17 May 18. Featured Paper

Link to paper as published on RSNA Radiology

Authors

Liang Chen, MSc; Anoma Lalani Carlton Jones, FRCR; Grant Mair, PhD; Rajiv Patel, FRCR; Anastasia Gontsarova, FRCR; Jeban Ganesalingam, PhD;Nikhil Math, BSc; Angela Dawson, MRCP; Basaam Aweid, MRCP; David Cohen, FRCP; Amrish Mehta, FRCR; Joanna Wardlaw, FMedSci; Daniel Rueckert, FREng; Paul Bentley, PhD, For the IST-3 Collaborative Group

Abstract

Purpose

To validate a random forest method for segmenting cerebral white matter lesions (WMLs) on computed tomographic (CT)  images in a multicenter cohort of patients with acute ischemic stroke, by comparison with fluid-attenuated recovery (FLAIR) magnetic resonance (MR) images and expert consensus.

Methods

A retrospective sample of 1082 acute ischemic stroke cases was obtained that was composed of unselected patients who were treated with thrombolysis or who were undergoing contemporaneous MR imaging and CT, and a subset of International Stroke Thrombolysis–3 trial participants. Automated delineations of WML on images were validated relative to experts’ manual tracings on CT images, and co-registered FLAIR MR imaging, and ratings were performed by using two conventional ordinal scales. Analyses included correlations between CT and MR imaging volumes, and agreements between automated and expert ratings.

Results

Automated WML volumes correlated strongly with expert-delineated WML volumes at MR imaging and CT (r² = 0.85 and 0.71 respectively; P < .001). Spatial-similarity of automated maps, relative to WML MR imaging, was not significantly different to that of expert WML tracings on CT images. Individual expert WML volumes at CT correlated well with each other (r² = 0.85), but varied widely (range, 91% of mean estimate; median estimate, 11 mL; range of estimated ranges, 0.2–68 mL). Agreements (κ) between automated ratings and consensus ratings were 0.60 (Wahlund system) and 0.64 (van Swieten system) compared with agreements between individual pairs of experts of 0.51 and 0.67, respectively, for the two rating systems (P < .01 for Wahlund system comparison of agreements). Accuracy was unaffected by established infarction, acute ischemic changes, or atrophy (P > .05). Automated preprocessing failure rate was 4%; rating errors occurred in a further 4%. Total automated processing time averaged 109 seconds (range, 79–140 seconds).

Conclusion

An automated method for quantifying CT cerebral white matter lesions achieves a similar accuracy to experts in unselected and multicenter cohorts.

Keywords

• artificial intelligence
• computed tomography (CT)
• stroke
• dementia