Radiomics features on computed tomography reflect thrombus histological age prior to endovascular treatment of acute ischemic stroke

Chendong Wang; Tao Li; Zhenyu Jia; Kai Qiu; Runhao Jiang; Yu Hang; Heng Ni; Yuezhou Cao; Linbo Zhao; Mingfang Li; Jincheng Jiao; Haibin Shi; Jiulou Zhang; Sheng Liu

doi:10.1016/j.jstrokecerebrovasdis.2023.107358

Radiomics features on computed tomography reflect thrombus histological age prior to endovascular treatment of acute ischemic stroke

J Stroke Cerebrovasc Dis. 2023 Nov;32(11):107358. doi: 10.1016/j.jstrokecerebrovasdis.2023.107358. Epub 2023 Sep 14.

Authors

Chendong Wang¹, Tao Li², Zhenyu Jia¹, Kai Qiu¹, Runhao Jiang¹, Yu Hang¹, Heng Ni¹, Yuezhou Cao¹, Linbo Zhao¹, Mingfang Li³, Jincheng Jiao³, Haibin Shi¹, Jiulou Zhang⁴, Sheng Liu⁵

Affiliations

¹ Department of Interventional Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Rd Gulou District, Nanjing, Jiangsu 210029, China.
² Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Rd, Nanjing 210029, China.
³ Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Rd, Nanjing 210029, China.
⁴ Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Rd, Nanjing 210029, China.
⁵ Department of Interventional Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Rd Gulou District, Nanjing, Jiangsu 210029, China. Electronic address: liusheng@njmu.edu.cn.

PMID: 37716105
DOI: 10.1016/j.jstrokecerebrovasdis.2023.107358

Abstract

Purpose: To investigate the role of radiomics features in thrombus age identification and establish a CT-based radiomics model for predicting thrombus age of large vessel occlusion stroke patients.

Methods: We retrospectively reviewed patients with middle cerebral artery occlusion receiving mechanical thrombectomy from July 2020 to March 2022 at our center. The retrieved clots were stained with Hematoxylin and Eosin (H&E) and determined as fresh or older thrombi based on coagulation age. Clot-derived radiomics features were selected by least absolute shrinkage and selection operator (LASSO) regression analysis, by which selected radiomics features were integrated into the Rad-score via the corresponding coefficients. The prediction performance of Rad-score in thrombus age was evaluated with the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis.

Results: A total of 104 patients were included in our analysis, with 52 in training and 52 in validation cohort. Older thrombi were characterized with delayed procedure time, worse functional outcome and marginally associated with more attempts of device. We extracted 982 features from NCCT images. Following T test and LASSO analysis in training cohort, six radiomics features were selected, based on which the Rad-score was generated by the linear combination of features. The Rad-score showed satisfactory performance in distinguishing fresh with older thrombi, with the AUC of 0.873 (95 %CI: 0.777-0.956) and 0.773 (95 %CI: 0.636-0.910) in training and validation cohort, respectively.

Conclusion: This study established and validated a CT-based radiomics model that could accurately differentiate fresh with older thrombi for stroke patients receiving mechanical thrombectomy.

Keywords: Histology; Prediction; Radiomics; Stroke; Thrombus.