Clinical value of automated volumetric quantification of early ischemic tissue changes on non-contrast CT

Deep Learning-Based Extraction of Biomarkers for the Prediction of the Functional Outcome of Ischemic Stroke Patients

Accurately predicting functional outcomes in stroke patients remains challenging yet clinically relevant. While brain CTs provide prognostic information, their practical value for outcome prediction is unclear. We analyzed a multi-center cohort of 743 ischemic stroke patients (<72 h onset), including their admission brain NCCT and CTA scans as well as their clinical data. Our goal was to predict the patients' future functional outcome, measured by the 3-month post-stroke modified Rankin Scale (mRS), dichotomized into good (mRS ≤ 2) and poor (mRS > 2). To this end, we developed deep learning models to predict the outcome from CT data only, and models that incorporate other patient variables. Three deep learning architectures were tested in the image-only prediction, achieving 0.779 ± 0.005 AUC. In addition, we created a model fusing imaging and tabular data by feeding the output of a deep learning model trained to detect occlusions on CT angiograms into our prediction framework, which achieved an AUC of 0.806 ± 0.082. These findings highlight how further refinement of prognostic models incorporating both image biomarkers and clinical data could enable more accurate outcome prediction for ischemic stroke patients.

MR microscopy to assess clot composition following mechanical thrombectomy predicts recanalization and clinical outcome

BACKGROUND

Mechanical thrombectomy (MT) is the standard of care for patients with a stroke and large vessel occlusion. Clot composition is not routinely assessed in clinical practice as no specific diagnostic value is attributed to it, and MT is performed in a standardized 'non-personalized' approach. Whether different clot compositions are associated with intrinsic likelihoods of recanalization success or treatment outcome is unknown.

METHODS

We performed a prospective, non-randomized, single-center study and analyzed the clot composition in 60 consecutive patients with ischemic stroke undergoing MT. Clots were assessed by ex vivo multiparametric MRI at 9.4 T (MR microscopy), cone beam CT, and histopathology. Clot imaging was correlated with preinterventional CT and clinical data.

RESULTS

MR microscopy showed red blood cell (RBC)-rich (21.7%), platelet-rich (white,38.3%) or mixed clots (40.0%) as distinct morphological entities, and MR microscopy had high accuracy of 95.4% to differentiate clots. Clot composition could be further stratified on preinterventional non-contrast head CT by quantification of the hyperdense artery sign. During MT, white clots required more passes to achieve final recanalization and were not amenable to contact aspiration compared with mixed and RBC-rich clots (maneuvers: 4.7 vs 3.1 and 1.2 passes, P<0.05 and P<0.001, respectively), whereas RBC-rich clots showed higher probability of first pass recanalization (76.9%) compared with white clots (17.4%). White clots were associated with poorer clinical outcome at discharge and 90 days after MT.

CONCLUSION

Our study introduces MR microscopy to show that the hyperdense artery sign or MR relaxometry could guide interventional strategy. This could enable a personalized treatment approach to improve outcome of patients undergoing MT.