Abstract
Background and Purpose:
Mechanical
thrombectomy (MTB) is a reference treatment for acute ischemic stroke,
with several endovascular strategies currently available. However, no
quantitative methods are available for the selection of the best
endovascular strategy or to predict the difficulty of clot removal. We
aimed to investigate the predictive value of an endovascular strategy
based on radiomic features extracted from the clot on preinterventional,
noncontrast computed tomography to identify patients with first-attempt
recanalization with thromboaspiration and to predict the overall number
of passages needed with an MTB device for successful recanalization.
Methods:
We
performed a study including 2 cohorts of patients admitted to our
hospital: a retrospective training cohort (n=109) and a prospective
validation cohort (n=47). Thrombi were segmented on noncontrast computed
tomography, followed by the automatic computation of 1485
thrombus-related radiomic features. After selection of the relevant
features, 2 machine learning models were developed on the training
cohort to predict (1) first-attempt recanalization with
thrombo aspiration and (2) the overall number of passages with MTB
devices for successful recanalization(But if you didn't get to 100% recovery then recanalization was not successful.). The performance of the models was
evaluated on the prospective validation cohort.
Results:
A
small subset of radiomic features (n=9) was predictive of first-attempt
recanalization with thromboaspiration (receiver operating
characteristic curve–area under the curve, 0.88). The same subset also
predicted the overall number of passages required for successful
recanalization (explained variance, 0.70; mean squared error, 0.76;
Pearson correlation coefficient, 0.73; P<0.05).
Conclusions:
Clot-based
radiomics have the ability to predict an MTB strategy for successful
recanalization in acute ischemic stroke, thus allowing a potentially
better selection of the MTB strategy, as well as patients who are most
likely to benefit from the intervention.
Footnotes
No comments:
Post a Comment