematoma enlargement. For subgroup analysis, radiomics score also showed good predictive value for hematoma enlargement in different locations (AUC were 0.828, 0.940, 0.836 and 0.904, respectively, for supratentorial, subtentorial, deep and lobes). Conclusion A radiomics score based on non-contrast CT may be considered as a potential biomarker for prediction of hematoma enlargement in patients with spontaneous intracerebral hemorrhage (SICH), and it presented a high incremental value to clinical factors for hematoma enlargement prediction.