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Clot-Based Radiomics Predict a Mechanical Thrombectomy Strategy for Successful Recanalization in Acute Ischemic

Jeremy Hofmeister1,2, Gianmarco Bernava3, Andrea Rosi3

  • 1Radiology Unit, Department of Diagnostic (J.H., X.M., S.B., P.-A.P., A.P.), Geneva University Hospitals, Switzerland.

Stroke
|July 21, 2020
PubMed
Summary

Radiomics from clot imaging can predict mechanical thrombectomy success in acute ischemic stroke. This approach helps select the best endovascular strategy and identify patients who will benefit most from treatment.

Keywords:
artificial intelligenceoutcomes assessment, health careradiologystroketreatment outcome

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Area of Science:

  • Neurology
  • Radiology
  • Data Science

Background:

  • Mechanical thrombectomy (MTB) is a primary treatment for acute ischemic stroke.
  • Current methods lack quantitative tools to select optimal endovascular strategies or predict clot removal difficulty.

Purpose of the Study:

  • To assess the predictive value of radiomic features from pre-interventional noncontrast computed tomography (CT) clots.
  • To identify patients achieving first-attempt recanalization with thromboaspiration.
  • To predict the number of passages required for successful MTB.

Main Methods:

  • A retrospective training cohort (n=109) and a prospective validation cohort (n=47) were analyzed.
  • 1485 clot radiomic features were extracted from noncontrast CT.
  • Machine learning models were developed to predict first-attempt recanalization and passages needed for MTB.

Main Results:

  • A subset of 9 radiomic features predicted first-attempt recanalization with high accuracy (AUC=0.88).
  • The same features predicted the number of passages needed for successful recanalization (explained variance=0.70).

Conclusions:

  • Clot-based radiomics can predict mechanical thrombectomy strategy success in acute ischemic stroke.
  • This enables better selection of MTB strategies and identification of suitable patients.