Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China.
BMC Med Imaging. 2021 Oct 11;21(1):147. doi: 10.1186/s12880-021-00678-1.
Radiomics analysis is a newly emerging quantitative image analysis technique. The aim of this study was to extract a radiomics signature from the computed tomography (CT) imaging to determine the infarction onset time in patients with acute middle cerebral artery occlusion (MCAO).
A total of 123 patients with acute MCAO in the M1 segment (85 patients in the development cohort and 38 patients in the validation cohort) were enrolled in the present study. Clinicoradiological profiles, including head CT without contrast enhancement and computed tomographic angiography (CTA), were collected. The time from stroke onset (TFS) was classified into two subcategories: ≤ 4.5 h, and > 4.5 h. The middle cerebral artery (MCA) territory on CT images was segmented to extract and score the radiomics features associated with the TFS. In addition, the clinicoradiological factors related to the TFS were identified. Subsequently, a combined model of the radiomics signature and clinicoradiological factors was constructed to distinguish the TFS ≤ 4.5 h. Finally, we evaluated the overall performance of our constructed model in an external validation sample of ischemic stroke patients with acute MCAO in the M1 segment.
The area under the curve (AUC) of the radiomics signature for discriminating the TFS in the development and validation cohorts was 0.770 (95% confidence interval (CI): 0.665-0.875) and 0.792 (95% CI: 0.633-0.950), respectively. The AUC of the combined model comprised of the radiomics signature, age and ASPECTS on CT in the development and validation cohorts was 0.808 (95% CI: 0.701-0.916) and 0.833 (95% CI: 0.702-0.965), respectively. In the external validation cohort, the AUC of the radiomics signature was 0.755 (95% CI: 0.614-0.897), and the AUC of the combined model was 0.820 (95% CI: 0.712-0.928).
The CT-based radiomics signature is a valuable tool for discriminating the TFS in patients with acute MCAO in the M1 segment, which may guide the use of thrombolysis therapy in patients with indeterminate stroke onset time.
放射组学分析是一种新兴的定量图像分析技术。本研究的目的是从 CT 成像中提取放射组学特征,以确定急性大脑中动脉闭塞(MCAO)患者的发病时间。
本研究共纳入 123 例大脑中动脉 M1 段急性闭塞患者(发展队列 85 例,验证队列 38 例)。收集临床放射学特征,包括头部 CT 平扫和 CT 血管造影(CTA)。根据发病时间(TFS)分为两个亚组:≤4.5 h 和>4.5 h。对 CT 图像上的大脑中动脉(MCA)区域进行分割,以提取和评分与 TFS 相关的放射组学特征。此外,确定与 TFS 相关的临床放射学因素。随后,构建放射组学特征与临床放射学因素的联合模型,以区分 TFS≤4.5 h。最后,在大脑中动脉 M1 段急性 MCAO 的缺血性卒中患者的外部验证样本中评估我们构建模型的整体性能。
发展和验证队列中放射组学特征区分 TFS 的曲线下面积(AUC)分别为 0.770(95%置信区间:0.665-0.875)和 0.792(95%置信区间:0.633-0.950)。发展和验证队列中包含放射组学特征、年龄和 CT ASPECTS 的联合模型的 AUC 分别为 0.808(95%置信区间:0.701-0.916)和 0.833(95%置信区间:0.702-0.965)。在外部验证队列中,放射组学特征的 AUC 为 0.755(95%置信区间:0.614-0.897),联合模型的 AUC 为 0.820(95%置信区间:0.712-0.928)。
基于 CT 的放射组学特征是区分大脑中动脉 M1 段急性 MCAO 患者 TFS 的有价值工具,可能有助于指导发病时间不确定的患者进行溶栓治疗。