RT Journal Article
SR Electronic
T1 Imaging-based prediction of histological clot composition from admission CT imaging
JF Journal of NeuroInterventional Surgery
JO J NeuroIntervent Surg
FD BMJ Publishing Group Ltd.
SP 1053
OP 1057
DO 10.1136/neurintsurg-2020-016774
VO 13
IS 11
A1 Uta Hanning
A1 Peter B Sporns
A1 Marios N Psychogios
A1 Astrid Jeibmann
A1 Jens Minnerup
A1 Mathias Gelderblom
A1 Karolin Schulte
A1 Jawed Nawabi
A1 Gabriel Broocks
A1 Lukas Meyer
A1 Hermann Krähling
A1 Alex Brehm
A1 Moritz Wildgruber
A1 Jens Fiehler
A1 Helge Kniep
YR 2021
UL http://jnis.bmj.com/content/13/11/1053.abstract
AB Background Thrombus composition has been shown to be a major determinant of recanalization success and occurrence of complications in mechanical thrombectomy. The most important parameters of thrombus behavior during interventional procedures are relative fractions of fibrin and red blood cells (RBCs). We hypothesized that quantitative information from admission non-contrast CT (NCCT) and CT angiography (CTA) can be used for machine learning based prediction of thrombus composition.Methods The analysis included 112 patients with occlusion of the carotid-T or middle cerebral artery who underwent thrombectomy. Thrombi samples were histologically analyzed and fractions of fibrin and RBCs were determined. Thrombi were semi-automatically delineated in CTA scans and NCCT scans were registered to the same space. Two regions of interest (ROIs) were defined for each thrombus: small-diameter ROIs capture vessel walls and thrombi, large-diameter ROIs reflect peri-vascular tissue responses. 4844 quantitative image markers were extracted and evaluated for their ability to predict thrombus composition using random forest algorithms in a nested fivefold cross validation.Results Test set receiver operating characteristic area under the curve was 0.83 (95% CI 0.80 to 0.87) for differentiating RBC-rich thrombi and 0.84 (95% CI 0.80 to 0.87) for differentiating fibrin-rich thrombi. At maximum Youden-Index, RBC-rich thrombi were identified at 77% sensitivity and 74% specificity; for fibrin-rich thrombi the classifier reached 81% sensitivity at 73% specificity.Conclusions Machine learning based analysis of admission imaging allows for prediction of clot composition. Perspectively, such an approach could allow selection of clot-specific devices and retrieval procedures for personalized thrombectomy strategies.The data that support the findings of this study are available from the corresponding author upon reasonable request.