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.

中文翻译：

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基于非增强 CT 的放射组学评分可预测自发性脑出血中的血肿扩大

目的

开发基于非造影计算机断层扫描 (CT) 的放射组学评分，用于预测自发性脑出血中血肿早期扩大的风险。

方法

单中心数据库共收集258例急性自发性脑实质出血患者。探索放射组学软件在基线非造影 CT 图像上分割血肿，并提取纹理特征。使用最小冗余和最大相关性（mRMR）以及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（基于放射组学评分与临床因素相结合）和临床模型（基于临床因素），并在测试队列中进行验证。评估了模型的辨别力、校准和临床实用性。最后，进行亚组分析以评估放射组学评分对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在预测血肿扩大方面均表现出良好的性能（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列线图对于检测血肿扩大显示出令人满意的校准和临床实用性。对于亚组分析，放射组学评分对不同部位的血肿扩大也显示出良好的预测价值（幕上、幕下、深部和叶部的 AUC 分别为 0.828、0.940、0.836 和 0.904）。

结论

基于平扫CT的影像组学评分可被认为是预测自发性脑出血（SICH）患者血肿扩大的潜在生物标志物，并且它对血肿扩大预测的临床因素具有很高的增量价值。