Clot Analog Attenuation in Non-contrast CT Predicts Histology: an Experimental Study Using Machine Learning


Exact histological clot composition remains unknown. The purpose of this study was to identify the best imaging variables to be extrapolated on clot composition and clarify variability in the imaging of thrombi by non-contrast CT. Using a CT-phantom and covering a wide range of histologies, we analyzed 80 clot analogs with respect to X-ray attenuation at 24 and 48 h after production. The mean, maximum, and minimum HU values for the axial and coronal reconstructions were recorded. Each thrombus underwent a corresponding histological analysis, together with a laboratory analysis of water and iron contents. Decision trees, a type of supervised machine learning, were used to select the primary variable altering attenuation and the best parameter for predicting histology. The decision trees selected red blood cells (RBCs) for correlation with all attenuation parameters (p < 0.001). Conversely, maximum attenuation on axial CT offered the greatest accuracy for discriminating up to four groups of clot histology (p < 0.001). Similar RBC-rich thrombi displayed variable imaging associated with different iron (p = 0.023) and white blood cell contents (p = 0.019). Water content varied among the different histologies but did not in itself account for the differences in attenuation. Independent factors determining clot attenuation were the RBCs (β = 0.33, CI = 0.219–0.441, p < 0.001) followed by the iron content (β = 0.005, CI = 0.0002–0.009, p = 0.042). Our findings suggest that it is possible to extract more and valuable information from NCCT that can be extrapolated to provide insights into clot histological and chemical composition.

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Non-contrast CT


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Acute ischemic stroke


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The authors thank the team headed by Dr. Ray McCarthy for providing clot analogs weekly as the source material for our study free of charge. Aglaé Velasco Gonzalez (Neuroradiology) performed this study in collaboration with the Institute of Biostatistics and Clinical Research and Institute of Neuropathology at the Faculty of Medicine, Westfälische Wilhelms-Universität Münster (WWU). We also thank Dr. Senner Volker for coordinating and Mrs. Andrea Rothaus of the neuropathology laboratory for preparing and staining the clot analog samples. Special thanks go to our X-ray technicians who always found a way to complete the experiments on time and whose support was invaluable. Finally, the authors thank the University of Muenster for giving us the time to complete this project.

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Correspondence to Aglae Velasco Gonzalez.

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Conflict of Interest

Aglaé Velasco González performed this study in the context of a program for research backed by the WWU University. One of the authors (Ray McCarthy) is an employee of Cerenovus. Authors who neither advise nor work for the industry had exclusive control over designing and performing the experiment, the data, and data analysis. This study received no industry financial support. All authors have approved the final manuscript.

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Velasco Gonzalez, A., Buerke, B., Görlich, D. et al. Clot Analog Attenuation in Non-contrast CT Predicts Histology: an Experimental Study Using Machine Learning. Transl. Stroke Res. 11, 940–949 (2020).

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  • Blood clot
  • Helical CT
  • Red blood cells
  • Decision trees
  • Iron